Stop grappling with rubbish data and make data quality a process

Markets can be disrupted, and barriers can be broken with data, but only if that data is reliable and comprehensible.

If your data is rubbish, your insights will be, too. And while most enterprises today have at least a basic understanding of the value of quality data, achieving it is another matter. That’s a significant problem, compounded by the perpetuating idea that achieving data quality is a drawn-out and difficult process. What if it didn’t have to be? With the right steps, you can increase the quality of your data in a structured manner – gain a better assessment of the quality of your data overall.

Why is your data bad?

While it’s true that data powers the modern world, it’s critical to remember that not all data is created equal. You won’t be able to reach your company objectives with bad data, and it can also result in many other challenges. Organisations are predicted to lose over £12 million annually as a result of bad data.

What causes data of poor quality? The four biggest causes include:

– Someone enters the data incorrectly

– A migration, interface or machine creates or moves bad data.

– As a result of the business changes, data that was fit is now unfit.

– The data is contaminated due to poor coding or incorrect system use.

The foundational challenge in developing data quality is balancing the four sources of bad data and addressing the appropriate sources at the appropriate time. It can be challenging to choose the right time to use scarce resources – such as time, money and attention. Fortunately, enterprises are now paying attention to this problem. According to Gartner, 70% of enterprises will systematically monitor data quality levels using metrics by the end of this year.

Toward better data quality

Technically speaking, recognising “excellent data” is straightforward; the challenge is figuring out how ready the data is to support and expand the business. You shouldn’t begin a project with the expectation that you would have complete control over all transactional and master data right away. Instead, it’s a very iterative process that should continually put the emphasis on providing value to the business based on improvements in data quality.

Any assessment should start with a top-down strategy and a technical evaluation. Focus on the most critical key performance indicators (KPIs) and their relationship to business processes that impact those KPIs. From there you can go a level deeper to determine what data elements play a role in executing those processes. The most important action an enterprise can take to increase data quality is to narrow its focus and really get to work on it.

Additionally, you can make use of business events that require a focus on data, such as a data migration or the implementation of a new system. Integrating quality into the program’s core helps improve your organisation’s data posture once the migration is complete..

Working toward a data-aware culture should have the long-term effect of truly acknowledging the essential role that data plays throughout the business, not just in the IT department, and should be supported by everyday leadership.

Enterprises require a strategy to keep their data quality under control once it has been achieved. Participation from people, processes and technology are all required. The professionals involved in a DataOps program must be dedicated to and homed in on bringing trusted data to the organisation, and their performance and goals must be handled with those emphases in mind. Appropriate data KPIs will spontaneously result in procedures being designed and customised to help achieve them.

Finally, these processes and people need the appropriate tools. This will ensure that DataOps processes use coordination and collaboration while also reducing some of the workload placed on humans via automation and intelligence.

Embrace data change

It’s helpful to think of data quality as a journey, not a destination. It’s also helpful to recognize that change is inevitable. In particular, data is changing at the speed of light, it seems. Data quality is therefore not a “set it and forget it” one-time project; rather, it requires ongoing upkeep and financial commitment. The amount of budget allocated is directly proportional to the success of data quality. A digital organisation must continually seek out new ways to enhance business models, improve customer experience and investigate new business opportunities. Quality data is essential to all of these activities.

About the Author

Rex Ahlstrom is CTO at Syniti. Syniti enables agile enterprises with silo-free enterprise data management that helps turn complex data challenges into competitive advantages. With a unified, learning platform and one of the world’s largest teams of data-focused experts, enterprises and global alliance partners choose Syniti when they require trusted data to ignite business growth and reduce risks.

Featured image: emmet