Likely more than once, you’ve witnessed executives and decision makers review dashboards and reports and then say, “This looks great, but I’m not comfortable relying on it because I don’t trust the data.” In such situations, artificial intelligence (AI) can play a pivotal role in enhancing confidence by facilitating data quality management.
Data can be brought to life and generate significant value to an organization using visualizations, dashboards, and analytics. Yet data visualizations, regardless of their complexity level, merely display input data in the form of key metrics, trends, predictions, and other analytical insights. Therefore, the output shown is only as good as the data fed into the data visualization tool. An expression describing this phenomenon in computer science is “garbage in, garbage out.”
Organizations are discovering the importance of reliable data quality management. According to Forbes, organizations lose an average of $15 million per year due to poor data quality.1 Suboptimal data quality management can lead to the retention of outdated, outlier, or poor-quality data, which may be pulled into reports and dashboards. When poor-quality data feeds a report, executives and decision makers often see anomalies or misleading analytics. However, addressing data quality issues across the organization can be more effectively handled by technology solutions, such as AI, than through manual executive intervention.
Data Quality Concerns
Data quality is impacted by many factors, including accuracy, timeliness, uniqueness, consistency, and validity.
As chief data officers push their organizations to become more data-driven, organizations should implement and rely on tested, rigorous, and well-accepted data governance processes. Furthermore, a “data mindset” should be embedded and ingrained into all layers of the organization, so that it evolves to embrace “data culture” as a fundamental part of the organization’s DNA.
While data security and privacy remain important concerns for any organization, data quality—and all its attributes—is paramount. A leading objective for data quality management is to make high-quality data omnipresent and pervasive throughout the organization.
Major focus areas to help meet this objective should include data relevance and availability. While focusing on building a flexible and scalable data architecture, organizations should prioritize these two key attributes to help empower data literacy and decision making.
Common challenges for remediating data quality concerns include:
- Many existing mechanisms for flagging data quality issues originate from predefined “rule libraries.”
- These rules have been built over time and are anchored in deep knowledge of the specifics of the business.
- The mechanisms are efficient at flagging the most obvious outliers and data entry errors; however, they take time to deploy and require frequent, manual maintenance.
- The codes to catch outdated or obsolete data can become quite complex with exception handling, and by no means do these guarantee exhaustiveness.
Is an alternative approach—or at least a hybrid one—possible that would complement the traditional rules-based solution?
Leveraging AI for Data Quality Management
Rather than relying solely on business rules, AI can help quickly identify unusual, unexpected, or abnormal data patterns. Forvis Mazars can help deploy an innovative data quality solution, Smart Data Quality Platform, powered by AI and the Microsoft Intelligent Data Platform. As a result, end-users get 360-degree visibility of data quality.
Key benefits for platform end-users include:
- Interaction with AI via an intuitive interface
- Ability to assess the outputs of a thorough scan of available data
- Analysis of the probable root causes of flagged anomalies
- Access to remediation suggestions ranked by probability of accuracy
In addition, advanced technical experience is not required to use the platform. Should your organization opt to use this hybrid approach, outputs also can be leveraged to build new business rules and update existing rule libraries.
Ready to Get Started?
At Forvis Mazars, our experienced technology consultants can help you efficiently review available master data sets and associated granular data to identify data quality issues (even at the source level), implement remediation best practices, and deploy AI-powered data quality solutions.
With the appropriate governance, we can help you deploy solutions that can improve overall data quality, enhance business rules with AI, and advance high-quality data analytics and visualizations your stakeholders can trust. Contact us today to learn more about how we can help enhance data quality management at your organization.
- 1“Flying Blind: How Bad Data Undermines Business,” forbes.com, October 14, 2021.