How to maintain high data quality and consistency

To ensure you have the best data analysis, you have to ensure data quality, most businesses fail in doing so.

You can invest in the latest big data tools and adopt the best practices but it wouldn’t matter if you were plagued by low data quality. Data analytics tools help analyse data and churn out meaningful insights, but the findings are dependent on the quality and consistency of data. If the data quality is poor, then the findings will be poor or inaccurate. Hence, organisations owe it to themselves to ensure that data is of high quality. In this blog post, I explain some tips and practices to ensure high data quality so you get the most accurate findings possible.

How to maintain high data quality and consistency?

Spend time understanding the origin of data

In my experience, there is a temptation for many organisations to take their data, run an analysis and walk away with the findings. That is a mistake. Before analysing data, it is important to understand where the data originated from. Understanding the origins of data prevents any errors and misunderstandings when you reach the analysis stage.

Understand the margin of error

The margin of error will vary depending on the volume of data collected. In my experience, the larger the volume of data, the larger the margin of error is. Data, despite its immense value, is not perfect and being aware of this will help you avoid pitfalls, address problems quickly and build on successes. The best way to improve data quality and not let it stagnate is to understand the margin of error before analysing it.

Set metadata measurements

When you draw data from different sources and it proliferates throughout the organisation, different departments will misinterpret the data. You can prevent data from being misinterpreted by implementing metadata measurements. In case you are not aware, metadata refers to information on data. The purpose of metadata is to provide a set of standards so that there are no misunderstandings in interpreting data.

Avoid bias

In my experience, cognitive bias against data is one of the most common malpractices that affect data quality. Many data analysts have a tendency to pick out and eliminate data based on their own opinions, instead of adopting a more objective basis of thinking. This cherry picking of data does not improve the quality of reporting but hinders it. It is difficult for us to identify the most important factors in a data report, hence, you should avoid making assumptions about what is important and what is not if we want high data quality and consistency.
Have an eye for detail

The smallest detail can lead to major inaccuracies, affecting data quality. It is important to have an eye for detail and avoid minor inaccuracies in your data. Data analysts should have to possess the skills to identify these mistakes. These problems include gaps in the data, sparse information and duplicate data entries. Data analysts should ideally focus on constantly improving and fine-tuning their reports to give executives the clearest picture possible for data entries.

Know that one size does not fit all

There is no one size fit all policy for data quality. Data comes from different sources, therefore, not all forms of data share the same metrics nor do they share the same standards of quality. For example, social media data is only 80% accurate, which is enough for sentimental analysis. However, 80% accuracy rating is not sufficient for other industries, like banking. Hence, it has to be refined further before analysis.

Hence, this falls to data analysts to decide how accurate data should be before processing. Keep in mind that not everything has to be 100% because the effort to improve accuracy is not worth the returns. For example, an increase from 98% to 99% will not make a significant change in business outcomes and may not be worth the effort.

Maintaining high-quality data

Data analytics tools have a little positive effect on business outcomes if data quality is poor. As a result of this, it makes sense for organisations to put time and effort into cleaning and sharpening their data so that quality is high. Maintaining quality data comes down to several factors, like setting metadata measurements and understanding the origin of data. Ensuring high data quality guarantees excellent business outcomes and prevents any errors in decision making.


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