Data Science Market Trends

When famed mathematician John W. Tukey postulated that advanced computing would have a profound effect on data analysis, he probably didn’t imagine the full extent to which this yet-to-be-named field would become embedded in all facets of future society.

This was in 1962, three years before Intel co-founder Gordon Moore famously authored the eponymous “law” predicting the doubling of computing power every two years.

That said, Tukey wasn’t far off the mark; in his seminal treatise The Future of Data Analysis, he ponders the instrumental role of computers in data analysis as an empirical science: “How vital and how important … is the rise of the stored-program electronic computer?” As it turns out, crucially vital and important.

Data Science

Though these foundations were laid back in the early 1960s, it wasn’t until 2008 that data science really came into its own as a specialized field for the organization and analysis of massive data sets.

At its core, data science is concerned with extracting meaningful insights from the volumes of structured and unstructured data produced by an organization.

Google Chief Economist Hal Varian puts it best when outlining the importance of data science practitioners:

“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”

Echoing Varian’s sentiments, global market research firm Market Dive estimates that the global data science platform market will surpass $224.3 billion by 2026, from a $25.7 billion market size in 2018, with a CAGR of 31.1% during the period between 2019-2026.

See more: Data Analytics Market Trends 2021

5 Trends in Data Science Software

From digital twin models powered by data analytics to autonomous, self-managing data warehouses, these are some of the top trends defining what data science software will look like in the coming months and years.

1. Time-series database dominance

Mobile device ubiquity and increased IoT adoption are widely regarded as driving forces behind the data explosion we currently find ourselves in.

Datasets generated by these devices — known as time-series datasets — resemble collections of values created between equal time intervals, organized in chronological order (e.g., temperature/humidity values, login date/times, air quality measurements). Specialized databases called time-series databases (TSDBs) are designed to handle these types of continuous data streams. 

TSDBs are currently the fastest growing database type in the enterprise. Some popular TSDBs include InfluxDB, Graphite, TimescaleDB, and AWS Timestream, not to mention Kx’s Kdb+ — the world’s fastest time-series database.

2. Data warehouses continue migrating to the cloud

Infinite horizontal scaling continues to be an irresistible draw for enterprises as they migrate from traditional on-premises deployments to cloud-based data warehouses.

Other cloud-native features like clustering to support at-scale data services and advanced aggregation/analysis engines make data warehouses ideal for “born-in-the-cloud” SaaS providers. 

Analysts predict the cloud data warehousing market size will reach $3.5 billion by 2025, with leading players/offerings to include Firebolt, Snowflake, and AWS Redshift, to name a few.

That said, on-premises data warehouses are still dominant — according to a recent TDWI report, the majority of those interviewed (53%) were still hosting their own data warehouses on-premises. 

3. Rise of AI-enabled databases

Though it’s true that most data warehouses still reside in the corporate data center, an emerging trend is further incentivizing enterprises to move to the cloud: the rise of AI-enabled databases.

These autonomous databases feature a bevy of self-grooming/self-optimization capabilities for putting cloud-based data warehouses and data lakes on autopilot, at scale.

For example, Oracle’s Autonomous Data Warehouse automatically scales compute and storage resources, provisions/configures/tunes databases, and bolsters resilience with self-securing data protection and security controls. Not to be outdone, many of the leading cloud data warehouse players (e.g., Snowflake, Firebolt) offer similar features in their solutions.

4. More powerful data visualizations

Humans are highly capable of interpreting and deciphering images but exceedingly poor at reading spreadsheets and reports. Luckily, for data wranglers, the task of extracting meaning from the proverbial business data “water hose” is becoming increasingly simple with the help of recent innovations in data visualization.

Vendors like Tableau, Qlik, and Kibana have been taking the lead with solutions for instant visualization of highly structured data, while players such as Grafana and Prometheus offer solutions for a myriad of time-series visualization use cases, like application and server monitoring.

New data visualization paradigms are also emerging that enable even more novel approaches to the handling/managing of large datasets. For example, in the case of high-fidelity digital twin development, users can now integrate their datasets with virtual/augmented reality services, like Amazon Sumerian for data manipulation in 3D space.

See more: Trends in Data Visualization

5. Continued DataOps adoption in the enterprise

DevOps made quite a stir when it arrived on the software development scene with its set of practices and methodology for improving application quality and delivery. Since then, variations of the portmanteau have emerged: MLOps for machine learning, AIOps for analytics insights, DesignOps for Agile UI/UX development, and more.

Naturally, data science also has its own flavor of DevOps called DataOps. Like the other derivatives, DataOps borrows its tools and processes from DevOps (mostly related to change management, quality control, integration, and automation) for bringing about agility in the management of complex data infrastructures and more speed/accuracy in the resulting analytics.

According to Gartner Research Director ‌Soyeb‌ ‌Barot, creating “a‌ ‌common‌ ‌architecture‌ ‌pattern‌‌ helps‌ ‌with‌ ‌operationalizing‌ ‌data‌ ‌science‌ ‌and‌ ‌ML‌ ‌pipelines‌ ‌and‌ ‌has‌ ‌been‌ ‌identified‌ ‌as‌ ‌one‌ ‌of‌ ‌the‌ ‌major‌ ‌trends‌ ‌for‌ ‌2021‌.”

So as ML gains a broader foothold in the enterprise, DataOps will become even more critical for incorporating ML models into new/existing business workflows.‌

Conclusions

If data is indeed the new oil, then information is the fuel that powers today’s digitized economies.

By the same token, traditional data science methods are akin to drilling and pumping for pay dirt — highly unsophisticated and prone to error.

Fortunately, with these five data science software trends gaining prominence, today’s organizations have at their disposal plenty of options for extracting valuable business insights from enterprise data. 

See more: Best Data Science Tools & Software 2021