What is Raw Data in Business Intelligence?
The data which keeps track of an organization's everyday transactions is the raw material in business intelligence.
The data which keeps track of an organization’s everyday transactions is the raw material in business intelligence. This raw data in business intelligence can originate from a range of sources, including contacts with customers, administrative work, operation management, and financial administration.
But before tackling the fundamentals of raw data in business intelligence, it may be a good idea to know what business intelligence is.
Essentially, business intelligence refers to a technological term that involves certain processes, including data preparation, data management, and data visualization. It enables users to obtain information from raw data which is useful in making data-driven decisions within the organization.
Generally, many business intelligence tools are available to handle certain data processes to analyze performance metrics and gather business insights in real-time. They’re also designed to help businesses minimize IT dependencies by identifying performance gaps, new business opportunities, and the latest market trends. For example, web scraping has been one of the important tools in business intelligence. It refers to the process of getting data from websites to obtain various information for a business.
Whether they want to know what their customers think about their brand, determine who their competitors are, or identify differences in pricing, these BI tools may provide you with useful insights from certain raw data. Thankfully, there are many web scraper tools in the market to choose from. But if you want to pick the right one, it may be best to do some research by getting information from reliable websites like the WP Dev Shed and other similar options.
Primary Transactional Databases for Raw Data in Business Intelligence:
As per the traditional model, the data gathered by everyday transactions is captured in three primary transactional databases:
- Customer Relationship Management
- Human Resource Management
- Enterprise Resource Planning
Any piece of data is inherently neutral, meaning it is not considered to be either “good” or “bad.” Take this as an example, if you knew that rep X had gotten Y dollars in orders this year, you would have no clue whether to be concerned or ecstatic.
Data, like raw materials, must be treated through analysis to be valid. However, considering the abovementioned example, when compared against rep X’s year-to-date sales target, the same set of information becomes meaningful. As a result, that piece of data has now become a part of the whole analytic process.
Data preparation for Business Intelligence (BI) can be a time-consuming and challenging procedure. You want to get the raw data in business intelligence to be transformed into the most valuable reports for assessment and analysis. However, before you can even begin to approach the findings, you must first analyze and handle the raw data. Furthermore, it is critical to ensure that data is captured and shared throughout the firm. This is referred to as the “democratization of analytics” by Gartner.
Steps to Prepare Raw Data in Business Intelligence:
There are various phases to go from raw data to relevant analytics to achieve the desired results:
- Data collection and loading
Collecting data includes putting it into a data warehouse, such as Redshift, so that you can take advantage of its relational database features and capabilities.
- Make data BI-ready by transforming it.
The best approach to begin this stage is to investigate the loaded raw data in business intelligence using manual queries. You can then assess the data’s quality and determine whether tables are no longer relevant or need to be updated. Then, in accordance, plan and decide on the appropriate transformations.
- Manual queries are used to test the system.
Experiment with alternative manual queries to see if you can get the same outcome. You can alternatively manually count the result and compare it to the effect produced from the transformation in this stage, or you can pull the results data into a spreadsheet (a sample of the data should suffice).
- Create the reports.
Create end-user reports and charts with the appropriate level of detail and resolution, such as DAU per device, nation, and so on.
The Worth of Raw Data
Data has the most value once it has been processed and evaluated. There isn’t much point in storing raw data if you can’t utilize it. Still, as storage costs fall, enterprises are seeing more and more value in gathering essential data for further processing — if not right immediately, then later.