Common Challenges to Data Quality
Cost. Organizations spend enormous amounts of money on digital analytics. Consider the costs for analytics tools, the analytics team, the developers who populate the data, and the indirect costs of the people who spend time using the data that is collected. All of these costs are wasted if the data they are using is incorrect.
Risk. If the data in your analytics implementation isn’t accurate, you can lose additional money by basing business decisions upon faulty data!
Infrequency. Many organizations only spot-check their data or fix data issues when they are noticed by a business user. Others pay additional money to use 3rd party tools to identify data quality issues.
Trust. But data quality isn’t something that should be left to chance or tackled on a periodic basis. If digital analytics data quality suffers, it tarnishes the reputation of your implementation and your analytics team. If your business stakeholders encounter too many data quality issues, they will stop using your data to inform their business decisions.
Inattention. Compounding the problem is the fact that executives tend to care more about higher-level goals than whether data at the lowest levels is accurate. While it may impact their analytics team or business users, executives don’t feel the direct pain associated with analytics data quality issues.
How Apollo Provides Real-time, Interconnected
Data Quality Validation
What This Means for Your Business
Apollo connects data quality issues to business requirements. Executives don’t typically care about individual data elements, but they should care about the business questions that can and cannot be answered by the analytics team or its stakeholders. Since Apollo knows how each data layer element supports each business requirement, it can highlight the exact business requirements that cannot be answered as a result of data quality issues. This means that executives and stakeholders can finally feel the direct impact of data quality issues.