Raw data, oftentimes referred to as source or primary data, is data that hasn’t been processed, coded, formatted, or yet analyzed for useful information. Whilst being a valuable resource, raw data is incredibly hard to comprehend or act upon, as it’s visually cluttered and lacks cohesion.
Companies, corporations, and organizations alike can use raw data to collect information about their targets. This, however, requires them to structure and organize the data into a form that’s easier to read and visualize into diagrams and graphs.
This article will help aid you in understanding the various use cases of raw data and how it’s processed by data analysts and scientists. You can also learn more about big data with our library of courses on TechRepublic Academy!
Table of Contents
- How Is Raw Data Used?
- Collecting Raw Data
- How Raw Data Is Processed in 5 Steps
- 8 Examples of Raw Data
- Why Is Raw Data Valuable?
How Is Raw Data Used?
Raw data is data that’s been collected from one or multiple sources but is still in its initial, unaltered state. At this point, and depending on the collection method, the data could contain numerous human, machine, or instrumental errors, or it lacks validation. However, any change that serves to improve the quality of the data is known as processing, and the data is no longer raw.
As a resource, raw data has infinite potential, as it comes in a variety of shapes and types, from databases and spreadsheets to videos and images.
Collecting raw data is the first step toward gaining a more thorough understanding of a demographic, system, concept, or environment. It’s used by business intelligence analysts to extract useful and accurate information about the condition of their business, including audience interest, sales, marketing campaign performance, and overall productivity.
Raw data is often cherished for having limitless potential. That’s because it can be recategorized, reorganized, and reanalyzed in several different ways to yield different results from a variety of perspectives — as long as it’s relevant and has been validated to be credible.
Collecting Raw Data
How data is collected plays a key role in its quality and future potential. Accuracy, credibility, and validity can be the difference between a database of raw data that’s a wealth of information and insights and a waste of space that barely produces any actionable results.
The first and most important step of collecting raw data is to determine the type of information you’re hoping to extract from the database afterward. If it’s userbase and customer information, then online and in-person surveys should focus on a specific age and geographical demographic, whether the process is done in-house or outsourced to a third-party company.
Other types of raw data may require planning in advance. For instance, collecting data from log records would require having a monitoring system in place for anywhere from a few weeks to a year to collect data before being able to pull it.
Second is the collection method. Choosing the appropriate technique can reduce the percentage of human or machine errors you’d have to scrub out when cleaning a raw database. Generally, electronic collecting methods tend to result in lower error rates, as you’d be eliminating the factor of illegibility of handwriting or hard-to-understand accents of slang in the case of audio and video recordings.
Once you’ve determined the source, scope, and methodology, only then does the actual data collection begins. Raw data tends to be large in volume and highly complex, and the actual volume of data acquired can only be estimated during the collection process. An accurate number is only found after the first step of processing the data, which is cleaning it of errors and invalid data points and entries.
How Raw Data Is Processed in 5 Steps
Data analysts, business intelligence tools, and sometimes artificial intelligence (AI) applications, all work together in order to transform raw data into processed and insightful data.
1. Preparing the Data
After acquiring the data through the various collection methods available, you’d then need to prepare it for processing. That’s because raw data, on its own, is considered “dirty,” carrying lots of errors and invalid values. Not to mention, the lack of a homogeneous structure and unification of formats and measuring units, especially if the data comes from a variety of sources or regions.
During data preparation, the data is cleaned, sorted, and filtered according to standard in order to eliminate unnecessary, redundant, or inaccurate data. This step is absolutely essential to ensure high-quality and reliable results from analysis and processing. After all, the results can only be as good and as accurate as the data being fed into the processing tools.
The cleaning step can be simplified or accelerated by using more reliable tools when gathering the data.
2. Inputting the Data
Data inputting, sometimes referred to as data translation, is a step that converts the data into a form that’s machine-readable depending on the tools and software that will, later on, be used in the analysis process.
In the case of digitally collected data, this step is minimal. Though, some structuring and changing of file format might be needed. However, for handwritten surveys, audio recordings, and video clips, it’s important to either manually or digitally extract the data into a form the processing software is capable of understanding.
3. Processing the Data
During this stage, the previously prepared and inputted raw data goes through a number of machine learning and AI-powered statistical data analysis algorithms. Those are responsible for interpreting the data in its raw form into insights and information by searching input for trends, patterns, anomalies, and relationships between the various elements.
This step of the process varies greatly depending on the type of data being processed, whether it comes from an online database, user submissions, system logs, or data lakes. Data scientists and analysts who are well familiar with the data itself and the type of information the organization is looking to extract are capable of fine-tuning and configuring the analysis software as needed.
4. Producing the Output
At this stage, the raw data has been fully transformed into usable and insightful data. It’s translated into a more human-friendly language and can be represented as diagrams, graphs, tables, vector files, or plain text.
This makes it possible to be used in presentations where shareholders and executives with little to no technical skills are able to fully comprehend it.
5. Storing the Data
The results produced by the analysis process should be stored in a safe and accessible location for later use. This is because even processed data can be further analyzed for more details by focusing on a certain area.
This step is critical if the data contains sensitive company information or user data and information. The storage quality needs to be on par with the rest of the company’s data and information, and it must abide by local and applicable data privacy and security laws, such as the GDPR and the CCPA.
Types of Data Processing
There are many data processing methods that can be used depending on the source of the raw data and what it is needed for. The following are six of the various processing types to choose from.
Real-time Data Processing
Real-time data processing allows organizations to extract and output from inputted data in a matter of seconds. This type is best suited for a continuous stream of data rather than an entire database.
Real-time data processing is used most in financial transactions and GPS (global positioning system) tracking.
Batch Data Processing
Batch processing handles data in chunks. The data is collected over a relatively short period of time ranging from daily analysis to weekly, monthly, and quarterly. The result is more accurate than real-time processing, and it’s capable of handling larger quantities of data. That said, it takes more time and is generally more complex to accomplish.
Batch data processing is used in employees’ payroll systems as well as in analyzing short-term sales figures.
Multi-processing
Multi-processing is a time-efficient approach to data processing, in which a single dataset is broken down into multiple parts and analyzed simultaneously using two or more CPUs (central processing units) within a computer system. This type is used for large quantities of raw data that would take an exceptionally long duration to analyze without parallel processing.
Multi-processing is most often used in training machine learning and AI models and in weather-forecasting data.
Distributed Data Processing
Distributed data processing (DDP) is an approach that breaks down datasets too large to be stored on a single machine and distributes them across multiple servers. Using this technique, a single task is shared among multiple computer devices, taking less time to complete and reducing costs for data-reliant businesses.
Thanks to its high fault tolerance, DDP is great for processing raw data from telecommunications networks, peer-to-peer networks, and online banking systems.
Time-sharing Data Processing
Time-sharing data processing allows multiple users and programs to utilize access to the same large-scale CPU unit. This allocation of computer resources allows for the processing of multiple different datasets simultaneously using the same hardware resources.
Time-sharing data processing is mainly used with centralized systems that handle the input and requests of users from multiple endpoints.
Transaction Data Processing
Transaction data processing is used for processing a steady stream of incoming data and sending it back without interruptions. Considering it’s resource-intensive, it’s mostly used on larger server computers responsible for interactive applications.
8 Examples of Raw Data
Raw data is a term that applies to a wide variety of data types. The only criteria for this label are for the data to be in its most crude form and haven’t been under any form of cleaning or processing.
In fact, raw data is more common than you might think, as it allows the utmost freedom and control over the information derived from the database. It can be divided into two categories, quantitative and qualitative data, depending on the values they measure.
Quantitative Raw Data
Quantitative data is raw data that consists of countable data, where each data point has a unique numerical value. This type of data is best used for mathematical calculations and technical statistical analysis.
Some examples of quantitative raw data include:
Customer Information
As long as answers are collected in numerical values or through predetermined multi-choice questions with no room for free answers, this is considered quantitative data. This includes data such as height, age, weight, residential postal code, and level of education.
Sales Records
Records detailing the quantity and frequency of sales of specific goods and services are considered quantifiable data. This can help to determine which variety of products is more popular with customers and at which time of the year.
Combined with customer information, you can even process for more targeted results, such as discovering which particular demographic of customers are most likely to purchase which offering.
Employee Performance
Data on employee performance can include working hours, overall productivity, quality of produced work, and compensation. It can help to calculate the return on investment of your company’s overall staff members, determining whether they’re bringing more financial value than they’re getting paid.
The various metrics, whether submitted through digital or paper surveys by the employees or collected through the internal network and activity monitoring software are quantifiable data.
Revenue and Expenses
Revenue and expenses are strictly quantitative values for a company. Using revenue and expenses data can involve tracking financial activity within an organization, including revenue coming from sold goods and services as well as acquired capital in investment, and comparing it against the expenses of the given period.
This raw data is used to produce the net revenue, which can then be further analyzed to determine which areas of the company have acceptable or unacceptable levels of return on investment.
Qualitative Raw Data
Qualitative data is data that can be recorded and observed in a non-quantifiable and non-numerical nature. It rarely includes numbers and is usually extracted from answers that vary per participant through audio and video recordings, and even one-on-one interviews.
Some examples of qualitative raw data include:
Open-Ended Responses on a Survey
In open-ended survey questions, the respondents are free to structure their own answers instead of choosing one of the predetermined responses. The data cannot be lumped together when it’s raw the same way numbers can be, but it offers a more authentic and insightful view of the thoughts and opinions of the survey takers.
Photographs
While photographs can be categorized in countless ways, there’s a lot of overlap that prevents the use of quantitative measuring methodologies. When training machine learning models for computer vision capabilities, working with raw photographic data is essential.
Customer Reviews
While the 5-star or 10-star rating of a product or service is quantitative data, the reviews left by the customers aren’t. The responses would need to be analyzed on a scale of positive to negative, and highlight the suggestions and pain points experienced by each customer.
News Reports and Public Opinion
Collecting data from news reports and articles that include the name of your company can be a great way to gain an understanding of public opinion. This data is, however, qualitative and cannot be immediately separated into positive and negative coverage, along with the details of praise and criticism mentioned without cleaning and processing the dataset.
Why Is Raw Data Valuable?
Having access to high-quality and reliable raw data serves several purposes, particularly in the realm of business intelligence. It allows experts the chance to access key statistical and predictive analytics to help shape decision-making.
Despite being an experience of trial and error, where not every processing attempt of raw data will result in actionable insights and information, companies can still try to regain and retain as much information as possible from the raw data they input into processing tools.
Some reasons why businesses heavily rely on in-house collected and outsourced raw data sources may include:
- Starting Point:Â Raw data is the initial source for all data-based decisions on the executive level. It permits you to create compelling charts and graphs of overarching analytical statements about the conditions of the company and anticipated future affairs.
- Data Integrity: Because raw data hasn’t been cleaned, processed, or altered, you can trust that no part of it has been subject to removal or adjustment. This, in return, guarantees more accurate results that haven’t been touched by humans or machines.
- Compatibility With Machine Learning: Machine learning and AI algorithms are incapable of analyzing data after it’s been processed and translated into more human-friendly languages. Datasets are only legible for intelligent models if they’re raw and unaltered.
- Backup Resource:Â With access to raw data, you can always check your work against it post-processing in case you run into a problem and need to measure findings against the data in its original state.
Raw Data in Business Intelligence
Business intelligence is an overarching concept that combines multiple practices to help better guide the processes of business decision-making through data-based insights and information. It covers business analytics, data visual representation, and data mining, in addition to database management systems and tools.
Raw data is critical in business intelligence, as it offers a reliable source of information. That’s especially important for data-reliant businesses such as healthcare, retail, and manufacturing.
Without accessible raw data, companies are confined to whatever format processed data comes in, and there’s always the risk the data has been processed in error or is misaligned with strategy.
“Any industry has the chance to drive innovation by transforming raw data into gold—if they have the digital tools to do it,” said Ben Gitenstein, vice president of product management at Qumulo and member of the Forbes Technology Council. “File data is growing exponentially, and it’s become increasingly challenging for organizations to manage.
“Retailers are manufacturers in the traditional sense, but they’ve managed to leverage the raw data they have to also become digital manufacturers, updating their services for customers through personalized shopping recommendations and improved supply chains.”
Improving Customer Satisfaction With Insights From Raw Data
Up-to-date raw data is essential in all industries, but especially in fields where the company is capable of further optimizing operations for more profit, fewer costs, and higher levels of customer satisfaction.
You can source data internally by asking existing customers to take a short survey rating their experience with the services or goods your company offers.
Alternatively, you can outsource the work to a data collection company that would target a specific demographic. Either way, raw data that’s specific to your work model and isn’t derived from a large-scale generic database available for free online or prepackaged for sale is the only way to gain direct insights into the opinions and suggestions of customers and clients.
Also, because the data is raw and hasn’t been processed, you can run it through a larger number of processing methodologies and tools to get varying results and standardize your tests. The larger a data sample is and the more expert-level analysis you run, the more familiar you can become with your customers and clients, and shift your business to meet their demands and requests.
Building Valuable Insights Starting With Raw Data
Raw data is data that hasn’t been cleaned, organized, or processed in any capacity. While it can’t directly output information and insights as it is, running it through multiple processing stages can refine it up to a point where insightful graphs, diagrams, and tables can become comprehensible for the average data analyst.
Making use of accurate and up-to-date raw data can be incredibly beneficial, from prompting a data-backed decision-making process and offering unique insights on the inner workings of a system or demographic to its ability to improve the trust of both customers and shareholders.
