DataOps - Closing the Gap between Data and Insights More Effectively
Although the adoption of advanced analytics is rather high with over 90% of companies recognizing its value, unlocking the full potential of analytics is still a big challenge for most businesses. The lack of automated tools and automated testing sees companies spend around 80% of their time (according to a McKinsey report) on repetitive tasks around data. A further 70 percent or more of data integration, migration, and business intelligence initiatives fail on their first attempt as businesses create more data than ever.
DataOps responds to this challenge through an automated, process-oriented methodology with a focus on improving quality and reducing the cycle of advanced analytics. A Gartner research found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses.
Making a difference
Enhanced Data Quality
The increasing demand for real-time data and insights requires data to be quality tested at every stage of its lifecycle. Dataops automations and processes not only makes this kind of iterative testing possible but allows for scaling business data and reporting processes efficiently.
Faster roll-out of new features
Businesses today are publishing new apps and updates at an unprecedented rate, sometimes several times in a day. By removing manual testing using inadequate tools of such critical highly visible data, DataOps not only cuts the risk associated but delivers a range of performance improvements. Findings reveal that the quicker development iterations in data automation allows the volume of new features to be increased by 50 percent.
Opening up insights
By promoting partnership among data scientists, data engineers, Devops engineers and other technologists, DataOps allows for a more seamless, in-sync working with data, and in a shorter time. With Dataops in the scene and the emergence of cloud date-warehouses and code-free visualization dashboard, decision makers have direct access to insights they need without having to depend on data teams.
DataOps responds to this challenge through an automated, process-oriented methodology with a focus on improving quality and reducing the cycle of advanced analytics. A Gartner research found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses.
Making a difference
Enhanced Data Quality
The increasing demand for real-time data and insights requires data to be quality tested at every stage of its lifecycle. Dataops automations and processes not only makes this kind of iterative testing possible but allows for scaling business data and reporting processes efficiently.
Faster roll-out of new features
Businesses today are publishing new apps and updates at an unprecedented rate, sometimes several times in a day. By removing manual testing using inadequate tools of such critical highly visible data, DataOps not only cuts the risk associated but delivers a range of performance improvements. Findings reveal that the quicker development iterations in data automation allows the volume of new features to be increased by 50 percent.
Opening up insights
By promoting partnership among data scientists, data engineers, Devops engineers and other technologists, DataOps allows for a more seamless, in-sync working with data, and in a shorter time. With Dataops in the scene and the emergence of cloud date-warehouses and code-free visualization dashboard, decision makers have direct access to insights they need without having to depend on data teams.
