Enterprise Data Landscape
Maturity Assessment

Enterprises planning to be data-driven in their decision-making and operations often create fragmented and siloed data modeling and analytical setups. Often challenged by problems of erroneous correlations, inconsistent data quality, lack of data discoverability, siloed analytical models, duplication of data, and lack of access control and governance, the data insights often come in late or incorrect or both resulting in significant data reconciliation and operational effort and cost with sizable delays in business activities and decisions and impact on revenue.

The ability or inability of enterprise data platforms to serve business consumers with timely, accurate, and actionable insights; as well as its ability to continue doing so over time as data producers, processors, and consumers scale and grow, relies on how the underlying data landscape adheres to or deviates from the Industry recommended reference landscape and standards.