Data modelling for autonomous database is changing as a result of new opportunities for businesses to quickly access and evaluate their data to boost performance. Data modelling is more than just arranging data structures and relationships in an arbitrary manner. It also needs to address end-user requirements and inquiries and provide direction to guarantee that the appropriate data is used appropriately for the intended purposes. You may improve your data modelling and its worth to your company by using the strategies that are outlined below.
1. Recognize the Business Needs and Required Outcomes
The largest problem in data modelling is typically accurately documenting those business requirements so as to know which data to prioritize, gather, store, convert, and make available to users. It is imperative that you ask individuals about the outcomes they desire from the data in order to gain a clear knowledge of the requirements. After then, begin to arrange your data with those goals in mind.
2. Make the Data to Be Modelled Visible
It’s improbable that gazing at endless columns and rows of alphanumeric entries can lead to enlightenment. The majority of people are significantly more at ease with graphical data representations that allow them to quickly identify any irregularities or with user-friendly drag-and-drop screen interfaces that enable them to quickly examine and combine data tables. These kinds of data visualization techniques assist you in cleaning your data so that it is accurate, consistent, and devoid of errors and redundancies.
3. Begin with Basic Data Modelling and Expand Later
A number of factors, including size, type, structure, growth pace, and query language, can cause data to become complex very quickly. It is easy to fix any issues or incorrect turns when data models are started tiny and basic. You can add additional datasets and remove any discrepancies as you go, until you are confident that your basic models are relevant and accurate. It is best to seek for a tool that is simple to use at first, can handle very massive data models later on, and allows you to “mash-up” several data sources from various physical places rapidly. This is one among the data modelling best practices.
4. Divide Business Inquiries into Dimensions, Order, Facts, and Filters
It will be easier to arrange data and deliver responses if you know how these four factors might define business queries. Let’s say your business is a retail establishment with multiple locations. You would like to know which stores have sold the most of a particular product in the past year. You may make it easier to analyse your data and determine the top sales performers for each sales period as well as provide answers to other business intelligence queries by creating separate tables for facts and dimensions.
5. Utilize Only the Information You Require, Not All of the Information Available
Large dataset processing on computers can quickly lead to memory and input/output speed issues. To address business problems, however, just a tiny subset of the data is frequently required. Ideally, you should be able to avoid performance problems and wasteful data modelling by simply checking boxes on the screen to indicate which portions of datasets are to be used.
6. Do Preliminary Calculations to Avoid End User Conflicts
Establishing a single version of the truth that consumers can utilize to ask their business inquiries is one of the main objectives of data modelling. There should be consensus regarding the underlying data and the math used to arrive at the answer, even though different people may have different ideas about how it should be applied. To determine monthly figures, for instance, a calculation may be needed to total daily sales data. These figures can then be compared to indicate the best and worst months. By setting up this computation in advance as part of your data modelling and making it visible in the dashboard for end users, you can avoid problems rather than forcing everyone to go for their spreadsheet software or calculators (both of which are major causes of user mistake).
8. Seek Causation Rather Than Correlation
Guidelines for using the modelled data are part of data modelling. Although giving end users the ability to access business intelligence on their own is a positive move, it’s equally critical that they refrain from drawing incorrect conclusions. For instance, they might notice that the sales of two distinct products seem to increase and decrease simultaneously. Do sales of one product influence those of the other (cause and effect relationship) or do they simply increase and fall in tandem due to external factors like the weather or the economy? Here, confusing correlation and causation could result in focusing on the wrong or non-existent prospects, squandering financial resources for the company.
9. Let Intelligent Devices Handle the Heavy Lifting
In order to process data before analysis starts, more complex data modelling may call for coding or other procedures. But if a software program can handle this “heavy lifting,” you may free up your time to work on other productive tasks for your business rather than having to master many programming languages. All the processes of data extraction, transformation, and loading (ETL) can be facilitated or automated with the help of an appropriate software package. A straightforward drag-and-drop interface can be used to combine data sources, and depending on the type of query, data modelling can even be done automatically. All of this is possible without the need for code.
10. Allow Your Data Models to Change
Because data sources and business priorities are always changing, data models in business are never set in stone. As a result, you will need to prepare to update or modify them over time. To do this, keep your data models in a location that facilitates their growth and modification, and make use of a data dictionary, also known as a “ready reference,” that provides accurate and up-to-date information regarding the format and function of various data types.
