Gartner estimates 50-80% of data analytics projects fail. Getting analytics right is critical because organizations are competing based on the effectiveness of their data-driven insights that inform decision-making. Analytics, machine learning (ML), and continuous testing eliminate costly errors.
Neither DevOps nor DataOps is mature. Both processes are dynamic and evolving, and the blueprint of the future hasn’t been drawn. Consequently, the marketplace for tools is chaotic and may be this way for some time. Many tools overlap, and job roles are fluid.
“DataOps gives programmers confidence they are using the right data at the right time," said Mitch Martin, Director of Software Engineering, Data Society, a Skillsoft learning partner. "Code is solid, while data is fluid and has a more complex life cycle. DataOps provides the orchestration needed,”
The advantages of applying DevOps' strengths to data are too great to sit this one out, but that doesn’t make it any easier to know where to get started. Data literacy is low in many organizations leading to less-than-optimal decisions about data sources and uses.
However, overcoming the chaos in the marketplace isn’t optional.