Data Architecture Before Science

How an organization leverages its data and analytics to help drive towards strategic business outcomes is critical to achieving success today.

Business stakeholders clearly see the value and vendors like Microsoft have made enormous investments in their data and analytics platform with Azure: Machine Learning and AI in addition to Power BI.  Commonly we see the business investing hundreds of thousands of dollars in a data visualization tools and hiring “data scientists” shortly after the Tableau Account Executive leaves your offices.

The problem is that this often leads to disappoint, rework, and loss of investment and time without starting with a solid data architecture and roadmap.

Without a solid data architecture in place that aligns to your strategic business outcomes for data and analytics you won’t get very far with a data scientist and business translator.

If you look at the reference data architecture above you will see that those folks along with data visualization tools really only adds value within distribution and consumption of the data.  Maturing and moving past those self-service and prototyping scenarios becomes impossible without addressing the rest of it.  Making business critical things like machine learning and predictive analytics impossible to execute properly and efficiently.

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