Optimising financial processes

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When Data Initiatives Fail to Deliver – 5 Reasons

There is considerable research reporting on excessive failure rates of many business initiatives, most recently “digital transformation” and “data projects”.

A recent article by Adrian Mitchell got me back on this track. His article entitled “5 reasons why 80% of data and insight projects fail” is worth a read.

But these “disappointments” are not related to “abject total failure” alone, but to the much more pernicious result of “failing to meet customer/stakeholder expectations”.

In any business, if you do not meet or exceed your customer expectations for a transaction or relationship, you have created a “learning opportunity” which we should grasp with both hands. 

The article I reference explains 5 key reasons for these “failures”. 

These reasons are focused on failure from the perspective of the “data professionals”.

It is important also to focus on failures from the perspective of the businesscustomer“.

They have the biggest stake in successful business outcomes AND there are specific actions that they can take to enhance the chances of them!

In the article below I describe 4 critical elements;

  1. The Business outcome required, or “what good looks like”.
  2. Collaboration between data analysts and business stakeholders is about the wrong thing. 
  3. Time/state dependency for “single source of truth” of data.
  4. Semantics – what does the data actually MEAN? 

I develop these themes in a short read here . . . and share Adrian’s source article there also. 

These are critical topics for data scientists, analysts and their leaders, BI teams, process mining experts, CIOs, and line of business managers, operational teams and subject matter experts, all trying to drive enhanced process performance, customer value and effectiveness in the organisation.

You can read this short article here. . . .

Thanks for reading