To stay competitive and innovate large organisations need to adopt Big Data, Advanced Analytics and Data Science capabilities and technologies. Ideally, that happens fast and safely with manageable costs and early business outcomes. The requirements and necessary capabilities are uncertain, though, and the appropriate technologies and implementation plan unresolvable. In that situation, many organisations jump head first into ‘build it and they will come’ out of desperation. However, technology driven adoption is in nearly all cases doomed to fail. But how do you bridge the gap of potentially significant strategic investments into something your organisation has little knowledge of and still emerging requirements? And sometimes learnings about the available new capabilities are needed first to facilitate the creative process in the product managers and leadership team. Most importantly the organisation has to move from sluggish IT as an inflexible underpinning of the business as usual to a platform for continuous innovation on which new ideas can be tested swiftly and inexpensively.
There is a way to break the above problem into a quasi-agile program of work that reduces the complexity, defers unknowables into the future yet future proofs the invest and solves business problems in the short term. While it sounds too good to be true, I have worked with customers achieving this and wrote a case study about it: Case Study: Selecting Big Data and Data Science Technologies at a large Financial Organisation. Please go ahead and read it.
While some of the details had to be abstracted to keep it anonymous, it should give an insight into a few key approaches. For example, deliberately engaging from a strategic level down towards a tactical level while deferring the technology conversations helps progress in the first phase. Regular workshops with, at the point, relevant key stakeholders allows reevaluation and refinement of the program of work as well as supporting the change management that comes with such a significant transformation.