Top 4 Tips for Improving Data Quality

Becoming data-driven is the trend everyone subscribes to nowadays. But other than being the cool thing to talk about, data is paramount to your company's success. Data quality impacts your bottom line.

The quality of your analyses suffers, and your decision-making falters when you work with low-quality data. The cost of acting on flawed data can be staggeringly high according to the 1-101-100 rule. Suppose you are verifying data as you enter it, and this costs you $1. Then, correcting inaccurate data after the fact will cost $10, and not correcting the data at all will set you back $100. Therefore, being proactive in maintaining data quality makes much more financial sense than relying on correction mechanisms after the fact or accepting failure.

When you take the "correction" path and focus on fixing data errors after they occur, a few things happen: You lose time, incur high processing costs for the same job, and risk tarnishing your brand image. When you choose to do nothing at all in the face of poor data quality, then you basically accept to live with bad analyses and wrong decisions and guarantee that your reputation will be ruined in the long run.

So, being proactive in ensuring data quality data makes sense for many reasons. However, a single individual or a department cannot improve data quality on their own. This task cannot be left to the IT department. It takes a holistic, strategic view that encompasses every phase of the data lifecycle to improve the quality of the data. Here is how you should go about it:

Starting off: Plan, observe, measure

You should define what success means at the outset. Determine the metrics you will use to measure data quality. Show your employees what high-quality data will look like. Map out how your organization is expected to achieve those metrics.

A good starting point would be to take an inventory of all the locations where data is stored within the organization. This catalog of data assets should identify all the sources and point out all the data lineages, underlining how data was aggregated, manipulated, or transformed.

Conduct a data audit (check out the Friday Afternoon Measurement, it offers a good template for the uninitiated). Bring together the last 100 records processed in your department or company. Identify a set of critical data attributes you need to focus on, evaluate each record for data errors, and determine the error-free ones. The number of perfect data records will be your Data Quality Score. This score will be a yardstick you will use to measure performance when you repeat the audit after you start implementing data governance policies.

Eliminate data silos

Pay attention to data silos. A data silo is a repository of data accessible to a business unit but isolated from the rest of the organization. Most of the time, this data is stashed away somewhere in the organization, and most employees are not even aware of its presence. The possibility that there might be a pile of inaccurate, inconsistent, incomplete, obsolete, and redundant data which does not conform to predefined formats is enough to give any IT manager nightmares.

Data silos are caused not only by the organization's verticals but also by every single application used by the employees. Each application functions as a data silo unless it is integrated with others. The situation may be direr than any of us could have imagined. A recent Mulesoft report prepared in collaboration with Deloitte Digital found that organizations have an average of 976 discrete applications in 2022, an increase of 16 percent over the 2021 figure. The same report pointed out that only 28 percent of those applications were integrated. So, around 700 applications work on their own, serving as bonafide silos.

High-quality data has to be consistent and unique, as we touched upon during our discussion of the dimensions of data quality. Data entries stored in different locations should match, and there should be no duplication or redundancy in what a data entry corresponds to. The mere presence of data silos breeds inconsistent and duplicate data. The elimination of data silos has a direct impact on data quality.

Establish a data governance culture

Of all the definitions of data governance being thrown around, this one makes the most sense: A set of principles and processes that ensure high data quality throughout the data lifecycle.

However, like all policies that set out with lofty goals, data governance, too, needs to be connected to the realities of the field if it is to be successful. That's why you should make sure that the data governance policies and measures you introduced to raise data quality align with business operations. Employees will shun your suggestions unless they see how improved data quality helps their work and have a positive impact on their day-to-day activities.

Establish linkages between the improvement recorded in data quality and the actual business KPIs. For example, if your organization has achieved a 20 percent reduction in invalid customer phone numbers that do not conform to the predetermined format and your NPS increased by 10 percent in the meantime, that connection deserves emphasis. That kind of tangible, meaningful connection between policies and outcomes will motivate your employees to embrace a data governance plan.

Go for quick wins

Establishing data governance is a long process during which lots of ideas will clash. There will be a lot of hearts and minds for you to win. That's why targeting the low-hanging fruit is key. Scoring quick wins in some areas will alleviate resistance and help promote new policies in the eyes of the stakeholders.

Final thoughts

Upholding data quality is an ongoing process, just like quality management in the manufacturing industry is a never-ending endeavor. Data governance should permeate every major decision taken at an organization. Are you planning to hire for a vacant position? Not everybody you hire will be a data scientist. But recruiting people who are data-focused or capable of understanding the implications of being data-driven goes a long way toward establishing the right culture. Organizations do not become data-driven without integrating this mindset into every aspect of business operations.