The first of these problems is about how businesses use people. All too often, data teams are seen as a cost, because organizations tend to answer problems by hiring more people. Instead of hiring people to do clever new things and create new value, they end up hiring people to just manage the same old inefficiencies, and to keep the wheels turning in the same old databases. Those people become ‘human middleware’ – a cost – rather than driving towards the goal of delivering business value.
There’s a second problem in terms of technology. In the modern data stack, it seems like new categories of needs in data pop up very regularly, and each one requires a new best-of-breed tool to deal with it. It’s easy to start believing that our businesses will perform better if we just adopt more technology. The problem is not derived directly from the technology itself, but is from our expectation that technology will solve problems and create efficiencies. On its own, technology won’t do that.
Thirdly, most existing data consumption still happens in a reactive manner – in business insights reports, or within a tool, and is approached in a centralized way by one data team, rather than by the teams who can act on it. Using data in this way doesn’t inspire action to be taken.
More info: Setting SMART Goals
