An earlier post noted that the default approach to investing in Business Analytics is via a top-down, commercial software-centric, project-based, formal initiative. Two of the more perverse things this approach provides to its organisational sponsors are an insurance policy and an internal marketing campaign.
The more money an organisation spends on software, the more it sees itself as entitled to blame the software vendor (or implementation partner, or advising consultancy) if the implementation falls short of expectations. The organisation may talk of being ‘misled’ by the vendor, of having promises unfulfilled, perhaps of being a ‘guinea pig’ or of having been sold ‘vapourware’. A relationship once characterised by mutual trust and responsibility starts sounding more like a story of a ruthless aggressor preying on an innocent victim. In any case, the money the organisation has spent on licences, implementation services and maintenance will have transmuted into an insurance premium. The organisation claims against its new insurance policy by shifting all blame for failure to the vendor and demanding ongoing unpaid services as compensation.
The second thing spending money with a commercial vendor tacitly ‘purchases’ is internal marketing profile. A sponsor who successfully gets up a large business case for Business Analytics and spends up on commercial software also gains an internal reputation boost: as a leader, a visionary, a change agent, bold, perhaps a risk-taker. All of these things may indeed be true. Nonetheless, they don’t need to be true. The reputation spoils come from affiliation with the initial purchase, not from achieving eventual success with it. And here it’s worth noting that, should that success prove elusive down the line, there’s always insurance.
About usAnalyst First is a new approach to analytics, where tools take a far less important place than the people who perform, manage, request and envision analytics, while analytics is seen as a non-repetitive, exploratory and creative process where the outcome is not known at the start, and only a fraction of efforts are expected to result in success. This is in contrast with a common perception of analytics as IT and process.
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