The benefits of RPA are numerous:

• RPA solutions help out in call centers by offering selling guidance to service agents.
• RPA tools assist in healthcare by completing data entry.
• RPA boosts the finance sector by distributing data across departments without human help.

Many RPA companies are transforming their software to help across industries to meet new customer needs. Below you’ll see a list of 15 vendors who have different solutions with RPA for different company needs:

• Top Robotic Process Automation Companies
  • UiPath: Best Overall
  • Automation Anywhere: Best for Cloud RPA
  • EdgeVerve: Best for Large Enterprises
  • Blue Prism: Best for Integration
  • WorkFusion: Best for Finance Industry
  • Kofax: Best for Large Data Loads
  • NICE: Best for Automated Automation
  • Hyland RPA: Best for Third-Party Bots
  • Pegasystems: Best for DPA
  • AntWorks: Best for Incorporating NLP
  • Nintex RPA: Best for Machine Learning
  • ServiceNow: Best for Customization
  • MuleSoft RPA: Best for Company Coverage
  • AutomationEdge: Best for Variety of RPA Tools
  • Keysight’s Eggplant: Best for Flexibility
• Vendor Comparison Chart
• How to Select an RPA Software Tool

Top Robotic Process Automation Companies

UiPath: Best Overall

UiPath has a solid claim to the title of RPA market leader, with a dedication to RPA rather than just including RPA as a part of its overall portfolio like many other vendors. RPA is well-suited for large enterprises with significant resources to commit to building RPA and associated artificial intelligence (AI) and machine learning (ML) technologies into its business workflow.

UiPath offers an Azure Cloud SaaS tool, an embedded analytics feature, and a mobile app for its Orchestrator tool. Significantly, it offers an AI integration fabric, which allows robust enablement of AI features.

To speed deployment, the software includes an integrated library of pre-built automation components to augment custom automation. In 2019 UiPath acquired ProcessGold and StepShot, which enabled it to build more process mining into its RPA functionality.

Target User

UiPath’s customers range from banking, healthcare, insurance, government, manufacturing, retail, and telecom.

Key RPA Features

Extensive Partner Network: UiPath has built a rich network of alliances with technology partners, which offer supporting software. These related applications run the gamut from AI to business process management (BPM) to complex process mining. This well-developed network should help UiPath stay up with the curve as RPA grows rapidly.

Advanced User Interface: UiPath clearly strives to offer an intuitive user (UI) interface to its bot dashboards. While it generally succeeds at this, UiPath is an advanced platform, which means not all deployment scenarios are low-code or no-code. Getting full use from RPA requires some machine learning expertise.

Newer Cloud Solution: UiPath unveiled its Cloud Enterprise RPA in June 2019, which is later than some of the top RPA vendors. The key advantage of this cloud version is that it serves companies while avoiding the added hardware-software data center expenses, and it adds scalability.

Pros

• Extensive product offering suitable for many verticals.
• Good for Windows-based users.
• Known for responsive customer support.

Cons

• User interface is complex.
• UiPath was late to the cloud.

User Reviews

Honors

UiPath has earned the following recognition:

• Emotional Footprint Leader for RPA in 2022.
• Frontrunner by SoftwareAdvice in 2022.
• Best Company Outlook by Comparably in 2021.

Pricing

For pricing, go to UiPath’s Buy Now page.

Automation Anywhere: Best for Cloud RPA

Focused exclusively on the RPA sector, Automation Anywhere is a market leader, with a high profile in the market that makes it a “first look” for any potential buyers. Automation Anywhere is fully cloud and SaaS-enabled, providing a low enough cost of ownership for small and midsize businesses (SMBs), with a product depth and a developed roadmap that’s also suitable for large enterprise customers.

Automation Anywhere is known for fast implementation. It has pluggable application programming interface (API) integration for developers. The company’s mobile app allows customers to monitor and manage bots on the run. In a step forward, its IQBot includes support for handwriting.

The company’s thin client architecture is well regarded, as is its ability to connect various automations.

Target User

Automation Anywhere’s customers range from business process outsourcing (BPO), finance, healthcare, insurance, life sciences, manufacturing, government, retail, and telecom.

Key RPA Features

Industry-Leading Partner Network: Automation Anywhere has a vast partner network, offering support for RPA tools and solutions of every stripe. It boasts nearly 600 staff in research and development (R&D), and the company has a market presence around the world, allowing it to serve even the largest multinational client.

User Interface: Automation Anywhere’s most routine bot automations are designed to be easy to build. The solution is fully cloud-based and SaaS accessible, so a web-based software robot is assembled with relative simplicity. The company’s flagship Automation Anywhere Enterprise A2019 offers a persona-based platform for developers and nontechnical staff to use for easy collaboration.

Accessible Cost Structure: Automation Anywhere’s scale and cloud-based approach allow it to offer a digital staff at an affordable cost. Additionally, the company’s bot store and its large community offer support and prebuilt elements that can further drive an accessible price point.

Like all RPA vendors, most of Automation Anywhere’s processes are unattended. But, the vendor acquired Klevops in August 2019, which boosted the company’s orchestration for the more complex attended automation.

Pros

• Known for responsive customer support.
• Flexible architecture allows for easy scaling.
• Typically an affordable cost structure.

Cons

• While simple bots are low-code, more complex automations require extensive knowledge and technical staff.
• In some cases, API access could be improved.

User Reviews

Honors

Automation Anywhere has earned the following recognition:

• No. 1 Leader for RPA by G2 in 2022.
• Gold Medalist by SoftwareReviews.
• Customers’ Choice for RPA by Gartner Peer Insights in 2021.

Pricing

The company’s Community edition is free for small businesses, developers, and students—which is excellent as a learning tool. The Enterprise solution also has a free trial available. For more details on pricing, go to Automation Anywhere’s Bot Store.

EdgeVerve: Best for Large Enterprises

EdgeVerve is a subsidiary of India-based IT giant Infosys Technologies. The company’s RPA software is best suited for major enterprise companies, including those with large reliance on consumer customer service. EdgeVerve is a strong player in RPA assistance for call centers.

In addition to its flagship RPA solution, AssistEdge Robotic Process Automation, it also offers a tool set of machine learning and AI tools called Infosys Nia.

Because of InfoSys’s many business relationships, EdgeVerve is expected to grow its customer base aggressively within this existing customer group; customers that don’t have an existing relationship with InfoSys are not as clearly targeted.

Target User

EdgeVerve’s customers are focused in finance.

Key RPA Features

Combining Automation and AI: Built into the EdgeVerge product roadmap is a focus on RPA governance, and a sophisticated approach to automation that uses this governance to manage the union of AI and automation. Part of this strength comes from EdgeVerve’s Infosys Nia division.

Customer Engagement: InfoSys has a strong services component, so it makes sense that the company helps speed deployment and the overall ramping up of RPA projects. This includes support for build and design.

Attended Customer Service and Call Center: With all of its offerings available as on-premises or cloud-based, EdgeVerve has a particular strength in the call center. Its AssistEdge Engage solution offers support for customer service agents, which reflects the company’s strength in unattended RPA. This is a differentiator in an overall market focused on attendance.

Pros

• Strong graphical user interface (GUI) leads to ease of use.
• Highly scalable solutions, with business intelligence dashboards.
• Enterprise-grade security that is GDPR ready.
• The company’s AssistEdgeDiscover offers a big boost to process discovery.

Cons

• While the company’s Nia division specializes in ML and AI, it is not completely clear how well these technologies are integrated between Nia and EdgeVerve.
• Some users want better reporting on robot performance.

User Reviews

Honors

EdgeVerve has been awarded:

• BIG Innovation Award in 2021.
• Gold Award in Enterprise (Large) Innovation Category by Golden Bridge Awards in 2021.
• Disrupter Company by the GLOBEE Awards in the Automation and Productivity category.

Pricing

Those interested should Request a Demo with EdgeVerve.

Blue Prism: Best for Integration

Definitely a market leader in RPA, Blue Prism has an elaborate product roadmap and a true commitment to using AI to advance its automation. While it does offer a free version as a trial, Blue Prism is squarely targeted at large enterprise companies with deep resources.

Blue Prism is the most established RPA vendor in the market, having been founded in 2001 by a team of software automation experts; it practically coined the term “RPA.” It has built an extensive array of partners, including consulting partners, that have built a large library of complimentary automation, analytics, and decision management applications. The company’s length of market tenure results in a secure, stable automation product. It is particularly known in financial services.

The vendor offers on-premises or SaaS cloud deployment; it integrates with Google machine learning workflow, which is arguably best in class. Top-tier security and audit trails include non-repudiation features.  Blue Prism is well known for its supporting documentation; this is key in the complex RPA market.

Target User

Blue Prism’s customers range from financial services, telecom, insurance, transportation, healthcare, government, manufacturing, retail, energy, and hospitality.

Key RPA Features

Strong Vertical Focus: Understanding that the sectors ranging from healthcare to manufacturing to retail have different RPA needs, Blue Prism has launched scores of industry solutions, with active customers in each.

Graphical User Interface: To allow less technical staff to create automations, Blue Prism includes drag-and-drop interface for building process automations.

Commitment to AI: Blue Prism Labs is an AI laboratory focusing on computer vision and document interface, primarily for unattended use cases. The company’s roadmap suggests it will use this AI depth to support more attended, human-involved use cases in the future. Blue Prism acquired startup Thoughtonomy, which built a cloud-based AI engine, in July 2019 to further its AI functionality.

Pros

• Blue Prism is strong in unattended use cases, which allow humans in the loop for augmented staff productivity.
• The vendor’s large array of partnerships includes many consultants; this could help with launch and implementation.
• The company’s long tenure and focus on security and encryption provide it with the stability needed for large enterprise deployments.

Cons

• Some users say reporting and scheduling could be more efficient in enterprise-wide deployments.
• While many RPA vendors’ bots record human staff and mimic them, Blue Prism doesn’t use this function to build automation.
• The company’s design studio generally requires a more technical user.

User Reviews

Honors

Blue Prism was named:

• Best Robotic Process Automation Company in 2020 by Artificial Intelligence Breakthrough Awards Program.
• Finalist in the 2021 Microsoft AI Partner of the Year.

Pricing

For pricing, go to Blue Prism’s Free Trial page.

WorkFusion: Best for Finance Industry

While some RPA solutions aim for simplicity and ease of use, WorkFusion strives to offer deep and robust AI and ML functionality into its RPA tools. It is this quality of fully leveraging AI and ML that will propel RPA into the future. The WorkFusion platform contains hundreds of advanced prepackaged use case solutions that drive performance in predictions, category classification, and data mining.

WorkFusion’s key focus is unattended RPA bots. For companies looking for complex, versatile, high-function unattended solutions that can traverse a large enterprise, WorkFusion is a top choice. The company has a strong presence in the financial industry.

Target User

WorkFusion’s customers are mostly in financial services.

Key RPA Features

Integrated BPM: WorkFusion offers an integrated business process management system to manage robotic automations. This well-developed system enables versatile ML tools and helps interoperability.

Developed Analytics: To better understand the performance of your automations, WorkFusion’s analytic tools offer the ability to target single automations, which help to fix inefficient processes before they go on too long.

Open-Source Databases: The company has included a number of open-source databases to boost data scalability and upgrade its data management capabilities. RPA requires plenty of data to fuel automations.

Pros

• Known for solid customer support.
• Top-rated development process to drive future product road map.
• Sophisticated feature set for analytics.

Cons

• Advanced platform that requires expert staff to fully implement.
• Not focused on attending automations.

User Reviews

Honors

WorkFusion was named a Leader in Everest Group’s PEAK Matrix Assessment for Intelligent Document Processing (IDP) 2022.

Pricing

For pricing, go to WorkFusion’s Request demo page on its website.

Kofax: Best for Large Data Loads

Kofax is well-suited to companies that typically gather large quantities of unstructured data from myriad data pools. This includes sources like social media and mixed-data customer interactions.

While many RPA vendors require customers to run automations on all client desktops and terminals, Kofax has shifted this system. Its RoboServers tool shows the system’s interface in a single, easily managed container. This ingenious method avoids having to run the solution on every desktop, which lowers infrastructure costs.

Potentially further reducing costs, the Kofax platform has such a strong offering in optical character recognition (OCR) tools that the need to purchase an additional OCR solution from a third party is typically avoided. The company is strong in the logistic and transportation industries.

Target User

Kofax’s customers include finance and accounting, customer engagement, and operations.

Key RPA Features

Easy Data Transport: An RPA tool uses automated bots to shift data from one web portal to another, saving staff time. Data transport is one of the most common RPA use cases.

Lower Need for VDI: Because the Kofax RPA manages the UI from a central location, the system needs fewer virtual desktop infrastructures, which cuts costs and allows for easier scaling.

REST/SOAP Interface: Kofax’s representational state transfer and simple object access protocol (REST/SOAP) interface allows easy embedding of automations into third-party applications. This is particularly helpful as companies use third-party apps to enable scalability.

Pros

• Strong in data extraction from documents.
• Can use APIs to mine and migrate data from any number of sources.
• Relatively easy reuse and redeployment of existing automations, which enables faster deployments in new situations.

Cons

• Some concerns with customer support.
• Could use more focus on debugging applications.

User Reviews

Honors

Kofax received three 2022 Best Software Awards from TrustRadius including:

• Overall Best Software.
• Best Software for Mid-size business.
• Best Software for Small Businesses.

Pricing

For pricing, go to Kofax’s Request a Demo page.

NICE: Best for Automated Automations

NICE is well-suited for a growing niche: large enterprises that need automated automations. That is, automations with humans in the loop—the so-called “augmented” human staff member—are particularly useful for large enterprises with a call center to maximize. For large enterprise clients, another plus for NICE is its long tenure as a company—almost two decades as an automation vendor.

Additionally, NICE has a solid record with unattended workloads, which remains the more common customer need in the RPA market. NICE is focused on analytics (yet another good tool for call centers), which allows close monitoring of how well a given set of bots and automations is achieving a return on investment (ROI).

Target User

NICE works with enterprises, small businesses, healthcare, financial services, BPO, education, retail, telecom, nonprofit, and collections.

Key RPA Features

Specialized Attended: The company’s Advanced Process Automation offers vertically-oriented attended automations to serve various industries, including manufacturing, banking, and telecommunications.

Real-Time Call Center: The NICE platform allows managers to track each sales rep in the call center, using a bot to offer suggestions, assistance, and support as that rep handles sales calls.

Automation Coupled With Analytics: The NICE Employee Virtual Attendant integration and Shape Analysis (NEVA) tool uses an analytics grid to check for performance over time, both for unattended and attended automations. This kind of RPA governance tool is useful for improving ROI.

Pros

• Known for good customer support.
• RPA solutions offered via virtually any format, from on-premises to SaaS, private or public cloud.

Cons

• AutomationDesigned, a core platform component, requires developer skills to fully maximize.
• Since it’s designed for large enterprises, the initial set up can require the expert staff that these larger companies tend to have on-premises.

User Reviews

Honors

NICE has been awarded for:

• Customer Experience excellence in the 15th Annual Ventana Research Digital Innovation Awards.
• CRM (customer relationship management) Industry Leader Award in 2020.

Pricing

For pricing, go to NICE’s Contact Us page.

Hyland RPA: Best for Third-Party Bots

Hyland RPA, previously Another Monday, will appear attractive to SMB and enterprise clients that are newer to the RPA market because the company offers a usage-based cost structure. Clients pay for automation instances that are successful, a key selling point (clients pay a micropayment per successful transaction). Given that a percentage of the RPA market struggles for true ROI, this offer should continue to help Hyland RPA attract companies of various sizes and verticals.

The company supports its platform as a managed service, which means Hyland RPA can play a hands-on role with its clients. For larger clients, like banks and telecoms, Hyland RPA will directly support the platform. This approach isn’t best for every client; many companies prefer to buy an RPA solution via the cloud or SaaS and manage it only with in-house staff.

That said, Hyland RPA works for those clients that seek a comprehensive solution, as opposed to an “experiment and grow” approach that starts small and scales. The company is strong in manufacturing and utilities.

Target User

Hyland RPA specializes in industries such as healthcare, financial services, insurance, higher education, and government.

Key RPA Features

Includes Third-Party Bots: Hyland RPA’s system is built to oversee and manage bots from third-party sources, which is an approach to RPA that enables great scalability.

Encryption: To bulk up security, Hyland RPA has built encryption into its platform. This is a key feature, given that bots transfer data across an entire organization.

Diverse Prebuilt Elements: The company offers a cohesive menu of automated bots, allowing an end-to-end solution that handles an entire company’s RPA needs with less development time.

Pros

• Platform offers strong bot governance features.
• Set up for the likely future environment of RPA, where a vendor offers an all-encompassing integrated services approach.
• Built with a decentralized architecture that needs no single application to launch and operate bots.

Cons

• Relies on services support from Hyland RPA and its partners; some users may not prefer this.
• A robust customer-created bot strategy in addition to its “all enterprise-wide” strategy would be a plus.

User Reviews

Honors

Hyland RPA was listed as No. 30 in Fortune Best Workplaces in Technology in 2021.

Pricing

For pricing, go to Hyland RPA’s Contact Us page.

Pegasystems: Best for DPA

Companies looking for an RPA solution with extensive features may select Pegasystems. An overwhelming majority of the company’s clients deploy these human-in-the-loop robot automations, which serve everything from call centers to diverse sales forces.

That said, Pegasystems’ real focus is digital process automation (DPA), sometimes referred to as business process management. In fact, the company’s RPA is available as a no-cost upgrade for buyers that purchase a DPA solution.

Although experts disagree, DPA is a more all-encompassing system that assists everything from business rules to document support, along with robotic process automation. This all-encompassing focus will appeal to some businesses, especially large enterprises, which have the need for such a robust platform. SMBs are likely less of a fit.

An additional plus: Pegasystems has a large market presence, fully global, which suits a multinational client.

Target User

Pegasystems works with financial services, insurance, healthcare, communications, government, manufacturing, transportation, energy, retail, media, and travel.

Key RPA Features

Easy to Deploy: Clients can get operational with relative ease, using Pegasystems’ lightweight architecture that supports fast stand-alone robotic automations.

Visual Studio Scripting: Using a system that watches and records human staffers’ use of applications on the desktop, the Pegasystems platform will then send resulting operational data to Pega’s machine learning platform on AWS.

Multi-Use Robots: In an efficient methodology, Pegasystems’ concurrent scheduling system enables unattended automated bots to assist in a variety of work situations, saving a customer programming time and virtual machines.

Pros

• Growing commitment to unattended automations.
• Advanced in natural language processing (NLP) and mining email and chat interactions with user-programmed automations.
• Strong presence in CRM, so a company sales staff will be assisted by its automations.

Cons

• Customer support for RPA could be improved.
• Bots can be programmed by a business analyst, but that individual will need some scripting skills; not strictly low-code.

User Reviews

Honors

Pegasystems has won:

• Champion 2022 Low-Code Business Process Management Emotional Footprint Award.
• Two platinum and three Gold Muse Awards.

Pricing

For pricing, go to Pegasystems’ Platform Trial page.

AntWorks: Best for Incorporating NLP

Based in Singapore, AntWorks is a global AI and intelligent automation company that uses RPA to manage, measure, and maximize a company’s production of digital workloads in real time. Working with BOTs, AntWorks is able to mitigate risk with continuity planning and failover management and is built for self-recovery.

The AntWorks RPA enterprise bot platform offers an array of automation modules, enabling the time-efficient list of reusable services. This is a popular strategy in the RPA sector and one that AntWorks takes up a step by incorporating NLP, extensive data capture, and an ML engine in its solution.

To its credit, the AntWorks approach stresses a holistic approach to automation that incorporates several elements of automation and management rather than isolated tools.

Target User

AntWorks RPA works with enterprise-scale automation across many industries, including banking, financial services, insurance, and manufacturing, to help a company build a digital workforce customized to a company’s needs.

Key RPA Features

Low-Code and No-Code: While many companies’ RPAs require some form of coding, AntWorks requires little to no coding for its functionality.

High BOT Productivity: AntWorks offers high productivity because of its availability and message-based BOT triggers largely limiting downtime.

Cognitive BOTs: AntWorks RPA is able to respond based on changes of environment ensuring the company’s success.

Pros

• Easy-to-use system.
• Helpful training and support.
• Integrates well with other company services.

Cons

• Slow service for companies.
• Billing portion can be confusing.
• Cannot group reports together.

User Reviews

Honors

• HFS recognized AntWorks as a Leader in Innovation & Embedded Intelligence for its RPA.
• Adobe presented AntWorks with the award for the best use of Technology in Learning.
• NelsonHall called AntWorks’ technology “cutting-edge” and among the most “intriguing competitors” in cognitive automation.

Pricing

For pricing, contact sales or select the Request Demo link at the top of AntWorks’ website.

Nintex RPA: Best for Machine Learning

Nintex RPA, once Israel-based Kryon, has RPA tool sets for both attended and unattended automations and is focused on process discovery. This commitment to process discovery will likely serve its customers well; discovering when and where in the workflow to fill an inefficiency with a bot is critically important.

Nintex RPA automates processes with its RPA to help businesses improve their performance and reduce operational costs and downtime. Nintex RPA is a market-leading tool that helps uncover answers to a company’s needs by identifying, mapping, and recommending the tasks suited for its automation systems.

Nintex RPA is growing. With patents for machine learning and other core RPA tools, Nintex is a vendor to watch.

Target User

Nintex RPA works with industries such as banking, healthcare, energy, government and education, manufacturing, technology, and the food industry.

Key RPA Features

Desktop Personal Assistant: Unlike most RPAs, Nintex RPA offers an assistant to help employees understand their systems. The assistant can be guidance for any tasks requiring human intervention.

24/7 Virtual Workers: A company can choose for a bot to complete tasks in less time and allows employees to focus on more valuable tasks. The bots can run all of the applications a company needs and give the company real-time information.

Automated Fragmented Processes: Nintex RPA streamlines vital processes for its customers. A task that previously involved many people can be done by bots automatically.

Pros

• Easy installation.
• Allows customer customization.
• Saves company time.

Cons

• Unable to upload spreadsheets as a list.
• Doesn’t work well for remote desktops.
• Constant updates.

User Reviews

Honors

Nintex has received the following recognition:

• Leader in 11 of 12 Zinnov Zones for Hyper Intelligent Automation—H1 2022.
• Won the 2022 TrustRadius Tech Cares Award.

Pricing

For pricing, go to Nintex’s pricing page.

ServiceNow: Best for Customization

Previously Intellibot.IO, ServiceNow’s RPA offers the full range of automation design, from chat tool and ML architecture to both attended and unattended bots. In an interesting twist, the UI offers a view of the software robots connected to one another, which offers designers a global view of the automated work process.

Now Platform offers UI-based interactions and a reduction manual, reduces repetitive system actions, and accelerates automation for its customers. There are management hubs, desktop design studios, integration with Flow Designer, and the ability to choose from over 1,300 tools such as connectors, templates, and optical character recognition.

Target User

ServiceNow customers’ industries are education, energy, financial services, government, healthcare, logistics, manufacturing, retail, service providers, and telecommunications.

Key RPA Features

Native Integration: ​​A company using ServiceNow’s RPA will notice their applications can easily integrate with the RPA, making the data between the applications readily available for customers.

Unique Components: Unlike many RPAs, ServiceNow’s RPA offers over 1,300 tools for its customers, giving them the ability to use specific components for their business.

Customization Factors: ServiceNow’s RPA offers three large customization options including Flow Designer, a management hub, and a desktop design studio.

Pros

• Helpful common service data model.
• Lots of modules for IT service management.
• Easy to work with.

Cons

• No way to go into change requests.
• Slow user interface.
• No mobile version.

User Reviews

Honors

Gartner has acknowledged ServiceNow as a leader in the 2023 Gartner Quadrant for Enterprise Low-Code Application Platforms for the third consecutive year.

Pricing

ServiceNow’s RPA is known for significant capabilities at reasonable cost. For details on pricing, call the sales department, or select the Demo page for more information.

MuleSoft RPA: Best for Company Coverage

Mulesoft RPA, previously ServiceTrace, offers RPA within a larger digital process automation approach. With the goal of efficiency, the company’s automation process uses an assessment form that staffers use to describe the value and potential benefit of an automation. Additionally, oversight of both human staffers and automated bots is supported by metadata from a business process model.

Mulesoft RPA has many subsections to help companies. These include the MuleSoft RPA Manager, MuleSoft RPA Recorder, MuleSoft RPA Builder, and MuleSoft RPA Bots to ensure coverage and help with any area a company needs RPA management. These subsections benefit a company further by eliminating manual tasks.

MuleSoft RPA is helpful to many companies needing additional help within their business practices.

Target User

While MuleSoft RPA covers many industries, it’s known for its help in financial services, healthcare services, and manufacturing.

Key RPA Features

Multiple Subsections: MuleSoft RPA has multiple parts to give companies the best result. These are the MuleSoft RPA Manager, MuleSoft RPA Recorder, MuleSoft RPA Builder, and MuleSoft RPA Bots.

Measurable ROI: MuleSoft RPA can automate routine workflows anywhere across a customer’s business for large and small processes.

Scalability: As more data is input into businesses, MuleSoft RPA helps to integrate and push digital capabilities for its customers.

Pros

• Helpful monitoring capabilities.
• Ability to define multi-tiered APIs.
• Flexible systems.

Cons

• Expensive tool.
• Needs more connectors.
• Need improvement with processing.

User Reviews

Honors

MuleSoft was acknowledged as a Visionary in 2022 Gartner Magic Quadrant for RPA.

Pricing

For pricing, go to MuleSoft Automation Pricing.

AutomationEdge: Best for Variety of RPA Tools

Offering both on-premises and cloud-based RPA, AutomationEdge has a robust partner list (including large vendor BMC), which should aid product offering and growth over time. To ease deployment, the company offers hundreds of prebuilt automations.

AutomationEdge RPA with its AI capability can help analyze a company’s data and gives a company more insights into its systems. AutomationEdge RPA benefits a company with cost reduction and efficiency with its ability to personalize and offer self-service.

AutomationEdge RPA offers many specific needs for its customers and larger companies as they grow.

Target User

AutomationEdge RPA helps in the finance, insurance, retail, telecommunication, BPO, logistics, manufacturing, education, and healthcare industries.

Key RPA Features

Conversational RPA: AutomationEdge Conversational RPA allows employees and companies to access their HR, accounts, operations, support, and other issues in one window.

RPA-as-a-Service: AutomationEdge’s RPA as a service was launched to help people not only pay as they go but help companies determine what they want before buying a new product.

SAP Automation: With SAP Automation using AutomationEdge, there is more visibility, control, increased efficiency, enhanced reliability, and reduced costs. AutomationEdge SAP automation customers have noticed little to no errors in their back end.

Pros

• Completes projects on time.
• Flexible and easy to use.
• Takes little to no coding experience.

Cons

• Little available support materials.
• Only performs one similar function at a time.
• Analysis tool needed.

User Reviews

Honors

AutomationEdge was recognized in Zinnov’s IT Automation Zone for 2021.

Pricing

AutomationEdge offers a number of pricing levels to allow budget-strapped businesses to get on board with RPA. For more pricing details, go to AutomationEdge’s pricing page.

Keysight’s Eggplant: Best for Flexibility

Keysight Technologies, based in Santa Rosa, California, is a technology company aiming to innovate new technologies. Eggplant’s RPA is data-driven, so a company can set up automatically completing tasks based on what a company wants for its records. Eggplant’s RPA is an AI-powered automation tool that comes with a deployable runtime that is flexible with a company’s platform.

Keysight’s Eggplant brings the power of RPA and AI together for a company’s automation process. Keysight’s Eggplant flexibility ensures a company can complete a task how it would like.

Target User

Keysight’s Eggplant focuses on industries such as aerospace and defense, automotive, enterprise IT, financial services, healthcare, retail, and telecom.

Key RPA Features

Works on Any Platform: Unlike most RPAs, Eggplant offers its services across applications, websites on any browser, remote devices, and operating systems.

Universal Fusion Engine: Using Eggplant’s AI-powered engine, companies can be confident in their system being identified, executed, and automatic tests.

API Evaluation: Eggplant ensures the company’s website’s operations match the front end and back end.

Pros

• Same setup for desktop and mobile applications.
• Easy to use with less programming skills.
• Verifies and tests for performance optimization.

Cons

• Requires two machines.
• Slow loading speed.
• High cost can be prohibitive.

User Reviews

Honors

Keysight’s Eggplant has been named a Leader in The Forrester Wave: Continuous Functional Test Automation Suites, Q2 2020.

Pricing

For pricing, go to Keysight’s Try Eggplant page.

To learn more: Best Practices for RPA Deployments

Vendor Comparison Chart

How To Select An RPA Software Tool

The market for RPA software is based on the emerging technology of user-programmed automation software. This is a new field with constant and rapid advances. The churning field of vendors is well-armed with complex marketing jargon about the wonders that RPA can achieve.

To help cut though the hype, examine these four factors when selecting a solution.

1. Depth of Product Roadmap

The challenge of RPA is that, while these bots are well-suited for simple, routine tasks, moving beyond the “easy wins” requires an RPA solution with versatility and depth. Does the vendor have a product roadmap that looks likely to handle all the growth in your business?

Does the solution include process mining, an RPA tool that points out gaps and inefficiencies in the workflow that automation can address? Will the RPA solution integrate with your legacy applications?

2. User Interface

If a given RPA solution is advanced and has deep use of artificial intelligence, the dashboard UI used to program it is typically complex. It often requires software developers to program the RPA bots.

If, however, you want a simpler user interface, you can find it. The RPA industry speaks of “robot design for citizens,” meaning nontechnical staffers can set up bots. But with this level of simple UI, you’re often sacrificing some level of advanced functionality in bot capability.

3. Attended or Unattended

Once programmed, an unattended RPA robot does its work without human assistance. Many of the RPA bots in use are unattended; set them up to do a simplistic job, and let them keep repeating.

An attended bot is also known as a “human-in-the-loop” bot. These unattended bots work in close partnership with staffers; for instance, offering real-time suggestions to a call center agent to close the sale.

4. Level of AI and ML in the RPA Platform

The confusion in the RPA market is particularly deep around the issue of artificial intelligence and machine learning. A simple RPA automation does not need AI to perform a series of routine repetitions. And yet the long-term promise of RPA is that these basic bots will use AI and ML to develop into “super performers,” accomplishing a dazzling array of complex tasks.

Consequently, RPA vendors are always touting their products’ depth of AI and ML usage. The degree to which any RPA solution incorporates AI and ML “under the hood” is often hard to fully know, and it will change with time. It may be built in but be limited.

Therefore, buyers need to ask probing questions about each vendors’ AI capability. Is it reality or marketing?

Bottom Line

While still an emerging technology, robotic process automation interoperates closely with artificial intelligence, machine learning, and intelligent automation of all types. As these related technologies develop, RPA will offer correspondingly more benefit.

From UiPath to Keysight’s Eggplant, a company should be able to find what works for it in its industry.

To learn more about RPA: Top RPA Certifications

Why Use Data Analysis Software?

Data analysis tools enable businesses to analyze vast stores of data for great competitive advantage. Data analysis software can mine data that tracks a diverse array of business activity – from current sales to historic inventory – and process it based on data scientists’ queries.

Many related technologies allow analytics software to create its results. Chiefly, these include data warehouse tools, ETL tools, and – now more often – cloud computing infrastructure. These tools allow data insights ranging from predictive analytics, business intelligence (a term often used interchangeably with data analytics), and structured and unstructured data.

We will go over the following top tools used for data analytics:

1. 1. Tableau
   2. Microsoft Power BI
   3. Qlik
   4. ThoughtSpot
   5. MicroStrategy
   6. Sisense
   7. TIBCO
   8. SAS
   9. IBM
   10. SAP

Does Data Analysis Software Use Machine Learning?

As big data analytics tools evolve, they make increasing use of artificial intelligence and machine learning. This AI and ML enables “augmented analytics,” meaning query results have greater depth and detail due to the advanced technologies.

Leading Business Intelligence Solutions

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TABLE OF CONTENTS

• How to Select the Best Data Analysis Tools?
• Top Big Data Analytics Tools & Software
• Comparison Chart of Big Data Analytics Tools
• Additional Data Analysis Software Market Leaders

The results of a data query are displayed in data analysis software using an elaborate visual dashboard, typically with a series of color-coded charts and graphs that illustrate business trend lines. These dashboards can be customized based on the input of staff. They can also be tweaked over time to produce a more specific representation.

These real-time visualizations of data are now a critical navigation device for most businesses. Many factors are driving the growth of the data analysis software market, but in sum: there is hardly a business today that can effectively compete without the insight from data analytics software tools.

How to Select the Best Data Analysis Tools?

Anyone who understands the data analytics software market will tell you that selecting a data analytics platform is complicated. It’s complex because the software is complex – and it’s only getting more complex over time. And it’s complicated by how it must fit into your business; any number of nuanced variables must be weighed for a data analytics tool to fit well with your organization. Review these seven tips and considerations when choosing data analysis software.

1. Data Analytics Methods and Types

When trying to find the perfect tool for your data analytics needs, it’s important that you first understand all of the different methods and types of data analysis that these tools might offer. While some tools will offer a portfolio of some or all of these analytic types, others specialize and dive deep into a specific category of data analytics needs. Some keywords that you should look for in your search for the right data analytics tool are covered below, so do your research to find out which approach best fits your data types and business strategy needs.

• Data Visualization and Modeling
• Data Preparation
• Business Intelligence
• Marketing Analytics
• ETL
• Statistical Analysis
• Predictive Analytics
• SQL
• Industry-Specific

2. Augmented Analytics

Using AI and ML to offer so-called augmented analytics is the biggest buzz in the industry, and most vendors claim to offer some flavor of augmented analytics. However, really understanding what’s under the hood in terms of AI and ML is difficult for a buyer. Make sure you probe the sales rep on this issue, get clear explanations, and don’t hesitate to ask for AI and ML use cases.

3. Types Of Users

How much do your staffers who will be using the app know about data science? Are they true data scientists or sales reps on the run? Some of the data analytics software tools you’ll see below work for either, while some are geared for one or the other.

4. Interoperability with Existing Data System

It’s exceptionally counterproductive to select a data analytics solution that doesn’t interoperate with your data warehouse and your ETL tools. Furthermore, does it work with your database and storage infrastructure? This may require a trial run.

5. Scalability

Data analytics solutions are hard – very hard – to replace. Because this is true, it’s critically important to select a solution that will grow with you over time. That is, will it handle an increasing number of queries based on large data sets? And also: can this vendor be expected to offer an array of next-gen features in the years ahead?

6. Cohort Of Experts

Some solutions have full communities of users built around them, so hiring an expert is a relatively easy (and inexpensive) task. Other solutions are also quite advanced, but you’ll not find a big pool of experienced pros for hire. Given how expensive data scientists are, you don’t want to make your recruitment any harder than it has to be.

7. New Vendor or Legacy

Some of the vendors in the data analytics sector have been household names for decades. Yet given how lucrative the sector is, new entrants have launched in the last several years. These new vendors may very well be a viable choice, even if their solution doesn’t have the track record. Are they willing to work with you on training and price? Perhaps more so than legacy vendors. Then again, would you sleep better at night with a legacy vendor?

Top Big Data Analytics Tools & Software

Tableau

KEY INSIGHT: Even among market leaders, Tableau is a top vendor in the data analytics software tools market. The company was acquired by Salesforce in 2019.

Tableau has built a large and enthusiastic user base due to the depth and quality of its data visualizations. The company’s data analytics platform is known for collecting multiple data inputs, allowing users to combine them, then offering a dashboard display that enhances visual data mining.

Furthermore, data can then be arranged and rearranged to create hierarchical and bin structures with relative ease. All of this advanced data manipulation can be accomplished by staff without an extensive background in data science, yet the Tableau platform is robust enough to reward a user with data science education.

PROS:

• Tableau has been a data analytics market leader due to its data visualizations. With its acquisition by Salesforce, it’s expected that enhanced capabilities in AI and ML will continue to grow rapidly.
• A good fit for companies of almost all sizes, from large enterprises to SMBs.
• The Tableau Online solution offers a wide array of deployment options for a multi-cloud environment.

CONS:

• Some users would like to see expanded admin and governance functionality.

Microsoft Power BI

KEY INSIGHT: Driven by its Azure Cloud, Microsoft is the leader in hybrid cloud. The company’s Power BI platform benefits from this strength.

In classic Microsoft fashion, the company’s related software products help promote its Power BI analytics tool. For instance, reminders in Excel and Office 365 urge users to adopt it. Consequently, between this built-in advertising and the software giant’s already-sprawling user base, Power BI can justifiably be called the most popular analytics program on the market. This is important because a large user base prompts constant product upgrades, which Power BI certainly benefits from.

Most important: with its deep pockets, Microsoft has built an impressive array of AL and ML functionality, powering the augmented analytics that has become the key differentiator in the data analytics sector. For example, image analytics – clearly a step ahead – are driven by Power BI’s AI feature set.

Significantly, these ML and AI features are driven by the Azure functions built into the Azure Cloud, which are industry-leading.

PROS:

• Top AI and ML tools offer augmented data analytics
• Very well respected among its large user base
• No company has a more extensive software product portfolio than Microsoft, and Power BI benefits from interoperability with this exhaustive toolset

CONS:

• The on-premise-only version of Power BI does not offer the depth of functionality offered by the cloud version
• Users must run the product in the Microsoft Azure cloud, as opposed to the other competing clouds that many companies also use

Qlik

KEY INSIGHT: If your organization seeks to use ML and AI to enhance the quality of data mining, the Qlik Sense is a top choice.

With two decades under its belt, Qlik’s combination of strengths offers a compelling vision in the data analytics sector. Chief among them: the company has advanced versions of artificial intelligence and machine learning built into its Qlik Sense platform. And it offers this functionality without requiring deep data science skills, so sales reps and mid-level staffers can leverage AI for data mining.

Also important: Qlik Sense is cloud-agnostic, so companies can deploy the data analytics tools to any cloud in their multi-cloud infrastructure. They can also deploy on-prem, and then hook the application into the cloud for a hybrid data analytics approach.

PROS:

• The company’s associated insights feature promises to deploy a cognitive application to dig for insights that users might miss.
• Very flexible and strong across public, private, and hybrid clouds.
• Enables upper-level self-service analytics for data scientists, or for users with minimal data science training.

CONS:

• While its product offering is strong, its overall vendor profile is not as high as industry giants like Microsoft or even Tableau.

ThoughtSpot

KEY INSIGHT: While not as well known as some other data analytics software vendors, ThoughtSpot offers a next-generation “search first” tool that earns it a berth as a leader in the market.

ThoughtSpot offers any number of compelling features, particularly an AI-based recommendation system that leverages crowdsourcing. Additionally, sources for its query options range from a legacy provider like Microsoft to a “new kid on the block” like Snowflake.

But most attractive of all, ThoughtSpot’s calling card in a crowded market is its search-based query interface. Users can input a complex analytics query – by typing or speaking – and the ThoughtSpot platform uses augmented analytics to offer insight. Impressively, it can handle large data queries, with many users sifting through more than a terabyte of information. All of this is accomplished – from comparative analysis to anomaly detection – with no software code required. So business staff can data mine without the help of experts.

PROS:

• The search interface allows easy queries of complex questions, analyzing billions of data rows with artificial intelligence.
• Founded in 2012 as a growing company, the company has ridden the wave of enterprise analytics to a solid niche in the analytics sector.
• Well regarded for its ability to scale and handle ever-larger query loads.

CONS:

• Without the large product portfolio of some vendors, users will need to bring their own related tools, like data preparation applications.

MicroStrategy

KEY INSIGHT: In a bold move, MicroStrategy envisions itself as the foundation of enterprise analytics, by connecting various competing platforms into a unified system.

In a highly competitive data analytics market, where each vendor is trying to top the others, MicroStrategy seeks to join them together. Its platform includes API connectors that join competing platforms while – of course – using MicroStrategy as the unifying layer. In a related technique, the company connects all business content from browser-based systems, like CRM and ERP (and competing analytics software), and then offers it as an easy-to-consume analytics dashboard.

As soon as a user moves their mouse over a link, the data appears – offering updated, real-time data insights through the workday.

Additionally, users who can code can leverage MicroStrategy to quickly insert or update a diverse array of data sources from mobile or across the Internet. This easy update from multiple sources plays into MicroStrategy’s “connector” strategy and is well regarded in the data analytics sector.

PROS:

• MicroStrategy’s Hyperintelligence linking technology is an innovative twist that may launch it into a leading position in the years ahead.
• Well respected for the stability of its platform, with little or no problem with bugs or downtime.

CONS:

• Does not have a high profile in the data analytics market.

Sisense

KEY INSIGHT: A sophisticated, forward-looking platform that is well-suited to complex, ongoing data processing – Sisense is great for the power user, but not so much the untrained staffer.

It’s clear that Sisense is committed to a forward-looking data analytics platform. The company reimagined and then largely rebuilt its platform to leverage the advantages of a cloud-native infrastructure.

Among these advantages is great scalability. Sisense drives cloud-native applications at scale in tandem with container technology. As your data needs grow, the platform will surely keep up in the years ahead as cloud platforms get faster and more flexible.

To drive still greater speed and performance, Sisense’s ElastiCube uses its own caching engine, which deploys in-chip and in-memory data crunching. Elasticube boosts the platform’s augmented data prep features. Additionally, Sisense acquired Periscope Data to increase its upper-level data processing features.

PROS:

• Strong support for cloud-native applications.
• Proprietary caching engine enables faster speeds.
• Able to handle a wide array of difficult enterprise analytics workloads.

CONS:

• Geared for advanced users, especially data scientists, instead of off-the-cuff business queries.

TIBCO

KEY INSIGHT: TIBCO is a solid platform with ML-augmented data analytics that work for either enterprise data scientists or for lesser-trained staff.

In a world where data is rarely at rest, gaining real insight from streaming analytics offers a major competitive advantage. This is one of TIBCO’s strengths. The company’s streaming analytics tools offer data mining on the run, with trend knowledge gained from the torrents of data streamed from IoT or other mobile devices.

Additionally, TIBCO Spotfire has advanced augmented analytics, driven by machine learning and featuring a natural language user interface. This ML capability has become one of the key “must-haves” in the data analytics sector.

To fill out its portfolio, Spotfire has data prep tools and data visualizations that users can poke and prod for further insight. All of this adds up to a stable, robust data analytics platform that works for the enterprise or for so-called citizen data scientists who might not have as much training.

PROS:

• Well regarded for its intuitive user interface.
• Well-developed, feature-rich data analytics software platform.
• Includes a large menu of drag and drop analytic functions to speed up data mining.

CONS:

• There are not as many experienced users of TIBCO, given that the vendor has a lower profile than some analytics leaders.

SAS

Key insight: A complete, well-developed data analytics portfolio that can support all of a large enterprise’s data mining process.

With decades in the software business, SAS offers a fully mature program that satisfies the demanding queries of data scientists, yet is also accessible to lesser-trained staff. In keeping with current trends, SAS has upgraded its augmented analytics tools – the use of ML, AI, and automation is now the key demand of analytics customers.

SAS’s well-developed portfolio is geared for the full scope of data analytics requirements. This ranges from complex model construction to analysis, to data prep, to the ability to monitor and manage data trend lines. All of these capabilities are offered in a unified platform, with interactive visualizations of the models. Each of these features is supported by ML, automation, and AI. To assist users, the platform outputs machine-driven predictions, which can greatly expand the query process.

The company’s SAS Visual Analytics platform – in keeping with the times – leverages microservices and the cloud for greater scalability and more flexible performance.

PROS:

• Extensive use of advanced ML and AI tools to aid human-driven queries.
• A unified data analytics portfolio that enables all aspects of next-gen data mining, from prep to visualization.
• A large user base across the globe, which provides an extensive cohort of SAS experts for hire.

CONS:

• Some users view the platform as expensive.

IBM

KEY INSIGHT: IBM is a top contender, particularly for those enterprises that are already focused on the IBM enterprise platform – the integration among data products is notable.

IBM Cognos Analytics is a platform that combines both enterprise-level managed and self-driven query work, along with augmented analytics and advanced reporting. In an improvement, Cognos now includes much of the functionality of IBM Watson. The platform can generate natural language processing and – impressively – natural language generation. It can also perform time series forecasting, which is the ability of a data model to forecast upcoming events based on historical context.

In a forward-looking twist, Cognos is built to offer insights into social data. It also offers data prep that is assisted by AI functionality, which can save many hours of human staff time.

In a bid to serve as many analytics customers as possible, IBM offers any number of cloud and multi-cloud usage options, from IBM’s own public cloud to any of the other cloud leaders. And of course on-prem is also possible.

PROS:

• The robust functionality of Watson is built into the already advanced toolset of Cognos.
• The interoperability between other elements of the IBM data portfolio is well regarded.
• An extensive array of deployment options across cloud and on-premise.

CONS:

• The platform is best suited to customers that are already leveraging other products in the IBM suite of products.

SAP

KEY INSIGHT: SAP offers strong functionality with augmented analytics, making this data analytics tool a top contender.

A compelling feature of SAP Analytics Cloud is the extent to which it integrates a wide array of analytics functionality into a single cohesive solution. This includes advanced predictive analytics and planning functions, as well as core analytics. Additionally, the company has a significant track record with augmented analytics. Rounding out the platform are natural language processing and natural language generation – that is, the analytic metrics are transformed, in essence, to natural conversational language.

To assist with data mining, which is driven by open-ended exploration, SAP Analytics Cloud performs “what if” processing. To speed up the process – a key advantage – the SAP solution also offers a menu of pre-written templates, models, and trend line stories to move the process along without reinventing the wheel every session.

Filling out the capabilities – and in keeping with the extensive integration mentioned above – SAP Analytics Cloud ties into the SAP Data Warehouse Cloud.

PROS:

• A fully integrated product portfolio offers essentially complete analytics functionality in one solution.
• An API menu enables connections with embedded solutions.
• Its cloud-native multi-tenant approach is contemporary and in keeping with today’s key emerging technologies.

CONS:

• For those organizations seeking an on-premise solution, SAP Analytics Cloud is not a fit.

Comparison Chart of Big Data Analytics Tools

As the CEO of Hitachi Vantara, Gajen Kandiah leads a company that offers IoT, data analytics, storage, artificial intelligence and cloud computing; in short, all of today’s key emerging technologies. Consequently, his position – overseeing 10,000 employees – requires him to be current with a remarkable mix of trends, yet also to be focused on the future, an action he calls “skating where the puck is going.”

In a wide ranging conversation about tech and culture – covering topics from data analytics to the importance of diversity – Kandiah spoke with me about Hitachi Vantara’s current and future strategy.

• Hitachi Vantara’s recent acquisition of Global Logic, a San Jose-based provider of digital engineering services.
• Kandiah become CEO relatively recently, in July 2020; he spoke about his experience so far.
• The struggle that many companies have in finding success with data analytics, and some examples of companies that have succeeded, with a view toward the social good.
• What it means to “skate where the puck is going” in the the tech market, and the trend toward customers that seek holistic solutions and partnerships, not just products.
• How the pandemic has driven an ever increasing focus on maximizing the value of data analytics.
• How his experience as an immigrant to the US has shaped his management philosophy.
• His own struggles with Imposter Syndrome, and how he uses this to avoid complacency.
• His deep commitment to diversity and inclusion as essential element of success in business.

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Edited highlights from the interview:

Hitachi just announced it is acquiring GlobalLogic. The company is based in San Jose, they provide digital engineering services. What was your strategy in acquiring GlobalLogic?

Software is eating the world. It’s a software play in many ways. It certainly enables an IoT play, the edge play, the data, data-driven play, if you will.

But in essence, for industrial companies and enterprise organizations to compete, increasingly, software is becoming a core requirement for them. And it’s true for us as Hitachi, as an industrial company, but it’s also true for our clients. And as we were looking for a set of capabilities that could enhance and accelerate and catalyze us as well as our clients, we identified GlobalLogic.

We’re very excited with their core capability, their delivery, the extensive delivery capability, their software engineering capabilities, and most importantly, their ability to work closely with clients to identify new sources of revenue and then build the software that enables them to go and capture it.

The importance of data and maximizing the value of data has really come to the forefront these days, that a lot of companies have gathered a lot of data, oceans of data. I think sometimes they don’t know how to get the most from it. What about some success stories? What’s a company that really has succeeded with data, along the lines of powering social good or an environmental cause?

And so I’ll echo your point, and I think one of the things that we’ve gotten really excited about is the concept of data-driven outcomes. By company, by industry, by segment. But the reality, and you nailed it, is data is growing exponentially and the ability, outside of a select few companies that you keep hearing about all the time, good deeds, bad deeds, you name it, is extremely low.

So talking talking about data and powering good, you know the work that we’re doing with the Rainforest Connection around deforestation is something that is truly both meaningful, powerful and close to my heart.

We are working with them to identify, to predict…a deforestation, illegal deforestation event using data, using sound and to be able to rapidly respond to someone that may be preparing to cut down a forest somewhere in the world and get the law enforcement there ahead of time, even before the event occurs. And I think that’s just a small example of how you could use data to power good and truly you know change some of the environmental elements that we have been talking about.

You’ve talked about helping Hitachi Vantara “skate where the puck is going.” I think it’s really tough to figure out where things are going in today’s marketplace. What does that mean to you to skate where the puck is going in a strategic sense?

You know, in today’s world, especially with a little bit of light at the end of the tunnel around the pandemic, we’re living in a world that business schools do not have models for, right? in many cases, people are coming along who have great strategies, but have never dealt with this type of a situation.

So it is super important for us to listen to our customers, listen to market signals and derive the point of where the puck is going from that process. So it becomes much more client-intensive and lot more listening intensive.

One thing we do see is this preponderance of data, data really exploding and the pandemic has only accelerated that. The second thing is the movement to the cloud; the third is the coming together of both the infrastructure and the products and the digitization of them.

We see our clients sort of either in trying to catch up with data and how to utilize data, in some cases able to make some meaningful sense of data and in very few cases capable of deriving new value from dat. And we think as a data company, based on where we come from, from storage, we have the opportunity to help our clients on that journey, and that’s sort of where we expect the puck to go, and that’s where we want to play.

I know that part of your strategy and your life has been shaped by the experience of being an immigrant, remarkably, when you are young, your family was forced to immigrate to the US to flee a civil war. How has it influenced your experience, being an immigrant to the US?

 You know it’s funny, right, When I first came here, you know we were like you know… The only thing we knew about the US was you know either Charlie’s Angels or Sesame Street, right, so you know and I’m sure you can relate…

So you expect that when you arrive here right and you end up an immigrant, you know you have a couple of bucks in your pocket and you’re in a neighborhood that you can afford, right? It is what it is.

So being the land of opportunity really comes to the fore, and especially going through the political season of last year and so on and so forth. I strongly believe that this is the country that gave me the opportunity to explore, push, learn, understand.

If you were willing to work hard, then we come from a family that works hard, a country of people that works really hard for what you want, this country gave me the opportunities. I had a really strong foundation with my parents you know, father being a military officer from the army, my mother being a Montessori teacher. And being an only child right, so there was a lot of focus on me – and you know there was some disappointment that I’d never turned into a doctor or lawyer or an engineer…

My dad has always been one of these people about, “Listen, listen, listen, listen.” Always listen, because you need to find your own voice, but listen first. And so this concept of ‘you have two ears and one mouth, so you have to listen twice as much as you speak,’ was something that was ingrained in me from the early days, and it’s been a part of the journey, and it’s been a hugely helpful part of the journey.

To first identify who I am and what I bring to the table, then to open up and speak about it. And then as I have had the opportunity to lead teams and companies at this point, to ensure that I’m giving my team the opportunity to voice their opinion, pull it out, understand, connect the dots together, and then set the path forward. So it’s been fantastic – though the beginning wasn’t that great, getting to where we have been has been great.

To wrap up our conversation, I think it’s important to talk about this idea that you’ve stressed, which is leading with inclusion. What does that mean to you, and how does that shape your approach?

First of all, we can all do a lot more around inclusion. I think that it’s a shame that we’ve continued to have to talk about diversity and inclusion on an ongoing basis. But that said, I have found that diverse and inclusive teams tend to be far more effective and successful than sort of the typical executive committee or board room that you would see. Because I think that there’s a sense of balance between market economics, how aggressive do you want to be, the environment, safety, security, privacy…

There’s a lot of things that matter in today’s world that didn’t in the past. And the ability to balance all of that in the context of a P&L that you operate is a critical, critical thing that a chief executive needs to do. And I think to do that you need a diverse team and you need an inclusive team. The diversity of thought that that team brings to the table helps you balance the approach and the strategy that you set for your company, and it factors into how you navigate. Because I think intelligence and empathy are both important, and you don’t always find one person with both.

And again, back to this notion of inclusion, I think however you cut it, I think the more diverse your team is, the more inclusive your leadership style is, the better it is for your business, and so that’s why I’ve been stressing inclusion.

I think one of the best things about our current era is for all its problems, is that that idea [of diversity] is very much on the rise, and I think companies will benefit as they embrace that.

And I think, Jim, that the pandemic actually has accelerated the opportunity for us to bring people who we may not have had access to, who may have gone off to raise their child, or whatever it may be, and was not quite sure how to get back to work.

With clients being more open to your working from wherever you are…Work from anywhere…You now have access to a pool of talent, a very diverse pool of talent that can now become a part of this journey, and I think that is something we should absolutely take advantage of.

To provide insight into the the intersection of data analytics and cloud computing, I spoke with Kent Graziano, Chief Technical Evangelist, Snowflake. 

The State of the Enterprise Data Platform

1. Before the report, let’s talk about the current move toward data in the cloud. It appears that this move has reached a new velocity recently, even before Covid. What’s driving this trend?
2. The report finds that “87% of executives agree: Data is the most important competitive differentiator in the business landscape today.” However, the core challenge that businesses face – a stubborn challenge – is extracting value from their data. Why is this such a challenge? And how does the cloud enable this?
3. An even bigger challenge is gathering data from the wider business ecosystem – partners, suppliers, and customers. How does a cloud-based approach help glean value from this diverse data environment? And what are the potential rewards?
4. How does a cloud data platform like Snowflake transform or expand the work that developers, data scientists and data analysts can do?
5. For data professionals and developers specifically, what are the advantages of using the Snowflake Data Cloud?
6. Future of the enterprise data platform? Specifically, any sense of the future evolution of Snowflake as a data platform?

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RPA enables companies to save precious staff time by automating many business work processes. It can interface with software – mimicking a human – and perform reams of manual work. Those hours and hours of repetitive tasks that employees do can be performed faster, and more cost effectively, by an RPA robot. Think of RPA as “pragmatic artificial intelligence.”

The RPA bot are programmable at many different levels and are gaining sophistication with time.

Yet RPA is still on the cutting edge. The market is growing rapidly – revenues are forecast to grow at a scorching rate – and RPA tools are still seen as “new” by many enterprises.

To provide advice on best practices to maximize an RPA deployment, I spoke with an industry expert, Eric Tyree, Head of Research and AI, Blue Prism.

Best Practices for RPA Deployments

1) Where are we with the RPA market now? It seems that there’s great interest in RPA, but it’s not yet fully mainstream. True?

2) When companies start to deploy RPA, what are common challenges and set backs they encounter?

3) What are some best practices for companies to ensure maximum success with their RPA deployment?

4) Where is RPA technology headed over the next few years. How can companies that want to deploy RPA – or are deploying it now – prepare for these changes?   

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Edited highlights from the interview – all quotes from Eric Tyree:

Is RPA Fully Mainstream or More Cutting Edge?

It’s definitely past the early adopter phase. I wouldn’t describe as mainstream yet.

I think it’s reached the point where most companies are aware of it to some degree. I was first seeing it five, six, seven years ago, actually trying to use it in my previous company, so about a year ago. So I think it’s well known, in other words people are aware of it, they know it’s a technology that’s out there, but I don’t think it’s seen as a mainstream standard tool kit as part of the overall digitization piece. I think it’s beyond early adopter, but it’s definitely still emerging, is the way I would describe it.

Challenges in RPA Deployments

I think the number one problem is it’s too low down on the company, so it’s used by a small group of people in a small business unit, they don’t have the support they need, it’s not sanctioned by senior management, they don’t have the budget and supports they need.

What’s true of automation is what’s true of digitization. The key to success is that you’re doing two things. One, it’s goal-directed, it’s very clear what the business case is for, or what you trying to achieve is measurable. But also that you’re looking at whatever you’re trying to automate, just like with digitization, you can’t just take what you’ve currently got and automate it, ’cause all you’re doing is taking your existing spaghetti ball and automating the spaghetti again, which isn’t really giving you much value.

Whereas successful [deployments] are bit more thoughtful, they really step back and think, “Okay, if I’m going to take my mortgage desk and I’m going to apply automation to it, what I really should be doing, is not looking at my processes, copying them, and just taking the manual working and replacing it with the digital worker.” What you should be doing is saying, “Okay, if I’m gonna redesign mortgage processing from an automation first perspective or a digitization first perspective… ” [Instead] you start with a blank slate and you redesign the entire thing,

And you think, “Okay, where is automation’s appropriate place? Where does human work have its appropriate place.” And affectively what you’re doing is you’re rearranging the way in which work gets done that’s getting the best out of robotics and the best out of people.

It’s probably part of a bigger company strategy, and that’s when you get to success in [RPA].

Best Practices for RPA Deployments

So I think it depends what you want to achieve. So I’m assuming that you’re looking for something transformational. Obviously, if you wanna go ahead and do something small, it’s a different set of priorities.

But let’s just say this is something big and you want to start with your mortgage desk and you’re gonna move to other back office systems, and you’re doing a big automation plan.

The first thing you need to do is make sure you understand what it means strategically. So why are you even doing this? As a bank, are you doing this because the market demands five-minute mortgages and you have to start providing it, and this is all about customer service, modernizing the shop front? Is this something else?

Your problem now is, how do I have to redirect my human capital from non-revenue generating to more revenue generating work? So again, you’re thinking top-down strategy, so what am I gonna do with automation?

In that case is I’m gonna attack non-revenue generating work so I can free up those people to spend more time working with clients and doing the value add. So again, it starts with that strategic alignment. Thats’s the most important thing, without that, it’s gonna sit there and fester as a small project and never get anywhere.

The next piece is to set up your center of excellence. It does take some skill and some knowledge. It’s the kind of thing that a power user can build, you don’t need a PhD in robotics to do it, you need to be the person who is comfortable with macros [RPA is low code].

All I argue is that the bigger your ambitions are, the more senior and the more aligned you need to be with strategy. I think that’s true of any project, particularly if you’re gonna be playing around with the company’s operations.

You want to know that you’re aligned and your boss and your boss’s boss, and your boss’s boss’s boss all know what you’re doing and agree to it.

The Future: Humans and Robots Melding

[With RPA], you’ll respond, and you won’t realize that the person who messaged you was a robot.

And that’s already happening, but we wanna take that to the enterprise scale, which is when you redesign your mortgage desk that’s what you’re designing in, you’re literally saying, okay, if it’s all the sort of generic mortgage applications out there that are a low risk, they get swept through by robots.

Robots are then looking “Ohh this one looks a bit borderline.” It’s going go and ask Joe over there what he thinks, he might ask Susan as well what she thinks, Susan responds. She doesn’t realize that the person who asked Joe in the first place was a robot, it doesn’t matter. It’s just an IM that’s come through Teams or Slack, it’s just a message.

And that’s the trick, and I think the real future of it, it’s not just this enterprise scale. Robotics is increasingly passing the Turing Test, people don’t realize they’re talking to them.

Chat bots are a great example. The trick with chat bots is how do you triage between humans and a robot without someone knowing? So a really good chat bot flips you to a human and back and you don’t realize that it’s flipped.

So it’s that triage process is the key, if you get that very quick, then you get a great customer experience because a robot is handling all the routine stuff. Humans will be able to dive in solve problems that the robot can’t, and the consumer is just getting a great experience and they don’t realize that it was two different types of entity that they were interacting with.

Both have advantages, and both have their proponents. Certainly the HDD has a stronger legacy position and a lower price point. Yet SSDs are clearly faster – flash drives are the high end sports cars of the hardware world.

To provide advice on the most effective strategy for today’s data center, I spoke with an industry expert, Colin Presly, Senior Director, Office of the CTO, Seagate Technology. 

Among the topics covered:

• Some analysts in the IT industry have predicted that the HDD will soon be replaced by the SSD, especially as flash prices have fallen. Is this what you see happening?
• How does the TCO of hard drives vs. TCO of SSDs at scale compare? And what light can TCO shed on this issue?
• What’s the ratio of hard drives to solid state drives in an average cloud data center?
• Can you address arguments in favor of SSDs that rely on TBW and data reduction?
• So the hard drive has a brighter future than some foresee. To what extent is the hard drive’s continued resilience a result of the evolution of the format, technologies like HAMR and dual actuator tech?
• What about the role of cloud service providers? What do hyperscalers mean for balance between SSD vs. HDD?
• How about the impressive performance levels of SSD? Surely, the data bears those out?
• What do you see as the macro trends in data storage in the years ahead?

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Edited highlights from discussion:

Today we’re talking about hardware – but it seems the tech world is obsessed with software.

“Absolutely. Seagate, we’re a hardware company fundamentally. However, we are moving more into software, and we understand that software is a big piece of the world. And software-defined is there because the world is changing so quickly, and for the world to change quickly, you really need software to be able to make that change.

What we see more of is such an open ecosystem now in software, and that’s actually moving into open in hardware as well. With massive developer communities that are developing software together as an industry, ’cause the industry problems are so big that we really need to solve them together, not individual companies.

So yeah, we absolutely acknowledge software is a big piece. What I think we see is that we need to necessarily maybe reduce the cost of the software, because the cost of the software can become prohibitive to the ecosystem growing. So anything we can do to help grow the open communities and reduce the cost of the software there, we can drive more value towards the hardware and ultimately the whole ecosystem can grow that way.”

Some analysts say, “We’re coming to a day where the hard disk drive is going to reach its sunset.” Everything’s going to be flash-dominant, especially as flash drive prices have fallen. How do you see that particular macro trend working out?

“No, that’s really not the way we see it at all now. Solid-state devices are really incredible devices, and there’s a lot of innovation going on in that space. And absolutely true that prices have fallen, and they’re in a different class relative to performance from a hard disk drive.

So all of those things are true, but if you really look at it, the cost differential is still very, very high. You’re talking at least eight to 10x from just a device cost perspective. And while it’s pretty easy to find use cases where it makes sense to move from an HDD to an SSD, maybe in consumer solutions or areas of enterprise performance where performance really dominates all other metrics. Absolutely, it makes sense.

But we like to look at it really that they’re not essentially competing technologies, they’re very complementary technologies.

We need SSDs to be successful as an industry to be able to grow hard drives, to be able to grow storage. We shipped one zettabyte of storage last year as an HDD industry. It’s a massive amount and we’re anticipating to ship four more zettabytes in the next five years. The amount of storage that needs to be created, it’s just immense, so we need SSDs and HDDs to be successful for that.”

The consumer world has made more of a switch to solid-state than the enterprise world, true?

“Oh yeah, that’s absolutely accurate. And I think that’s sometimes where some of this confusion comes in, is the devices we see every day and we work with in our laptops and our phones are transitioning that way.

But I think what the pieces people don’t see is that, what those things are actually doing is they’re enabling really more storage in the data center. Everybody’s taking photos on their phones. IoT now is just emerging. The IoT devices are going be talking to each other, transmitting data, and cars are going be transmitting data and talking to each other. All this data is just going explode. And this data that we don’t necessarily see and interact with as a user, that data has to go somewhere, and it ultimately ends up in the data center.”

Certainly, if you look at the hard disk drive as a format, it continues to evolve in terms of its capability. There’s things like the HAMR, there’s dual actuator technology. I’m assuming these are some of the technologies that are keeping the hard disk drive alive going forward.

“For us, we see massive room for innovation still within the HDD piece. Clearly, perpendicular recording, which is the recording technology we’re currently on. Perpendicular recording is somewhat coming to the end of its life. We see diminishing returns right now in terms of growing areal density with the perpendicular technology.

But yeah, HAMR, heat-assisted magnetic recording. That has a massive promise for increase in areal density. We just had a recent analyst event where we showed some of that data. We have lab demonstrations showing devices that could go to 50 terabytes by 2026, even 100 terabytes by 2030 potentially.

So that allows us to fundamentally really increase the cohesivity of the disk, and then using heat to essentially temporarily lower the cohesivity as we write it, we can achieve massive increase in areal density. So with heat-assisted…we’re already shipping that technology today, and we have plans to transfer to that over the next decade. It will provide us a really big boost.

So as we increase capacity with HAMR, we also do need to address areas where some customers become starved of their capacity because they don’t have enough performance. So even though we’re not still trying to compete with a solid-state drive in performance, what we’re trying to do is keep the access to the data to a point where it’s useful for customers.

And so by adding in a second actuator to the drive, we’re actually now able to double the amount of accessible capacity for customers that have become constrained. So we also have a solution to that to match with HAMR. So, yeah, lots of innovation in the hard drive business.”

Let’s look to the future. What about the trends in the data storage industry, and in the hardware data or software in the data storage industry in the years ahead. What do you see happening and how might a company get ready for that now?

“So one big trend we see is composability and disaggregation as a general trend. There was a phase where companies went to hyper-converged infrastructure for easier deployment, where you put storage, compute networking together.

Now we’re starting to see a trend away from that and more towards composable architectures where you tend to see optimal systems for storage, for compute, for networking acceleration. And these are all backed by new fabrics like NVMe over fabric. There’s new [trends] like memory disaggregation; by being able to disaggregate all the components of the ecosystem, you’re now able to scale independently, whereas before when you converge everything together, you become locked with certain ratios of storage, compute memory. And by disaggregating everything, now you’ve got to a point where you can scale them differently.

And that comes back a little bit to the software too, once you’ve now got to a hardware disaggregation that really enables new techniques like containers and Kubernetes. These types of technologies that can compose different services and storage and compute above the devices, allows much easier provisioning of those because now you can access those type of resources on demand and be able to scale them independently. So that’s a really big trend.

So other ones are security, it’s clearly a big one, massive, the whole industry, intense security with a fundamental tenant of security at rest. We’re seeing much more prominence in people wanting to secure their data at rest and in flight as well. So those two are big ones.

And then back to the software piece, object storage, we do see that that is growing and growing and growing. People need big, cheap ways of storing data and object storage is really, really good for that, and it has a very rich metadata associated with it.

So the ability to find the data and be able to use it and unlock it, feed it into different AIML algorithms, object storage is really, really growing and there’s a very vibrant open-source community too that we’re now contributing to with Seagate’s CORTX solution. It’s a way of really unlocking the value of object storage and making it cost-effective for the world.”

The very term data analytics infrastructure is itself far from simple. It’s a wide ranging concept that comprises the many technologies and services that support the essential process of data mining for competitive insight. These many elements include managing, integrating, modeling and – perhaps most important – accessing the rapidly growing data sets that allow companies to better understand their business workflow and forecast market moves.

The challenge of data analytics is that it changes faster than you can say “business intelligence.” The technology itself is now undergoing rapid evolution, as is the techniques that practitioners are using. This is one sector where even an approach that has seen no refresh in a mere six months is already falling behind.

To provide a current snapshot, I’ll speak with Brian Wood, Research Director, Dresner Advisory Services. Wood will discuss the new report from Dresner, 2021 Analytical Data Infrastructure Market Study.

Among the questions we’ll discuss:

• What use case for ADI platforms did most respondents list as a top priority? What does this mean for the ADI market?
• It seem as if corporate standards have been a low priority for ADI, compared with security and performance. What changes do you think this trend will create?
• Is cloud or on-prem more popular for ADI platforms? What about the hybrid platform?
• Are there factors that make creating a coherent strategy for analytics projects difficult? (Like the range of innovation and the variety of ADI platforms.) How can business leaders deal with this challenge?
• Your sense of the future of the ADI market, several years out?

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Edited highlights from the full discussion – all quotes from Brian Wood:

Need for a Chief Data Officer

“One of the things that tends to help to limit this kind of [problematic] spread across the organization of different components is having a chief data officer, CDO or a chief analytics officer. Because that becomes a focus for them to make sure they have a cohesive and efficient analytic data infrastructure as opposed to a little of this here and a little of that there.

In most cases [the CDO] don’t really play the role of the cop trying to enforce it, although if you have the C in front of your title, you tend to get attention.”

Governance vs. Compliance

“To me, the only difference between governance and compliance is where the requirements come from. Governance is placing requirements on yourself. They’re internally. Compliance is from external.”

Shadow IT for Data Analytics

“Corporate standards [for data analytics practices] aren’t important. If one person finds a tool that is purely cloud-based and web-based and it works well for them, they will go ahead and buy it.

A lot of these tools and products have freemium models where someone can put their personal credit card in and use it for a month and then of course once they get used to the tool they’re not gonna let it go, and it becomes part of your analytic data infrastructure.”

Hybrid Reporting – and Hybrid Cloud

“One of the things that I find interesting is, even in the large organizations, they want everything in the Cloud, but they’re not starting from a green fields situation. They have lots of On-premise type of systems already.

But in order to get there from where you are today, you need a hybrid analytic infrastructure.

It has to report on the On-premise and the Cloud. But of course then you have multicloud as well. You have multiple public clouds, you have virtual private clouds. Having an infrastructure that will work with all of those, and I think particularly for the larger organizations that have been around for longer, it’s a stepping stone on the path if they wanna get to Cloud.

And most of them do. The survey says that is a preferred deployment approach for most industries and most functions. But in order to get there you have to go through the hybrid to get to a Cloud infrastructure.”

Future of Data Analytics

“So I’m often called an idealist, because I tend to look at the way things should be instead of the way they are. [chuckle]

So I’ll say, with a grain of salt, I will say that we will have AI capabilities that will enhance the way we do our jobs and not replace them. The future of work part aspect of it is one part of it, but realistically, we have models that do a lot of pieces of what a human brain does well, but there isn’t the master algorithm.

And so, what you’ll have is you’ll have the ability for an AI system to look at the different analytics in your organization and make recommendations, like, “This is good, but really you’re only using the trending. You’re not using the actual data.”

Obviously, now we’ve got models that beat the best chess players and all that. But you have to have something to model. So the way humans process information may or may not be the most efficient, but just taking that and putting it on a silicon substrate instead of soft wetware, so to speak – that helps a lot, but you still need the people to say, This is how I think. These are the connections that I make that led me to this conclusion.'”

Yet despite its apparently sleepy profile, data governance is a dynamic, rapidly changing endeavor that plays a crucial role in a company’s operation. Data governance is about properly planning and managing data flow, all while monitoring compliance and safety; without it, no organization can effectively function. Moreover, data governance enables a company to mine its data for maximum competitive advantage.

In short, data governance will – increasingly, in the years ahead – separate the winners from the losers.

To provide guidance on key trends in data governance, and to share insight on best practices, I’ll speak with Susan Wilson, VP, Data Governance, Informatica.

Among the topics we’ll discuss:

• At what stage are companies, generally, in terms of data governance? Where do you think the biggest pain points are among companies in their data governance practice?
• If we look at cloud computing, it appears that trends like multi-cloud and cloud native are shaping its development. What are some key trends or technologies in data governance that are playing a similarly key role?
• What role does AI and ML play with data governance? Is it mainstream yet?
• If a company asked your advice for a few essential best practices for data governance, what would you recommend?
• Data governance in the future, say 3-5 years out? What will the sector look like?

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Where do you think companies are – generally – with the idea of data governance? What do you see as some of the big pain points with data governance?

Let’s take a walk down history lane for just a bit. I’ve seen in years past 2017, 2018, go back that far, largely data governance was about regulations. It was about more of the risk-based approach to data.

Fast forward now to 2020, we started to see this in 2019 and 2020, and certainly in 2021, it is really about helping customers to see the value of data. And in fact, many of these organizations that are looking at self-service analytics say, “We need data governance in order to effectively deliver that.”

The challenge is with the sheer volume of data that companies are generating today, the whole concept of delivering transparency and trust with a balance of right data ethics, data governance and privacy has now also become even more critical.

And in general, data protection rules have definitely kept a lot of our customers awake at night. And so we’ve helped them with their data governance or framework that enables them to define and document standards, the norms and the accountability and ownership that’s needed.

No, [data governance] is not under control and what [companies] are finding is that they’ve got these goals to make data more accessible but in a trusted way. And so the ability to know the data they have and to have the appropriate quality are things that are very much top of mind for them. And so they’re seeking our help, not only in our technology, but also the best practices.

What are some technologies or trends that are shaping data governance these days?

We live now in the cloud AI era. What fuels the cloud AI economy is data, right? Businesses are increasingly becoming more and more data driven in how they run their supply chain, their marketing, their customer service. It can’t be off of gut feel.

I’ve gotta use data to make these decisions especially with these new market conditions too that we’re all dealing with. And they are also increasingly moving their data to the Cloud. Especially during this global pandemic, we saw massive transformations there.

So new data types and sources are contributing a huge amount of information that needs to be assessed, curated, cleansed and protected so that anyone, whether it’s business or IT or any system, can also use it.

So the answer really is an intelligent data governance solution that can help you with that. The transparency, the quality, the protection, the data access, one that is automated and can scale to handle the largest data lakes and is intelligent not only in just name only.

AI and machine learning is in our company’s DNA. We were born with it, [chuckle] with all of the metadata management that we’ve invested in over decades, and it’s really at the core in everything we do at Informatica.

And we doubled down on AI machine learning, especially over the last three years with continuous investment in our R&D efforts and launched the industry’s first metadata-driven AI engine called Claire. And in fact, the way that it spelled is C-L-A-I-R-E. [chuckle]

Data Governance Best Practices

I have this conversation just about every single day.Basically the clinic hours with Susan. [laughter]

How do we derive more options? How do I really make this sticky ’cause there’s a lot of data governance programs. They’re at their fifth attempt and they’ll lose their credibility with their enterprise.

And so I always say always start with the business problem that you’re solving for. Always think about the experiences of the consumers, of people that you need to benefit from solving this business problem, and that might be a business analyst, that might be a data scientist. It could be your executive team. And so having, I call it the wisdom, the “What’s in it for me” in mind of that individual is critically important. Don’t just say, “I’m going to implement the full glossary to terms for findings.”

What’s the end in mind? It’s all about what’s the business problem I’m solving for and how do I make it stick it and build from there? Because listen, data is now recognized as one of the most important strategic assets and enterprise has to manage.

And data is a powerful asset when it’s governed, meaning I understand it, I have the context rendered, its quality I can trust. And that’s critical for any data-driven transformation and initiative.

So I always say, “Look for the data initiatives in your organization that you can have the biggest impact but mediates a small amount of effort – so find quick wins, right? And those quick wins could be with self-service analytics. It could also be with a regulation. It could also be helping to break down the data silos across maybe finance in your research organization.

So it’s looking for those data initiatives and really using that as an opportunity to start small: “Think big, start small, scale fast” and find those opportunities and use them and to grow your program over time.

Future of Data Governance

Looking at my crystal ball, automation is going to be playing a bigger role in data governance and privacy in the years ahead, and customers will get more and more value out of it. We’re building a lot in. We’re becoming a lot more aware of what a customer is doing, what the customer needs, the broad set of capabilities.

It’s not like the Cloud. We know things about your systems. We know what you’re doing with the data. We have the data supply chain in mind. We also understand the business objectives, so we’re putting it in the R&D space more to get more out of the value of data for companies. So more is gonna be built into the focus on establishing a solid foundation now and getting more people engaged with that trust of data.

Governance programs will also support more projects in every corner of the organization because they’ll be able to scale faster. They will be more automated. We’ll be able to provide more insights into how they can connect ’cause there’ll be a lot more intelligence about data from finance, data from operations.

And the technology that supports these programs will also likewise scale, replacing more and more of the manual tasks with automation and machine learning methodologies to help reduce that friction and enable customers to get more accurate and consistent answers out of their data.

To get the data governance right, though, they’ll need to go live with their applications and services faster and empower more lines and business to do great things, make faster, better decisions and unite their customer data. In short, we really do believe that for data that’s properly governed, the sky is really going to be the limit.

Indeed, hybrid cloud has earned a place as a default enterprise technology. This is true despite the fact that Gartner predicts that by 2025, 80% of enterprises will shut down their traditional data centers.

A key technological linchpin in hybrid cloud is hyperconverged infrastructure, or HCI. Once mostly hardware, today HCI software is undergoing rapid adoption. Software-defined, benefitting from API technology, HCI allows sophisticated and flexible cloud management.

To explore the rapid growth of hybrid cloud and HCI, I will speak with Wendy M. Pfeiffer, CIO, Nutanix. Among the questions we’ll discuss:

• What’s your sense of where companies are in the cloud journey, and is hybrid cloud merely a transition, or is it the new default?
• What role does HCI software play in the hybrid cloud?
• Where is the HCI market, looking out several years in the future? The market reached an impressive $2 billion in revenue in Q3 2020. What key trends and technologies do you see shaping the HCI market going forward?
• The larger picture of enterprise IT: Why does enterprise tech seem to lag the functionality of consumer tech? Will IT ever catch up?

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Edited highlights from the interview:

If I look back at the prehistoric history of the cloud, way back in the year 2012, many pundits said, “the public cloud will make the data center obsolete.” But here we are in 2021, the data center’s very much alive, which means, of course, that hybrid cloud is very much alive because it connects the data center and the public cloud. Yet we’re in this period of rapid shifting. Gartner predicts that by 2025, 80% of data centers will be shutdown. Of course, Gartner’s not always right. What is your sense of where companies are in their cloud journey? Is hybrid cloud the default now? Is the war over and hybrid cloud has won, or is this just a transition?

Oh my gosh, I love this question. First of all, I love John Madden, and John Madden always says that pundits are guys who aren’t in the game. And I have a lot of respect for the pundits, but most of them sort of over-index on technology for technology’s sake.

So there was a great survey that was just completed by Vanson Bourne, it’s the third year in a row. It’s called the Enterprise Cloud Index. Every year, they ask more than 3,000 CIOs, CTOs, VPs and above in IT around the world what is their ideal operating model. This year, the 2020 survey, 86% of the 3,400 people polled said that, “Hybrid is the ideal operating model for me.” That’s current data. Now, this doesn’t mean, “I don’t want to be in public cloud.” It doesn’t mean, “I don’t… I only want to be on-premises.”

What this means is, “I want the optionality to run workloads in the place that makes the most sense at the time, and to change things up.” And in order to do that, we need some components.

One of the components is, “We need underlying infrastructure that makes flexible use of the associated resources,” and that’s the sort of the magic of hyperconverged. So hyperconverged infrastructure essentially says, “I can treat my on-premises infrastructure the same way the public cloud vendors do. I can dynamically, elastically assign resources to the workloads based on the needs of those workloads.”

And so if I’m operating in a mode where I can do that both on-premises and in public cloud, then I’m truly operating in a hybrid mode, and there’s all kinds of benefits and economies of scale to that. I can very easily spin up infrastructure, whether on-premises or in public cloud, and I can write the code once and re-use it everywhere, this thing that IT calls infrastructure-as-code.

The Value of Hybrid Technology

If we use technology and electricity in the way that we managed steam [in the steampunk era], then it won’t work, right? We need new modes of operating. But now, we can turn those new modes of operating back to our old technology and things like hybrid engines. The Prius is a wonderful example of gas and electrical.

And it’s been more successful than any purely electric car or purely gas vehicle because that mixed mode is the mode that allows us the flexibility, and the range, and the capacity, and so on. And so this is one of those moments where we’re in this transition, and we can take the best of all worlds.

Is HCI software almost a synonym for cloud management these days? In essence, how does HCI work with hybrid cloud?

One of the things that we want to be able to do with our cloud, which is just our place that we operate, is to get the best possible use out of our capacity. The best possible performance, the best possible capacity.

For example, it’s the difference between, let’s say that I have 100 barrels of oil to transport across the desert. I can get 100 Jeeps and 100 drivers, and I can drive those 100 barrels of oil individually across the desert, and I might lose a few along the way. Or I can put them all on a single train, and that train can make its way across the desert, it will expend, we now know the metrics on this, 100,000th of the fuel. The 100 barrels of oil will arrive more quickly, and so on.

It’s the same idea when we’re talking about hyperconverged infrastructure. Hyperconverged infrastructure is like the train and the train tracks versus the 100 Jeeps of all of these, “Hey, I’ve got storage, I’m doing it this way. I’ve got network, I’m doing it this way. I’ve got compute, I’m doing it this way across five different locations.”

When we look at hyperconverged, we say, “Look, I’m using all the power of the engine and all the structure of the tracks to do the heavy-lift. By the way, behind all that is multiple different cars with multiple different loads and multiple different storage methods and containment methods and uses at the other end, but I’m putting the infrastructure to work in a way that is most efficient.” And that’s all that hyperconverged is.

And so what we’re saying here is we can use the same theory for how we utilize and access resources in the hyperscalers data centers as we do on-premise.

If we use that same, the exact same code for how we access storage and compute. That is, an operating system running storage and compute on hardware in a public cloud vendor’s data center and/or on-premise, the minute we start doing that, then we get to behave the way that people do when they spin up a workload in public cloud. We get to write controlling code and operating code once, and use it the exact same code everywhere. We get to do software-defined things. We get to do DevOps in an IT way.

Enterprise Tech vs. Consumer Tech

My perspective is that the past was just wiped out. And so I’m not gonna opine on whether or not we’re old and slow, but today, a huge percentage of the global workforce is working remotely. And so what we’re doing is we have dropped enterprise technology into the milieu of consumer technology, and it’s swimming in that pool.

And what’s happening is our employees, in order to be productive, which is core to IT’s mission, our employees are having to blend those two, and they’re making choices based on how they work, what they’re comfortable with, etc. And so this has led to a proliferation of consumer tools being intermixed with enterprise tools and us having to figure out how to secure that, how to support that, how to make that performant.

In some cases, employees are choosing consumer tools over enterprise tools because they are more productive from their home environments. And even the enterprise tools are massively changing. For example, we’re speaking over Zoom, and you may have noticed that Zoom has added some new filters. Now, there are all kinds of things. You can put a funny hat on or glasses, all kinds of things, right?

It’s very consumer. That’s blended in an enterprise app now because the people using that enterprise app and paying for that enterprise app have come to rely on some of those features as part of how they communicate.

And so we’re seeing that blending happen and we’re seeing acceleration of that blending happen, but also, in short, IT needs to be less precious, less egotistical about the things that we’re offering.

And we need to securely and performantly and cost-effectively open up our environments, our ecosystem to all of the players in that ecosystem. We are richer, more productive, our employees are happier when they are interacting with the capabilities that we provide in ways that are personalized and ways that involve choice.

And ultimately, that’s the secret to consumer tech. The secret to consumer tech is that I can choose to use the things that are natural to me. I can choose to use the things that are delightful to me. And look, if we have employees using things that are natural and using things that are delightful, we have math that says those employees are more productive, and that is better for our companies and our societies and all of those things.

And so employee happiness ties to employee productivity, ties to the interaction design and the way that they are connecting with technology, especially accelerated right now during this time of mostly remote work.

Yet challenges abound. How can workers stay connected with one another in a remote environment? How can the previous enterprise management structure survive, based as it was on face-to-face interaction?

To explore those topics, I spoke with four top thought leaders:

Myles Suer, Data Principal Product Marketing Manager, Dell Boomi

Sophie Wade, Founder, Flexcel Network

Ian Gertler, Influencer Relations, Citrix

Dion Hinchcliffe, VP and Principal Analyst, Constellation Research

Among the topics we discussed: 

• How has COVID-19 acted as an accelerant for changes that were already in process regarding the way we work?
• Where has technology made remote work possible? Where has it let workers down?
• In the past, remote workers were 2^nd class citizens. If over 30% of workers remain remote and a majority of workers become hybrid workers, where do HR IT leaders need to invest to drive better worker experiences and streamline internal processes?
• Geoffrey Moore argued a few years ago that the focus should move from systems of record to systems of engagement. How would this cause us to rethink how workers engaged with all corporate systems?

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Edited highlights from the full discussion:

How COVID-19 Has accelerated Changes Already in Process

Suer: Well, those of us who were remote workers, about 8% or so of the population, we got the leftovers for a long period of time, and we were forced to try and interact with others who were there and present.

And I think every conversation I have with CIOs, they’re actually trying to figure out now, and they feel like they now have the charter to figure out: how do we drive better experience? But COVID has accelerated that, and I think also the CIOs who got in trouble weren’t ready in any way. They didn’t have remote technology, they didn’t have laptops for folks, they didn’t have Zoom and those kinds of things, and they had to scramble. In many cases, it didn’t go well for them.

Wade: Well, for me, the acceleration’s been extraordinary, and the way I talk about it is, the future of work, which I’ve been predicting, we’d all been thinking about it, it has arrived.

And some of the ways that it’s been accelerated…to the extreme is forcing so many people to be working from home, or working in much more pressurized environments on the front line. Companies pivoted in extraordinary periods of time and had to automate and digitize in order to be able to do that and had to identify a lot of their workflow.

The companies, as Myles was talking about, many of those who weren’t prepared didn’t know how their work flowed through the organization, and therefore they couldn’t pivot, because they didn’t know what was happening, who was doing what, and if I needed to take something down the office, down the corridor to John, how was that gonna happen when you’re working in a different way?

So and there was also safety. I was interviewing the CEO of a company that makes peanut butter, and they had to do a lot of digitization along the manufacturing chain in order to not have lots of physical hand-offs of pieces of paper, so there were so many different things for safety reasons as well as decentralized workforce reasons that had to be put in place because of COVID.

Gertler: Well, I think we’ve heard a number of things over this past year. What I’ve seen and heard is an interesting stat that what people generally saw as a multi-year digital transformation plan, so three to five years, instantly happened within a few weeks to maybe a month or two.

And I think probably the most interesting thing is digital transformation. And it’s been hyped for most of the past few years, and when you see that, the initial thing that comes to mind is always, “Oh, it’s all about technology.” I think what the pandemic did was show this transformation is both technology and people.

It’s like a DNA strand, and each strand they have to wrap around together and work together, or nothing comes of it. So, I think we’ve seen the evolution of technology and transformation actually happening with people, but you have to be careful because we’re finally seeing well-being addressed.

We’re finally seeing burnout because people don’t know when to cut off. You could say, “I like working remotely because I don’t need to commute, I don’t need to worry about the extra time being away from the family,” but at the same time that commute to the office and from the office is often your stop and starting points. For the better.

How Does Tech Enable People To do Their Best Work?

Wade: Well, for example, the CMO of Workfront, she has been a remote leader for a very long time, and she has used a combination of Slack, and an open calendar, and when she basically shows that she’s online on Slack and she’s blocked out her calendar, and it isn’t a blocked out with something specific, which isn’t necessarily identified, she really means any person in her team can drop in, and she honestly means that.

And she encourages that, so she enables people to try and not duplicate, but have some of the same benefits of being able to drop in and out, to be able to communicate effectively using those different channels and to focus people on, do you really need to chat and have a meeting about it, or can you do it with just like pinging me via chat to communicate something?

So, I think there are different technologies, just as you see, some people love email, some people love text, and different communications channels help people get the work done in ways that are not as intrusive necessarily as having to go all the way to having a meeting.

Technology and Remote Work: Upsides and Downsides

Hinchcliffe: Well, there’s no question that the Internet and the cloud has allowed us to all work from home in a way that we couldn’t have even five years ago. The bandwidth was not good, the meeting tools weren’t there. So that was great.

The problem is, it created a support challenge. I did a survey of CIOs recently and asked them how they think that their workers think they’re doing, and most of them think they have significant challenges even now supporting workers in these highly varied environments.

The good news is: most workers now built out a place to work, but we have cyber security challenges, we’ve got issues with middle management not having been taught, “How do we keep my workers connected and engaged with the mothership?”

That’s the thing I’m hearing about now is, it’s a year later, or almost a year later, people are kind of drifting away. They have this sense that, well, they haven’t been in the office and they haven’t been with their co-workers in so long, and these digital tools are good at engagement, but maybe we’re not using the best ones to keep people connected.

So they actually have a sense of what’s communal belonging, what’s going on in the rest of the organization. And while there are solutions for that, they’re just not used widely.

And so we see the real challenge is: just how do we sustain this long-term? Now the good news is, maybe by the middle of this year, we won’t have to anymore. It’s starting to look very good, but we know the future’s probably gonna be little bit bumpier than the past, so now is the time, I think, to get the skills and the infrastructure in place, and we have a ways to go yet for that.

Work-Life Balance in Remote Work

Gertler: But I think, when I think about all that’s going on, I never liked the phrase, which you always hear, it’s one of the big things that certain people push hard, “work-life balance,” because balance is so subjective.

How can your balance be the same as my balance? So I always said, “It’s more about work-life integration, right?” It’s about connecting each part of your life and work so that you can have them co-exist, and there’s a leveling that happens at certain points of the day, certain business trips versus family trips, but what I think it is, to bring it back to technology, it’s like an API.

So an API is meant to connect different systems together so they can be integrated and there can be a cohesive integration of them working together to help you. But you don’t see all of that data coming in at the same time non-stop with it as you need it, as you request it. That’s where I think the work-life integration now, the remote thing is challenging for many, because they need that kind of physical structure at times to understand, “Well, when am I working and what am I not?”

How Should Management Handle this Tectonic Shift to a Hybrid Environment, Combining Remote and In-Office? 

Suer: It’s interesting, I just ran a CIO chat, and a lot of the CIOs were lamenting about really three things: One was, there used to be a transition time between meetings, and we don’t seem to have that, we can have Zoom stack right after each other.

The second was that they would often get early to a meeting, so they could talk to folks around the edge of a meeting, and that’s gone. And then a lot of them really subscribe to Tom Peters’ idea of management by walking around, and they can’t do that.

And so, the thing I learned really from the CIOs in the chat was that people and process always have to come first. And so technology will have to morph to that, but I think we have to figure out those people process elements, how do we stay engaged? How do we get more interaction to happen when we’re in a media like this [Zoom meeting].

Wade: I would say those beginning and endings of meetings are incredibly important. And so whether those are deliberately put in as buffers because they’re always kind of like, “Hey John, I wanted to talk to you just about this before everybody comes on,” and to round out at the end of the meeting.

So it’s about these boundaries and creating space and time; as well as having those mandated non-work calls, videos calls, to shoot the whatever, just to chat about stuff, to find out how it’s going on. Because one of the key elements that we’re hearing about so much, the second pandemic is going to be the mental health issues that are coming through this.

Not just the burnout, but the overwhelmed, the challenging situations that, I can be great today, this week, but next Monday I might have a bit of a shaky moment, and so managers need to be checking in a lot and dealing. Leaning in much more to their team members, seeing how they’re doing, and authentically checking in to see how people are doing.

From Systems of Record to Systems of Engagement

Suer: Yeah, it was interesting. I remember having to learn how apps worked. That was the essence of you took a new job and suddenly they said, “You use this,” and you have to go spend time doing it.

And millennials are saying, “No, no, no, no, this is where the apps live.” And I know how to use them because I’ve used every other app that’s out there. And I just think of things like expense reporting, I remember having to staple things together and hand it to my boss and then it would go from place to place to place.

Well, now I just take a picture and put it in there and push a few buttons and it’s done. I think we have to create great user experience, it needs to be easy, and I think that’s really gonna be the challenge for companies because they’re not gonna keep their workers if it becomes hard in a remote scenario. So I think this is a wake-up call.

Hinchcliffe: Yeah, I’ve actually spent a lot of time on the intersection of those two worlds. The systems of record came first. Now, systems of engagement is something we’ve been working on the last 20 years or so in a major way, and the problem statement is that there are two separate things. And there is this artificial gap between the two and it hasn’t been good for us because most work is unstructured communication and collaboration, and then you have the work product of the decision that comes out or whatever, and that goes in the systems of record.

So most of the interesting stuff is on the engagement side and has all the context, but it’s kept in a different system, it’s separated.

So what we’ve learned is that this artificial distinction we’ve created because of how I grew up in IT, has moved into our organizations and made things complicated, needlessly fragmented and siloed.

And so now there’s this big push like, “How do we unify those together? How do we create a more seamless employee experience?” Especially now, we don’t need all these different touch points that don’t have context, are not connected together and our work spans all of this disjointed landscape. So that’s the frontier right now, and a lot of people, a lot of organizations, a lot of vendors are trying to solve that problem. We’re getting closer and closer.

Wade: Right, we’re going through a huge amount of change. I know the reason behind it, but this extraordinary disruption that we are going through is bringing the future of work, and it’s giving us the opportunity to actually do things differently.

Rather than stay stuck in our entrenched way of doing things, actually integrate the technology that we need. Not have budgets that are just trying to sort of make do, but really kind of strategically plan for what’s ahead, and I think that’s huge.

But I think we’re not quite ready for simplicity, if I may, because…A lot of people have got to work out where we are, and we’re not through it yet, and it’s going to be…COVID is gonna be integrated into our lives in different ways for a long time. So I completely hear and I agree with you but I just don’t think we’re quite there yet.