More data doesn’t (always) mean more insight: The old chestnut of “MI Requirements”

Figure at red intersection of arrowsI hazard a guess that many of us have worked in organisations where data is, quite understandably, seen as the crucial “life blood”.  Data can be an important asset that not only keeps the operation ticking over but also drives informed decision making.  Not only can good quality data help us to benchmark organisational (and individual) performance, it also helps us to spot trends and anticipate future customer needs.  Managers and executives need reliable and accurate data to make tactical and strategic decisions—and in some cases this may lead to repeated requests for more and more data.  We might find that a mini-industry is created in crafting internal “Management Information” reports, often involving triangulating data from multiple, disparate systems, with an associated risk of error.   We may well initiate projects to automate the creation of reports so that we can get them to our stakeholders quicker, faster and in greater quantity.

 

This addiction to data is understandable – after all, organisations need to rationalise and justify the decisions they make.   Whether a company is multinational, mid-size or even a small entrepreneur, it’s vital to know how things are performing and where improvements can be made.   However, there’s a real danger that a dependency on misleading data can lead to delays, indecision and could even damage decision making.  Put simply, more data doesn’t always create more insight.  And this has a significant impact for businesses, projects and business analysis.

 

The dangers of data addiction

There is nothing inherently wrong with an increased focus on data.  In fact, making decisions based on appropriate data is likely to improve organisational performance.   The danger comes when an organisation floods itself with data without considering the outcomes and decisions that are desired.   It collects – and reports – on so many metrics that forming any kind of conclusion becomes extremely difficult.   If our managers are relying on spreadsheets and reports to try to conceptualise multiple dimensions and a high volume of variables, it starts to get confusing.   There’s a danger that the data might be misinterpreted.  That could lead to a request for more data (which delays a decision) and could even cause a less-than-perfect decision to be made (as the data has been misread).

 

The focus should be on generating insight and actionable data.  Not just more data or more reports!

 

What this means for business analysis and business analysts

Often, the change projects we work on may have an impact on the data that our systems process, and that our systems report on.  It’s essential that we thoroughly consider the impact of how the business might need to use the data – both operationally, but also to drive insight for strategic decision makingThis is an area that stakeholders might not immediately tell us about, so we need to prompt them with questions to elicit their true needs.

 

We should also be extremely careful with requests for new “MI Reports”, and work with our stakeholders to understand:

 

  • Is a report the only way of delivering this (might there be other solutions, perhaps more interactive and flexible solutions?).   A classic case of ensuring we analyse the problem before jumping to the solution
  • What is the data needed for? (What insight are you trying to gain?)
  • How frequently is it needed ?
  • How fresh does the data need to be? And do we have access to data that’s ‘fresh’ enough (if not, how do we get it)?
  • Do we have the underlying analytic capabilities to deliver this? If not, how do we attain or obtain it?
  • Is the underlying data trustworthy?
  • Who will use the data? Are they the same person who is making the end decision? (If not, does the end decision maker have any other requirements)?

 

This isn’t an extensive list of questions by any means; there will be many more depending on the individual business situation.   However, it is useful to consider these questions as a starting point.   Fundamentally, understanding how data will be used and why it is needed is key.

 

Conclusion

Data isn’t just used for operational purposes; it’s also interpreted, extrapolated, chunked up and used for strategic decision making.  It’s important that this is considered when initiating any change project as it may well have an impact on the requirements.   Creating opportunities for data to be actionable and for us to create insight is valuable.

 

 

How do you handle data in your organisation and in your projects?  I’d love to hear your views and insight, so please keep the conversation going add a comment below. And if you like my blog, don’t forget to subscribe!.

 

 


This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies or opinions

 

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