Why data quality is essential to business success [Q&A]

Enterprises are increasingly reliant on data, but it's only really useful if its quality can be assured. With growing volumes though maintaining data quality and compliance is a big challenge.

We spoke to Rex Ahlstrom, chief strategy and technology officer at Syniti to find out how businesses can improve and maintain the quality of their data and how this can benefit them.

BN: What is the problem with achieving high-quality data to facilitate compliance and what challenges are organizations facing in this regard?

RA: Generally speaking, poor quality data is generated four primary ways and can affect how your company mitigates risk and ensures compliance standards are met.

• Human input error or lack of detail based on unknown requirements
• A machine, interface, or data migrations create or move the bad data
• Bad code and poor usage warps the data in a system
• Data that was once fit-for-purpose is no longer valid due to business changes

The challenge with achieving data quality is striking a balance between addressing these four causes of poor data and correcting the right ones at the right time. The added element of achieving data quality in a regulated environment only adds to these challenges. The reality of limited resources (time, money, attention) and making the most impactful use of those resources at the right time can be a hard decision to get right.

BN: A lot of attention is being placed now on using data effectively and providing data insights to the right people at the right time. How accurate do you think the data is that companies are using?

RA: We recently partnered with HFS to conduct research on data trustworthiness. What was revealed was astonishing. Only five percent of C-level executives have a high degree of confidence in the data they have. This low confidence vote leads me to believe that it is a result from years of C-suite leaders viewing data as an IT problem rather than an asset to be leveraged. Data issues weren’t commonly accepted as a business problem until recently. The question for leaders now becomes whether bad data impacts companies enough to address the problems I mentioned earlier. The window of long-term sustainable growth is closing on businesses who've yet to utilize their data on an advanced level.

BN: How can companies assess the quality of their data?

RA: Defining what 'good data' looks like at a technology level is relatively easy. The harder part is defining your organization’s data-readiness to run and advance your business objectives. I recommend any data assessment start with a strategic view that ties data to actual business outcomes versus a purely technical assessment. Take one key KPI and use that to scope the business processes and data that lean to that KPI to baseline the fit-for-purpose quality of that data.

BN: What can companies do to improve the quality of their data and use it more effectively? How and where do they start?

RA: Organizations looking to improve the quality of their data should select one business project to begin with. I often see companies try to take on massive data projects, but these projects can be very complex and can fail when tangible KPIs aren't set up in advance. My advice is to keep these projects small to begin with to show results and learn best practices that work for your organization before scaling into larger projects. If you’re unsure of where to start, begin with a business process that you know is a pain point either in terms of waste, rework or frustration, and the impact it has in aligning with compliance. From there, identify the key data elements used in that business process and start to incorporate those data elements with the rules and policies that determine if the data is fit for purpose (good) or causing issues (bad).

Leaders can also take advantage of external events with major data implications like implementing new systems or data migration. If a large data initiative is jump starting already within your organization, be sure to work quality control and governance checks into the foundation of that program that can lift your overall data posture.

If leadership works toward building a data conscious culture, the result is a deeper, long-term realization of the important role data plays across all organizational levels.

BN: How can organizations maintain their data once they get control over it?

RA: My recommendation to control data quality is an approach that combines business ownership and accountability with technology that makes it easier for the business to contribute. One of the key elements for an effective Data Operations (DataOps) program is to encourage a data-conscious culture where your people are committed to and focused on bringing trusted data to the enterprise. Identifying proper data KPIs will naturally lead into defining and designing the necessary processes to achieve these KPIs. The right tooling is a must to support these processes and people because it will ensure both the collaboration and orchestration of DataOps processes while also supporting people ops through automation and intelligence.

BN: Looking ahead, what are the advantages for companies that invest in their data quality?

RA: Organizations that invest in their data quality now will enhance their ability to remain competitive with their market while maintaining compliance standards within evolving regulatory landscapes. With investments in data, companies can take bigger risks because their decision-making power is backed by data -- not a 'gut feeling.' Quickly accessible, trustworthy data gives leaders the ability to make data-backed decisions at a moment’s notice when compliance standards change. This ability will embolden and empower organizations to make decisions faster from a foundation of trusting the knowledge extracted from their raw data points. This will lessen the impact from changing regulations and other public disclosure requirements.

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