How to Ensure Your Business Intelligence or Data Management Function Succeeds

Gartner estimate that between 70 & 80% of BI Projects fail.

Nobody wants to believe that their next initiative will fall into the failure bucket but, if this statistic is true, the likelihood is that it will.

This two day interactive course focuses on the main reasons for failure and how to combat them.

Each of the topics will be unpacked through explanation and feedback.  Delegates will then learn techniques to tackle the problems.

This course is not about a particular project management methodology nor does it focus on any particular toolset. Rather the course teaches principles citing real life examples of poor and good practice.

Over the past 19 years Altis has successfully delivered hundreds of data driven projects and rescued many that were headed for failure.  Additionally Altis have employed all the major methodologies including; Kimball, Inmon, Data Vault, Agile, Waterfall & Prince.

Course Details

This course will be run on the 13th & 14th of June 2017, please contact Peter Hopwood or Daley Davis for pricing.

London, client site delivery also possible.

Who Should Attend?

Anyone who has an interest or responsibility for good data management, or BI/ MI delivery and wants to ensure that their organisation does not suffer from the problems that others have already experienced.  This includes:

  • BI/ MI Managers & Project Managers
  • Information Users (e.g. Heads of Departments that are consumers of management information or BI)
  • Heads of BI/ Planning/ Management Information
  • CEOs/ CFOs/ CIOs/ CTOs

Day 1

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Introduction

Why BI Projects Fail

Communications and Stakeholder Management

  • Poor communication with business
  • Lack of buy-in

Data and Tools

  • Using BI tools that no one likes (or knows how to use)
  • Silver bullet syndrome
  • Poor data quality
  • Poor data integrity

Day 2

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Approach and People

  • Lack of agilty
  • ROI taking too long
  • Developer optimism

Requirements, Analysis & Design

  • Vague scope
  • Incomplete requirements
  • Analysis paralysis
  • Complexity