Demystifying Data 101: The Nuts and Bolts of Successful Data Ops

Everyone is collecting data, but few companies know how to get the most value from it. Between collection and complex analysis, there are many stages to data management — which means many opportunities for something to go wrong. According to Gartner, poor quality data costs organizations an average of $12.8 million per year. Minimizing this drain on resources will require improving data operations, through data observability. Fortunately, the right data management system will also reap significant business benefits. The first stage of successful data ops is gaining a comprehensive understanding of what’s going on in your data environment, both the good and the bad. In this first installment of the Acceldata series “Turning Data into Information: How Multi-dimensional Data Observability Uncovers the Insights at Your Fingertips,” data observability experts will dig deep into the data pipeline, data and data processing layers and call out the pain points you may not even be aware of. Join Acceldata and a panel of experts to learn more about: Why data collection isn’t enough How to re-envision your data pipeline as a supply chain, with each stage leaving its mark on the larger data lifecycle What companies most commonly get wrong about their data quality strategy The opportunities available if you commit to a data observability protocol