News & Resources

CFOs: Not all big data is spotless

Jul 27, 2016 Walt Wojciechowski

When it comes to scrutinizing risk, financial data analysts have plenty of tools to choose from. Customer profitability analytics show accountants which demographics generate the most revenue, while credit and decisioning tools show which customers are financially responsible.

These and other applications are the children of big data. However, the analyses these tools produce depend on the availability of complete, accurate information. Unfortunately, not all big data is reliable.

Defining data quality
According to TechTarget, data quality is an assessment of information's condition. It allows data scientists, analysts and others to figure out whether it's adequate to serve its purpose. Professionals do this by assessing the following criteria:

  • Comprehensiveness
  • Accuracy
  • Relevancy
  • Consistency
  • Dependability
  • Accessibility

The impact of poor data quality
One of the biggest problems facing businesses today is that they're unable to identify inaccurate data. TechVision Research noted that data quality metrics do not always spot incorrect information. In addition, if the information appears accurate, many professionals assume its true.

Inaccurate data can mislead accountants and financial analysts.

Such a lack of adequate checks and balances can compromise a company's decision-making. What if a credit analyst approves a loan based on erroneous data that paints the borrower in a good light? Now, he's entered a risky agreement without knowing the potential consequences.

Developing a data governance plan
In order to support data quality assurance measures, organizations need to first institute data governance plans. According to Oracle, data governance is a permission and accountability framework that dictates how individuals evaluate, create, store, use, archive and eliminate information. Integrating data governance into enterprise operations is a three-step process:

  1. Explore: Identify specific data quality needs, policies correlative with business goals and effective QA metrics. In addition, find systems and technologies capable of supporting these demands.
  2. Expand: Extend data governance beyond the initial project, integrating it into other business segments.
  3. Transform: Continuously assess operations to improve data QA and governance processes.

The first stage warrants extra attention, as it's where enterprises procure the systems necessary to build their data infrastructure. For businesses that use consumer financial data to analyze risk, there are several solutions which enable them to establish a strong data QA foundation.

Accessing data quality tools
In response to data quality concerns, consumer data collection companies have developed solutions which leverage proven, effective QA measures. Essentially, they reduce the costs associated with data governance, as users don't need to allocate as many resources checking information for accuracy, relevancy and other factors.

MicroBilt offers such solutions. For 35 years, we've been collecting and analyzing financial data for businesses competing in a variety of industries. We've designed our systems to provide relevant, accurate and complete data that empowers staff to make informed decisions around leasing, lending, collections and risk management. Quality data is the rule, not the exception.

If you want to learn more about MicroBilt's data tools, speak with one of our representatives.