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Enterprise Architecture Matters

Adrian Grigoriu

Why big data analytics fail?

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This is a long comment for the HBR blog on "Why your analytics are failing you" that states that "the real challenge is recognizing that using big data and analytics to better solve problems and/or make decisions obscures the organizational reality that new analytics often requires new behaviors".

True, big data analytics outcomes may require change that sometimes imply new behaviours. But that means in fact, a change in the company Culture and, as we all know, cultural change is hardest to achieve. Hence, the fact that "analytics fail" at the cultural scale of change is no new "news".

And that's no different from any other larger enterprise level change. One cannot effectively change a process without changing the human activity involved and its performance.

Therefore, big change often requires cultural transformation.
The paradox is still, how should the analytics uncover anything new that the human does not program beforehand in the analytics intelligence by asking the right questions and the correlations to seek? The outcome truly depends on expectations. This looks like a hard to break vicious circle.

Even so, it is the human that interprets the results. This is where the "subjective", and with it the analytics failure, comes in. People see what they want or are programmed to see in the first place, same as the analytics systems most of the time. 

In addition, (big data) analytics outcome may need a lot of work to be presented in a form suitable for successful decision making (i.e. alternatives with business cases, pros and contras, size of effort and costs, profit projections, proposals ...).  Do analysts really do that? What is your experience?

Moreover, the analytics results may not be presented to the right level of management, that is, high enough for the kind of decision that affects culture. 

So, own organization and its culture may be a roadblock for analytics success in the first place.

Anyway, simple observation, market research and statistics may be relevant enough for most enterprises. Especially if we do the marketing due dilligence in advance and have the right business model.

Big data analytics applies really only to the companies that have a huge amount of data, perhaps Internet level companies. That is, most companies have no big data.

But why do we have to assume that there is always a treasure buried in the big data? In truth, no news maybe good news, i.e the company is doing the right thing. Is there something wrong with that?


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Adrian Grigoriu blogs about everything relating to enterprise and business architecture, SOA, frameworks, design, planning, execution, organization and related issues.

Adrian Grigoriu

Adrian is an executive consultant in enterprise architecture, former head of enterprise architecture at Ofcom, the spectrum and broadcasting U.K. regulatory agency and chief architect at TM Forum, an organization providing a reference integrated business architecture framework, best practices and standards for the telecommunications and digital media industries. He also was a high technology, enterprise architecture and strategy senior manager at Accenture and Vodafone, and a principal consultant and lead architect at Qantas, Logica, Lucent Bell Labs and Nokia. He is the author of two books on enterprise architecture development available on Kindle and published articles with BPTrends, the Microsoft Architecture Journal and the EI magazine. Shortlisted by Computer Weekly for the IT Industry blogger of the year 2011.

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