Whether you work in a technical team such as software development, or a non-technical team such as sales, marketing, accounting, medical, human resources, etc., there is a need for querying and analyzing data as data analytics plays an important role in every part of a business. Forbes Insights and KPMG report that “84% of CEOs are concerned about the quality of the data they’re basing their decisions on.”
There are different ways of storing data such as spreadsheets, relational databases, non-relational databases, etc. Among those methods, SQL, or Structured Querying Language is one of the most common languages we use for querying data from relational databases.
However, there are critical problems related to writing SQL statements.
- Changing queries or adding new ones for different scenarios is a database admin, software developer, or system engineer’s task, requires database and code changes. No configurable, composable query language for domain experts.
- Business users such as subject matter experts find it difficult to query databases without SQL knowledge and not every organization can have a technical person who is dedicated to retrieving data from a database whenever it is necessary.
- Incorrect queries retrieve incorrect data making errors in business rules and data-driven decisions. This can lead to an increase of operational costs, operational inefficiencies, reputational damage, missed opportunities, resulting loss of profits.
Gartner measures the average financial impact of poor data on businesses at $9.7 million per year.
Now let’s see an example from the insurance industry and how we address these challenges.
Query Claim History Database without SQL Knowledge
Consider a scenario of an underwriter of an insurance company having to investigate the claim history database to get data of the customers with unpaid claims in a certain year.
This is a sample SQL Query to retrieve name, contact details and claim amount of the customers with unpaid claims in 2021.
Here we have joined two tables and retrieved data. But in a real-world application, there can be multiple tables joins creating fairly complex SQL statements.
Therefore, using the FlexRule platform, we have created a decision table that only exposes the data that can be easily updated by a non-technical user.
In a separate boxed expressions document, we have mapped the columns of the table.
If you want to retrieve all the customers with unpaid claims, you can simply disable the specific row related to the claim year.
Or if you only want to retrieve the First Name and the mobile number, you can disable the rest of the rows.
The ease of retrieving good quality data enhances decision-making, efficiency, and compliance.
SQL queries are powerful, yet it can be complex for business users to query a database without SQL knowledge. Most importantly, writing an incorrect statement will retrieve incorrect data resulting in many errors in the decision-making process. Creating a decision table that exposes only the required data solves this problem as it is easy to understand and maintain. It reduces the errors by giving clear visibility of what data you are retrieving. In the end, giving the opportunity to the business users to access good quality data in a timely manner means better decisions and it opens better opportunities to the business.
Last updated July 8th, 2021 at 04:38 pm, Published June 24th, 2021 at 04:38 pm