Data, if those using it understand and trust it, has the capacity to shake up markets and forge new ones. However, if your data is junk, your insights will be, too. Most enterprises today are aware of the value of data quality, but achieving it is another matter. This is their main problem, which is made worse by the assumption that the process is drawn-out and difficult.

What follows is information on how to increase the quality of your data and the metrics you should use to assess quality.

Overcoming bad data

We’re aware that data powers the modern world, yet we also need to be aware that not all data is created equal. You won't be able to accomplish your company objectives using bad data, and it can result in a wide range of other difficulties. Organizations lose an estimated $12.9 million annually as a result of bad data. That’s a lot of money being lost when it doesn’t have to be.

These are the main four causes of poor data quality:

  • It's entered incorrectly by a person
  • A machine, interface or migration creates or moves bad data
  • The data quality is reduced due to incorrect system use or poor coding
  • Because of the business's adjustments, formerly fit data is now unfit

The fundamental challenge in instituting data quality is correcting the above sources of bad data. Choosing the right time to use scarce resources (such as money, time and attention) can be challenging. The good news is that businesses are paying attention to this situation. According to Gartner, 70 percent of firms will use metrics this year to carefully monitor the quality of their data.

Getting to quality data

Technically speaking, it's fairly easy to recognize good data, but it's more complicated to assess how prepared the data is to support and expand the business. If your organization is immature from a data quality and master data management perspective, you shouldn't begin a project with the intention of controlling all master data right away. It's not a good idea to begin by assuming that your data will be perfect immediately. Rather, it's a very iterative process that ought to keep its attention firmly fixed on providing value. All assessments should start with a technical evaluation and a top-down strategy. Examine the business processes and the associated data using easily measurable key performance indicators (KPIs) and set a reference point for how fit the data is for its intended use.

The best thing a corporation can do to increase data quality is to narrow its focus and get to work on it. If you're unsure of where to start, pick a business process that you know needs to be addressed. This process should be one that results in rework, waste, annoyance or financial loss. The next step is to identify the crucial data components that this business process uses and include those components in the rules and policies that determine whether the data is appropriate for use.

Additionally, you can use outside circumstances that significantly affect data, such as a data migration or the implementation of a new system. Including quality into the program’s core helps improve your enterprise’s overall data posture if a big data-related initiative is about to start or has just started.

Working toward a data-conscious culture should have the long-term benefit of acknowledging the crucial role that data plays throughout the business, not just in the IT department, and should be supported by leadership every day.

When an enterprise has achieved data quality, it needs a strategy to keep it that way. This will require the joint effort of people, processes and technology. The performance and goals of the people working in a DataOps program must be handled in accordance with their dedication to and emphasis on bringing trusted data to the organization. The right data KPIs will inevitably result in the creation and customization of processes to help achieve them.

Last but not least, these procedures – and people involved with them – need the appropriate tools. The right tooling will ensure that DataOps procedures incorporate coordination and collaboration while also reducing – through automation and intelligence – some of the workload placed on humans.

Data quality for the long term

In the style of Ralph Waldo Emerson, we could say that “data quality is a journey, not a destination.” And change itself is the only constant, to paraphrase an anonymous bit of wisdom. Data is evolving at a dizzying pace as people switch jobs, businesses change identities and business priorities shift.

Data quality is therefore not an activity you do once; rather, it requires ongoing upkeep, consistent effort and financial investment. The funding allotted has a direct impact on the success of data quality, so factor that in as you re-assess or begin your data quality journey.

Subscribe to our daily newsletters