Data is at the heart of every organization.
In the journey to become more data-driven in decision making, we are seeing unprecedented democratization of data and adoption of self-service analytics. Rigid data collection and reporting processes of the past have given way to rapid gathering of raw, unstructured and crowdsourced data. As a result of that change, there are inevitable trade-offs with data quality.
Self-service visual analytics solutions often quickly expose data quality issues that you may not even realize exist. Unfortunately, inaccurate data undermines the powerful value of self-service analytics. If people don't trust your reports, they won’t use them. Since self-service analytics credibility, adoption and success hinges on accurate data, data quality should be given more attention as you implement these solutions.
Every organization today depends on data to understand its customers and employees, design new products, reach target markets, and plan for the future. Accurate, complete, and up-to-date information is essential if you want to optimize your decision making, avoid constantly playing catch-up and maintain your competitive advantage.