Other than this, legacy organizational systems and processes often serve as an obstacle in seamless data usage. Organizational data teams face constant interruptions with data and analytical errors. It is estimated that data scientists spend almost 75% of their time preparing data and executing manual steps. This is further exacerbated by a siloed organizational structure that hinders collaboration between groups and leads to overall lengthier analytics cycle time.
The challenges can often be daunting and deter an organization from utilizing valuable business data as a source of insight. Several other factors including process bottlenecks, rigid data architectures, wait time for approvals, technical debt from previous deployments, data discrepancy & data deduplication and more are also at play.
Considering this plethora of challenges, organizations need to rethink their data strategies and approaches radically. An integration of data workflow and processes that enables rapid implementation of ideas and quick execution of higher quality models and analytics will be required to integrate high-quality data analytics into the regular workflow. Here, one of the first steps is to understand data governance and how to orchestrate it.