Boosting Adoption Blog Series: #4 Make Sure Your Data Is Trustworthy
Insights are only as good as the underlying data that supports them. Bad information leads to bad analytics that ultimately result in bad decisions. That’s why optimizing data accuracy, completeness, and consistency is our fourth way to drive widespread business intelligence (BI) and analytics usage.
For many organizations, data quality is a huge barrier to BI and analytics success. If you want your users to embrace a tool, you have to make sure they trust it. They’ll only need to get burned by bad information once before they go running back to Excel, start bugging your IT department to generate reports for them, or install their own data discovery tool. This, of course, won’t solve the problem, as it will continue to proliferate the use of disparate and disjointed reports and spreadsheets – a key contributor to data quality problems.
Broad usage will only be possible if your environment serves as a truly trusted way to interact with enterprise information. Every organization has data quality problems – the key is to address them early before you roll out analytics to a large user base. This means leveraging tools that proactively identify and correct these issues before dirty data makes its way into dashboards, reports, apps, and other analytics content.
The wisest approach is to choose a BI platform with integrated data quality management and master data management (MDM) capabilities that will prepare and optimize data for analysis by ensuring its accuracy, completeness, timeliness, and consistency at all times. By embedding capabilities like profiling, cleansing, matching, and merging directly into your environment, you can build confidence in your data and promote greater adoption – without devoting a significant amount of time to manually finding and fixing bad data.
Next week, I'll highlight our final tip for expanding BI and analytics adoption: Including Your Customers.
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