From HBR: IT fumbling analytics

Harvard Business Review certainly doesn’t need endorsement from me; its impact is well-known worldwide. So it’s the spirit of sharing with fellow Data Animators that I’m about to rave about a brilliant HBR article by Donald A. Marchand and Joe Peppard.

Titled Why IT Fumbles Analytics, with the tagline

Tech projects should focus less on technology and more on information

the article argues that analytics is all about information – the information that emerges from domain experts interpreting data – and, as such, analytics projects should never be treated as regular IT projects. Fantastic.

Marchand and Peppard have studied more than 50 international organisations in the course of their research, and assert that those who are making the most of their data are doing so by focusing on the “I”, and not the “T”, of IT i.e. by putting the technology aside, and focusing instead on the information, these analytics leaders are gaining competitive advantage. This is the realm of the Data Animator; bringing data to life, and deriving real information, understanding and knowledge from it. There are so many quotable passages in the text, that I could reproduce large chunks verbatim – if you have more than a passing interest I implore you to go and read the full text. A few of the highlights for me are:

…rather than viewing information as a resource that resides in databases—which works well for designing and implementing conventional IT systems—[best practice] sees information as something that people themselves make valuable.

The idea that information emerges from the audience is a real challenge to Cathedral-style organisations; for them, democratic data access is uncomfortable, and somehow dangerous.

The reality is that many people—including managers—are uncomfortable working with data. Any information-based initiative must acknowledge that. It must place users—the people who will create meaning from the information—at its heart. It should challenge how they do or do not use data in reaching conclusions and making decisions, urging them to rely on formal analysis instead of gut feel. And it should question their assumptions about customers, suppliers, markets, and products.

Those are my highlights of key phrases, and my take on them is:

  • “… uncomfortable working with data.”
    Yes, because it makes demands on people to think – and this is often uncomfortable ground, especially in large process-oriented organisations
  • “…challenge how they do or do not use data…”
    That challenge is often lacking; I’m ashamed to say that I have sidestepped it from time-to-time, and it has risked analytical project results on every occasion
  • “… formal analysis instead of gut feel.”
    There is always room for a little gut feel, but it should guide intelligent questioning…. through formal analysis; it’s the hypotheses-forming, evidence-gathering cycle
  • “… question their assumptions…”
    Which requires us to accept, identify and name our assumptions; something we are all guilty of ignoring

Then they take on the data world that most of experience:

Initiatives designed to extract information from existing systems or new sources of data must acknowledge how messy—and complex—that process is.

and

The steps of sensing a potential problem or opportunity, deciding what information is needed, and then gathering, organizing, and interpreting it occur in cycles.

Wow – the IT crowd isn’t going to like those two (again, the highlights are mine). They’re a hard slap of reality. I’m all for formal data management, as you will see from other posts here, but however good an organisation is at collecting collating and curating data, there is always more – and an ever increasing volume of more – outside its boundaries. As that external data becomes easier to access, and of greater value to use, the need for tools and techniques which can integrate unfamiliar data sources grows ever-more important.

Then there is a nod to my “known unknowns” soapbox in:

At one moment a manager will need data to support a specific, bounded decision; at another he’ll be looking for patterns that suggest new business opportunities or reveal problems. He must be able to build both kinds of knowledge.

Here, I would suggest that “… a specific, bounded decision…” is a “known known”, and that “… patterns that suggest…” are “known unknowns”.

And then, possibly the best summary of analytics I have read (I will be using this one!):

Analytics projects succeed by challenging and improving the way information is used, questions are answered, and decisions are made.

Followed by sage advice for anyone engaging in analytics, and looking to move beyond “known knowns”:

Avoid being bounded by easily accessible data and systems, which are based on particular assumptions and logic about how the business should be run.

OK, enough – I’m quoting large chunks like I said I wouldn’t. I will, however, steal their closing paragraph to finish off this piece. Please go and read their original.

Improving how businesses extract value from data requires more than analytical tools. It involves creating an environment where people can use the company’s data and their own knowledge to improve the firm’s operational and strategic performance. In this new paradigm, the manager’s priority is to make discoveries that could benefit the organization and identify unknowns that could put it at risk.

So go discover some unknowns yourself.

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