Raw data, often referred to as source or primary data, is data that has not yet been processed, coded, formatted, or analyzed. While raw data is a valuable resource, it can be challenging to work with or understand as it’s visually cluttered and can lack cohesion. Organizations can collect and use raw data to learn more about customers, sales, the success of marketing campaigns, and other useful targets, but first they need to structure and organize the data into a form that’s easier to read and visualize.
Enterprises that actively employ data for analysis, decision-making, or reporting must understand how raw data works and how it fits into a larger overall data strategy.
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How is Raw Data Used?
Raw data is data collected from one or multiple sources that remains in its unaltered initial state. At this point, the data might contain human, machine, or instrumental errors—depending on the collection method—or it could lack validation. Once the data is changed in any way to improve its quality, the data has been processed and is no longer considered raw.
Raw data has infinite potential as a resource, as it comes in a variety of forms from a wide range of sources. Collecting raw data is the first step toward gaining a more thorough understanding of a demographic, system, concept, or environment. Business intelligence analysts can extract useful and accurate information about the condition of their business from raw data—for example, audience interest, sales figures, marketing campaign performance, and overall productivity.
Raw Data Collection Steps
How raw data is collected plays a key role in its quality and future potential. Accuracy, credibility, and validity can mean the difference between a database of raw data that’s a wealth of information and insights, and unactionable data that simply takes up space. The following steps lay out a path for data collection to help ensure it meets the organization’s needs.
Define the information you want to extract to lay the groundwork for your raw data-gathering goals. For example, if the desired data is user-base and customer information, online and in-person surveys focused on a specific age and geographical demographic can be used to gather it.
Other types of raw data may require advance planning. 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.
Choosing a Collection Method
Choosing the appropriate raw data collection method 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—manual collection can introduce variables that leave room for interpretation, such as illegible handwriting or hard-to-understand accents in audio or video recordings.
Raw data tends to be large in volume and highly complex. During the collection process, the overall volume of data is only an estimate—once you process the data by cleaning it of errors and invalid data points, you’ll have a more accurate sense of scope.
How Raw Data is Processed in 5 Steps
Analysts well-versed in data trends and patterns work with modern business intelligence tools and, on occasion, incorporate artificial intelligence (AI) applications to transform raw data into insightful knowledge using the following steps:
1. Data Preparation
Raw data, by definition, is faulty and may contain errors—it might also lack consistency in structure and format, especially when it’s been obtained from different sources. During data preparation, data is thoroughly cleaned, sorted, and filtered following predefined standards to ensure high-quality, consistent outcomes.
2. Data Input
Inputting data—sometimes referred to as data translation—involves converting raw data into a machine-readable form. The specific process will vary depending upon the tools and software that will be used for analysis.
In the case of digitally collected data, this step is minimal—some structuring and changing of file format might be needed. But for handwritten surveys, audio recordings, and video clips, data will need to be manually or digitally extracted into a form the processing software is capable of understanding.
3. Data Processing and Analysis
Raw data is processed and analyzed for insights by searching for trends, patterns, anomalies, and relationships between the various elements. This process varies depending on the source, and can be done manually or using artificial intelligence and machine learning.
4. Data Output
Once the raw data has been fully transformed into usable and insightful data, it’s translated into human-friendly form—diagrams, graphs, tables, vector files, or plain text, for example. This step makes the data usable and actionable.
5. Data Storage
Data storage is essential, as the processed data may be subjected to future analysis for additional insights—but it becomes even more critical when dealing with sensitive corporate information or user data. Storage quality should be consistent with the overall standards of the company’s data and information, while also complying with any local data privacy and security legislation, such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA).
9 Types of Data Processing
Choosing the best approach to handle raw data is critical for effective data management. It will depend upon the type and volume of data and the pace of collection, among other concerns. Choosing the correct approach that is consistent with what the business wants to achieve not only improves workflow but also helps gain the most value from data, enabling smart choices and strategic insights.
Here are the nine most common types of data processing:
- Batch Processing—A set or batch of data is processed in bulk at regular intervals. This method is suitable for operations that do not require instant replies, such as payroll processing, when efficiency trumps real-time engagement.
- Real-Time Processing—Data is processed as soon as it is created or received, ensuring quick reactions. Commonly used in instances when rapid decision-making is essential, such as financial transactions or monitoring systems.
- Online Processing—Similar to real-time processing in that it handles data as it is entered or requested, as seen in interactive systems such as online databases. Allows for quick data retrieval and updating in response to changing user requirements.
- Distributed Processing—Spreads processing work across interconnected computers to improve overall system efficiency and performance. Frequently used in large-scale data processing applications where centralized processing is unfeasible.
- Parallel Processing—Processes many jobs or programs at the same time using multiple processors, enhancing processing speed for difficult calculations. Ideal for jobs that can be broken down into parallelizable subtasks.
- Multi-Processing—Performs many processes or applications at the same time on a computer with multiple processors. By processing several jobs concurrently, improves overall system speed and throughput.
- Transaction Processing—Individual transactions or company processes are processed in real-time. In systems such as online banking, where the rapid and correct processing of financial transactions is critical for ensuring data integrity, this is essential.
- Manual Data Processing—Involves human interaction in data processing in the absence of automated technologies. This might include procedures like manually inputting data into a system, which is inefficient yet may be required for particular jobs or data kinds.
- EDP (Electronic Data Processing)—Data processing and analysis using electronic devices and computers. Encompasses a wide range of automated operations, ranging from simple data input to complicated computations, that are routinely employed in current data processing applications across a variety of sectors.
2 Types Of Raw Data and Their Examples
Raw data includes a wide range of data types, which are typically classified as either qualitative or quantitative. The most important condition for this categorization is that the data is not cleaned or processed in any way. Raw data allows unrivaled flexibility and control over the information collected from the database.
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. Here are some common examples:
|Enables targeted results and demographic insights when combined with other data.
|Quantifiable data on number and frequency of goods and services sales. Identifies popular products and optimal sales times. Combined with customer info, provides insights into customer demographics and preferences.
|Quantifiable data on working hours, productivity, quality, and compensation. Collected through surveys or internal monitoring software. Assists in calculating staff return on investment.
|Revenue and expenses
|Strictly quantitative data tracking financial activity, including revenue and expenses. Used to calculate net revenue and analyze return on investment in different areas of the company.
Qualitative Raw Data
Qualitative data 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 for each participant through audio and video recordings or one-on-one interviews. Here are some common examples:
|Open-ended response on a survey
|Open-ended survey questions with unstructured responses provide real insights into respondents’ ideas and opinions. It cannot be easily aggregated like quantitative data, but it does give a true viewpoint.
|Photographs and videos
|Categorizing photos and videos is complex due to overlap, but raw data is crucial for training machine learning models in computer vision, especially for surveillance and visual scenario analysis.
|Star ratings are quantitative, while reviews are qualitative, assessing responses on a positive to negative scale, and highlighting customer suggestions and pain points.
|News reports and public opinion
|The data from news reports and articles about your company provides valuable insights into public opinion but requires processing to distinguish positive and negative coverage.
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. There are a number of ways for enterprises to use raw data to learn more about their customers and improve those relationships.
Using Customer Surveys to Collect Internal Data
Internal initiatives that actively request client input are one approach to acquiring raw data. This comprises engaging current customers to take part in quick surveys that provide important information about their contacts with the company’s services or products.
Outsourcing Data Collection
Organizations can enhance the raw data with a wider range of insights by actively outsourcing data-collecting operations to specialist organizations that target certain populations. This active approach ensures a diversified variety of ideas and opinions from an outside perspective.
Incorporating Diverse Processing and Expert Analysis
Because of the unprocessed nature of the data, a wide range of processing approaches and tools can be used. This adaptability enables businesses to react efficiently to the demands and requests of consumers and clients by actively increasing familiarity through larger data samples and expert-level analysis.
Bottom Line: 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 be used directly to generate information and insights as it is, it can be processed and refined to make it actionable. Making use of accurate and up-to-date raw data can fuel data-backed decision-making and provide unique insights that can benefit organizations in many ways.
Read Data Management: Types and Challenges to learn more about how enterprise organizations work with the vast stores of data they rely upon for decision making, customer insights, and business intelligence.
This article updates an earlier article by Anina Ot.