Introduction
Business leaders in Fortune 500 companies, as well as start-up entrepreneurs are now swearing by the advantages of Big Data. In business circles, you do not need to convince anymore about why data is important, or what are some of the major benefits of data.
The discussions and conversations have now veered towards how to ensure data is of a high quality at all times. In other words, ‘Data Quality Management’ has emerged as a hot topic in business circles.
Data Quality Management or DQM is the process of maintaining the strictest possible high standards when it comes to-
- Collecting or acquiring data from different verticals within the organization.
- Storing data in a safe and secure fashion away from prying eyes.
- Processing and analysing data according to the business goal in mind.
- Executing steps and implementing strategies based on data.
In the following sections, we will discuss why businesses are investing and focussing on data quality management unlike ever before.
What are the fallouts of poor Data Quality Management?
According to a study by Gartner, an average business suffers from an estimated loss of nearly $15 Million USD because of poor quality data every single year!
This means that many businesses are still not aware of how to optimize data in the first place. In many situations, businesses do not even know where to look for in order to compensate for the losses.
If you look at the four points, we had mentioned while discussing DQM, you must notice a common thread. In other words, all the four points are sequential and are as important in their own right as the last one.
Any problem in one of the points (areas) can lead to the other points suffering on a massive scale. The final output: the data is- inaccurate, faulty and will lead to poor decision-making based on uninformed data!
How to Define Data Quality in the right fashion?
Many experts have suggested that in order for data to work, it needs to follow a set of ethical and professional standards. The important thing to note is that there is no elbowroom when you are managing data. One false entry can lead to catastrophic results.
It should also be pointed out that the rules and norms for data quality are different for different industries. However, we have gone ahead and laid out some principles, which we feel, are the foundation blocks for data quality, irrespective of the business or industry niche-
- The Integrity of the Data-
Every data, which is brought into the system, needs to be true at all levels. Employees and team members should be made to understand that real data numbers could only promote the best decisions going forward.
- The Accuracy of the Data-
This is related to the last point, but also includes avoiding carelessness, crosschecking and verifying data from the different verticals in an organization. If the data is not accurate, it is never going to yield the desired results.
- The Validity of the Data-
Time is a crucial element when it comes to data quality management. Data sets or actions derived from DQM cannot be applicable indefinitely. If your data pertains to the last quarter, you will be able to take actions based on that in this one.
- The Completeness of the Data-
When a Data Analyst processes and analyses data, he or she looks for complete data sets. You were absent on a specific date and have not entered the data for the date, will make the data quality suffer in the worst manner possible.
Conclusion
Businesses are rethinking their approaches, strategies and growth projections by analysing data. This is why trimming, pruning, extrapolating the right data and ensuring data quality management has become necessary for almost all organizations.
In very simple terms, data shows that path the business should take, and shows the areas, a business should avoid. While this understanding of data might be too simplistic, it really demonstrates the efficacy and effectiveness of data to any business organization.
