Best Practices for Improving Data Quality

Your clients need quality data to yield accurate and useful insights.

how to improve data quality

Garbage in, garbage out is undeniably true when using data analytics tools. Although you may be able to provide your clients with a user-friendly, end-to-end Data Analytics as a Service solution, they won’t unlock the value it can provide without data quality.

What is Data Quality?

There are numerous definitions of data quality, but a common theme is that the data is well-suited to the use case. DATAVERSITY states that attributes of quality data include:

  • Accuracy
  • Completeness
  • Consistency
  • Integrity
  • Reasonability
  • Timeliness
  • Uniqueness
  • Validity
  • Accessibility

How Data Quality Impacts Business

The insights from data analysis are intended to inform decision-making and lead to better outcomes. Unfortunately, when companies act on data analysis based on low-quality data, the results can range from bad to worse. Losses may include repair costs, lost assets, decreased customer satisfaction and loyalty, or damage to brand reputation. For example, poor data quality could result in a resort overbooking rooms or a retailer selling an item online that’s out of stock – or an organization could experience a catastrophic loss such as the destruction of the $125 million Mars Climate Orbiter in 1999 due to engineers not converting English units to metric.

IBM estimates that poor data quality costs businesses in the U.S. about $3.1 trillion annually and points out that about one-third of business leaders don’t trust the information they use to make decisions.

How to Improve Data Quality advises taking a holistic approach to managing data:

  • Organization: Your client needs to build a culture that recognizes the importance of data quality and assigns roles to people with the right skills to ensure it. Specific data sets should have a data owner who defines requirements, provides accessibility, grants access, and authorizes stewards to manage data. The data steward is responsible for operational data quality, such as deduplication. The MSP could assume the data manager role, implementing the technology that enables data collection and security.
  • Process: Your client needs to follow a data quality cycle made up of phases, including:
    • Define goals to meet business needs.
    • Determine the type of data needed to meet those needs.
    • Clean data according to those rules.
    • Enrich data from other sources if necessary.
    • Monitor and check data on an ongoing basis.
  • Technology: Equip your clients with technology solutions that will streamline processes and save time for data owners, stewards, and managers. Find tools that suit the technical expertise of users – solutions that are too complex will have little value to your clients.

Your Expertise and Services Add Value

As a Data Analytics as a Service provider, your role must be more than just a reseller. Your clients and prospects will rely on you for recommendations for the best solutions for their businesses and use cases. They’ll also count on you to help them implement those solutions and train their staff to get optimal results.

They may also need some guidance and education on data quality – turning to you for best practices that ensure they’ll get reliable, accurate insights. Your expertise helps them maximize the ROI they receive from their data analytics solutions, but it will also benefit your business. Lending your expertise to your clients’ applications will ensure they gain the insights they need to advance their businesses. That’s a reputation you want.