If you have been part of corporate meetings, you might have heard the sentence, ‘this data is bad.’ Alternatively, some high-ranking executive might have stated, ‘that data is irrelevant or not up to date.’
While businesses have started to acknowledge and understand why data is important, they have far less clue about maintaining the quality of data. In this article, we will focus on how and why business organizations should focus on data quality.
However, before we begin, let us first look at what exactly does ‘Data Quality’ stand for.
Data Quality: Definition
In the words of gartner mdm magic quadrant, data is said to be of a high quality if it satisfies five basic criteria-
- Updated or Timeliness
- Consistent and Cross Reference-able
- Relevant and Goal Oriented
- Accurate to the ‘T’
Any data set, which fulfils these five basic criteria, is termed high quality. When a data set is able to accomplish the end for which it was needed, it is beneficial for everyone. Such high quality data helps the organization, its different verticals, processes and outcomes.
Ways to Maintain Data Quality in a Business Organization
- Inspection of Data incoming into the organization-
Most businesses complain that data is of a poor quality, because it was identified at the nascent stage. If you are able to check, control and monitor the inflow of data flowing into the organization, you will eliminate a major problem.
Most data flows from other organizations or third party vendors. You need to check whether it is credible by testing a small subset, every now and then. The best companies use data profiling tools to help them in this regard.
- Check for Duplication of Data across different departments-
Apart from checking the inflow of data from external sources, you need to ensure that your internal data is not being duplicated. Experts suggest creating a pipeline that helps in preventing data duplication.
In addition to creating pipelines with the right stakeholders, effective communication is critical. If you avoid this problem, which naturally creeps in, you can build efficiency and reduce wastages.
- Narrow down the Goals of the Organization and then generate data-
Business organizations should first list out their requirements for the data. What they hope to achieve is the basic question to be asked. This should be followed by generating the data that will help lead to the fulfilment of that goal.
Businesses, who complain or poor data quality tend to do the opposite. As they value data over everything else, they first generate the data and start fixing their goals based on what they found and did not find. This is where the quality of data sits at its possible worst.
- Tracing Data and Maintaining Data Integrity-
If you have the best data sets at your disposal, you will be able to trace the exact points of origin. You will also be able to cross-reference the same with ease at all times. Data, which cannot be traced is of a very low quality and should be avoided at all times.
In addition, every organization should ensure that too many people are not handling data at the same time. By keeping the circle small, you reduce the chances of tampering, with intent or just because of carelessness.
- Set up the right Data Quality Teams-
It is always advisable to set up an internal data quality team to help the organization. The technical knowledge and experience of the team makes it handy when it comes to filtering, checks and processing.
You cannot expect your Marketing Head to be a Data Analyst who can use data profiling tools. In such an event, having a data quality team helps in maintaining the quality of data in a major way. It also streamlines processes and cuts down on wastages.
Ensuring data quality from the very outset is very important. This is because actions around data build up from the start. In other words, as soon as the generation starts, other actors, processes and tools start working in the background.
By following the article, you will be able to ensure that your organization’s data is of a high quality. Do you think the importance attached to data is overrated or underrated?
Let us know in the comments section below.