Introduction of Power BI

More and more people are using Power BI in their everyday roles. Understanding the different deployment types and basics of the product helps with the adoption of self service data and digital migration. Not every organization has the appetite or capability to use Power BI in the cloud.  The cloud and on premises capabilities do have some differences but it is a great place to engage with dashboards and paginated reports.  The presentations I have given covered these areas.  

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Planning a PowerBI Enterprise Deployment

There is an updated version of the Planning a Power BI Enterprise Deployment whitepaper. Deploying Power BI in a large enterprise is a complex task that requires a lot of thought and planning.

The paper includes these areas for consideration.

Section 1: Introduction Section 2: Power BI Usage Scenarios Section 3: Power BI Architectural Choices Section 4: Power BI Licensing and User Management Section 5: Power BI Source Data Considerations Section 6: Power BI Dataset Storage Options Section 7: Power BI Data Refresh and Data Gateway Section 8: Power BI Dataset and Report Development Considerations Section 9: Power BI Collaboration, Sharing and Distribution Section 10: Power BI Administration Section 11: Power BI Security and Data Protection Section 12: Power BI Deprecated Items Section 13: Support, Learning, and Third-Party Tools

There is a summary of some of the changes at https://coatesdatastrategies.com/blog/updated-whitepaper-planning-a-power-bi-enterprise-deployment

Data Maturity Assessment

The  Data Maturity Assessment is the first part of my Data Quality Framework . It is important to know where you are in the process of data maturity before you can seek to improve the situation.

DAMA sets out the maturity model in various stages. These being 

• Initial /Adhoc
• Repeatable
• Defined
• Managed
• Optimized

There are various tools out there which can help you carry out an assessment. These tools have similar model definitions. One such tool is the hesa tool.

Ethical Data Handling Strategy

As a part of the Ethical Data Handling Strategy in my Data Quality Framework there are various things to consider. A good place to start is with The Data Ethics Canvas which can help you identify and manage ethical data issues. 

ODI have produced a clear definition of what data ethics is

“Data ethics is a branch of ethics that evaluates data practices with the potential to adversely impact on people and society – in data collection, sharing and use” The Open Data Institute, 2018.

Data Ethics is important to consider and embed in your data system now. With the use of data in AI expanding it is important to obtain create an Ethical Data Handling Strategy.