Big Data Analytics: Why Data Quality Is Important?

Big Data can be leveraged to generate business insights and allows business leaders to make data-driven decisions based on such insights - revealing hidden trends and patterns that are not easily recognized by using traditional analytics methods. However, like any other digital or IT solution, Big Data also comes with challenges, including Data Quality, which is essential to ensure the success of big data analytics projects.

In fact, many businesses understand these problems and are taking measures to combat such quality problems - in order to extract the maximum benefits out of their data assets. In this article, let's explore some of the important data quality characteristics and the challenges involved to clean those data to meet the quality standards.

Big Data Quality

Generally speaking, different analytics projects would have different requirements in terms of data quality. For example, if a retail or eCommerce company collects business data to analyze consumers’ activities on their website, they would like to have an overview of the big picture, specifically their business performance. Here, the retail company does not need to have 100%-accurate visitor activity records for analysis to understand the current state of business. (This is also not exactly achievable.) 

However, if a healthcare institute or hospital applies advanced analytics to monitor and understand their patient-specific health status or well-being, an accuracy of 98% or above could be required. 

Thus, the requirements of data quality may vary and actually depend on the company project’s needs or industry standards. Business organizations need to have a realistic understanding instead of rushing to have their data at the highest level of quality possible. The first correct step is to analyze your organization's big data quality requirements and establish the level of quality standards on your big data.

What exactly is good data quality?

To determine the quality of a given data set, organizations need to understand the criteria and data quality characteristics in order to distinguish whether such a data set is suitable for the projects. 

There are quite a number of data quality criteria. The five basic traits, including accuracy, completeness, reliability, relevance, and timeliness.

In terms of big data quality only, it should be noted that not all these criteria are applicable to big data projects and none of them are accomplished 100% of the time.

For starters, a big data project would allow a certain degree of noise which impacts the “consistency” criteria to reach 100% satisfaction. The structure of big data, and in massive volume, would make it a challenge to delete all of the data set that does not meet the consistency level. 

Still, the logical relations within your big data are still required in different cases. For instance, in the BFSI industry, big data can be used to monitor and automatically detect potential frauds like credit card frauds. In order to do so, the analytics solution may need to collect different data set about customers, and require a specific measure of consistency level. In some other cases, the level of consistency might not be too strict - especially on a large scale, where it won't affect the analytics results much.

In terms of completeness and accuracy, as stated above, the level may vary case by case, with no strict level of acceptable standards. For instance, a marketing campaign for an eCommerce company might run some trend analytics reports based on some missing data due to a server outage issue that lasted for a few minutes or hours. This is still acceptable since businesses are still able to calculate the trends based on longer time-frame monthly or quarterly data - the big picture analysis result is still sufficient, even without the missing data. Yet, in a situation like healthcare analytics, the level of accuracy is more serious and in-depth historical data is a must because a small margin of error could actually lead to serious consequences.

What is acceptable then?

Again, the big data quality will come down to the specific requirements of the business and the project itself. None of the criteria should be strictly applied to all cases and should be considered separately, on a project-by-project basis. Trying to chase the perfect data quality standard may be very costly and time-intensive as well as quite impossible to accomplish.

As a result, it's important to assume what is acceptable and good enough instead. This means organizations can establish a minimum acceptable threshold of data quality - which can provide them with satisfactory analytics results, and improve the data quality gradually, staying above the level.