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Customer Success Story

PrecisionPoint-case-study-thumbnails-150x150This month, we are thrilled to highlight the incredible success story of 'Traditional Medicinals' and how they leveraged the powerful capabilities of Power BI to revolutionise their data-driven decision-making process. By implementing Power BI, 'Traditional Medicinals' experienced a remarkable transformation in their ability to gain valuable insights and visualize crucial information.

Let us help you transform your business and achieve remarkable success, just as we did with Traditional Medicinals.

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Looking for Business Intelligence for solutions
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Precision Point - How to mitigate against self-service polluting data

In this, the second of our two-part discussion paper, we look at how to mitigate against the risks of self-service BI tools allowing individuals to pollute key corporate data. Having reviewed who owns the data governance, you now need to address the tools in use throughout the organisation.


Points to consider :

  1. The tools that build reports for the most part only connect to the data.
  2. An enabler is required between the front-end reporting and the database.
  3. This enablement layer needs to simplify the rules and logic of the data structure and without this layer, the user needs database skills as well as an understanding of the reporting requirements of the business… a rare combination. This is why most BI tools available today require significant investment (in time and expertise) before coherent reports can be extracted as systems are understood and data interpreted.
  4. A data warehouse can increase the speed to delivery of these reporting requirements as it removes the need to learn complex data schema and can then provide the right information to the right users:
    1. Power users will need ad hoc reports
    2. Most users however will only require a dashboard and the ability to drill down to the data but without the ability to extract and manipulate the data
  5. A data warehouse conforms to the data governance requirements of the IT department as it can validate and reconcile true accuracy and then pass that to the BI tools and reporting requirements of the business

The Simple Steps to Data Governance

  1. Perform a full audit of the database (or databases) and see how data flows through the systems and company, to understand the structure and processes.
  2. Build a data library and from that a data warehouse providing the one single source upon which the business can report.
  3. Implement a repeatable, re-useable, accurate form of input and apply data governance rules via the use of a data warehouse.

Doug Laney, research VP at Gartner stated that Most deployments are ultimately unsuccessful when end users are given access to manipulate their own data — sometimes from unreliable sources.”  This can be corrected with correct data governance which can be included within an enterprise BI tool that traces data source and journey.  Alternatively, any business that has a fully managed data warehouse, resolves this as the data warehouse will have adhered to what Gartner terms “smart data preparation.”

A data warehouse removes the dependency on the BI developers who would have to model, build and test multiple reporting layers to achieve the desired reports. A data warehouse is often used to examine business trends to establish a strategy for the future. The viability of business decisions is contingent on good data, and good data is contingent on an effective approach to data quality management.

Thus it concludes that self-service BI can only be achieved with the correct data governance, project governance and data warehouse underpinning any and all projects.

What defines a Successful BI Project?

Forrester carried out research to find that successful BI project tips (Ref 6);

  • Put the business into business intelligence – you need a business-level executive sponsor
  • Be agile and aim to deliver self-service – allow an iterative development
  • Put a solid governance foundation in place – data and project governance

Without proper data governance, a company risks a lack of accuracy and accountability.  And yet the pendulum has swung away from the governance of the data by the IT department, towards a preference for Self-Service BI and these do not insist on the correct data governance of control and accuracy.

Stated Wayne Eckerson from Inside Analysis; “Self-service BI is great for users with analytical experience, but bad for users without an analytical background”.

This point was elaborated on by Iain Plunkett of Garrett As soon as you enable end users to make the decisions about how they access and use data – then you have a huge central problem. You cannot ever then control where that data is being replicated and used. Enter enterprise content management and a central data warehouse – where data is controlled.”

Self-service BI dramatically increases the need for data governance because of the potential introduction of un-governed data sources and the freedom allows business users to create their own data modelling.

The use of a data warehouse can put an effective “data firewall” in place, giving not only one view of the data and thus true accuracy but also preventing the reintroduction of bad data and only allowing the display of controlled, quality, approved information.

Andrew Mennie



PrecisionPointWhere data becomes trusted insight

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  1. Data Governance Basics
  2. Solving the People Problem as Data Governance Grows
  3. data governance
  4. Forrester best practice tips for business intelligence success
  5. BI implementation
  6. Forester BI project best practices
  7. Self-Service BI vs. Data Governance (Page 1)
  8. Self Service BI Governance
  9. Q&A: Self-Service vs Traditional Business Intelligence
  10. Microsoft self-service BI and importance of Governance