Steps to successful adoption of a new data warehouse

What is taking so long to get the data warehouse ready?

In a new deployment of a data warehouse there are many infrastructure components that have to be put in place. Modeling tools, ETL Servers, ETL processes, BI Servers,  and Bi interfaces and finally reports and dashboards. Not to mention sessions for user interviews, business process review and metadata capture.

I say server(s) because there should be dev/test and prod platforms for each of these.

Figure 3-4: how data models deliver benefitImage via WikipediaA recent article at talks about data modeling taking too much time if done correctly.

Add all of these things together and you have a significant period of time to wait before seeing a benefit to a Data Warehouse/Business Intelligence project.

Here are some suggestions to reassure the stakeholders early on during the project lifecycle.

Give them data early and often.

     Put together a small and simple data model for the first pass. Load the small star schema with a subset of the data relevant to a group of business users, then create some reports or give some power users access to create their own reports.

    This shows the concept of continuity. A Continuity test in electronics is the checking of an electrical circuit to see if current flows, or that it is a complete circuit.

Show the data quality issues

  "A problem well stated is a problem half solved" Without seeing data quality issues, the people that enter data into the system of record can not fix it.

Get and give feedback often

   As soon as people start using the "prototype", you will get feedback. Use this as an opportunity to explain why the process should take longer. It also identifies gaps in understanding among the team. Once people have a hands-on view of the presentation layer they will try a number of things.

They will use it to answer questions they already have answers to. Thus validating the transformation processes.

They will also start to try to answer questions they may not have asked before. This is the best opportunity for learning more about how the data is being used.

These steps lay the foundation for making data work for you and your business.

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