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 to make important decisions because, fundamentally, I don’t trust the data.”
Data can be brought to life using visualizations, dashboards, applications and analytics, all known to generate significant value to an organization. Yet visualization applications, regardless of their complexity level, merely display company data in the form of KPIs, trends, predictions and other analytical insights. The output we see, therefore, is only as good as the data that feeds these applications.
An expression describing this phenomenon in computer science is: Garbage in, garbage out.
A chief data officer’s challenge
Data quality is impacted by many factors, including accuracy, timeliness, uniqueness, consistency and validity.
As chief data officers (CDOs) push their organizations to become truly data-driven, businesses must implement and rely on tested, rigorous and well-accepted data governance processes. Furthermore, a “data mindset” must be embedded and ingrained into all layers of the organization, so that it evolves to embrace “data culture” as a fundamental part of the business’ DNA.
While data security and privacy remain important concerns for any organization, data quality (and all its attributes) is paramount. The ultimate objective of overall data quality is to make data omnipresent and pervasive throughout the organization, aiming for a “data-as-a-product” model.
Major focus areas must include data relevance and availability – while building a flexible and scalable data architecture, empowering senior data executives’ decision-making and evolving toward a dynamic organization that promotes data literacy.
The frequent challenges of monitoring and remediating data quality issues include:
- Most of the mechanisms in place to flag data quality issues are based on “rules libraries.”
- These rules are built over time and anchored in deep knowledge of the specifics of the business.
- They are efficient at flagging most obvious outliers and types of data entry errors; however, they take time to deploy and require frequent and timely maintenance, thereby involving business users and manual inputs.
- They can become quite complex (exception handling) and by no means do they 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
Rather than relying solely on business rules, AI can help discover unusual, unexpected or abnormal data patterns. Mazars has developed an innovative data quality solution, Smart Data Quality Platform, that’s powered by an AI engine combining advanced technical tools and business knowledge. The result? End users get 360-degree visibility of data quality.
Other platform benefits for end users include:
- Interaction with AI via a friendly interface
- The ability to assess the outputs of an exhaustive scan of the available data
- Analysis of the likely root causes of the flagged anomalies
- Access to remediation suggestions ranked by probability of accuracy
Better yet, no advanced technical expertise is required to use the platform. Should the organization opt for a hybrid approach, outputs can also be leveraged to build new business rules.
Ready to get started?
At Mazars, our team of data scientists efficiently perform comprehensive reviews of available master data sets and associated granular data to identify issues (including at the source level), suggest remediation and advise clients on the optimal strategy for their data journeys.
With the appropriate governance, we recommend and deploy solutions that lead to lasting improvements in overall data quality, enhanced business rules derived from AI, and high-quality data analytics and visualization applications you can trust.