Three strategies towards effective data projects

Three strategies towards effective data projects

How to align your data science projects with business needs? Successful problem solving requires finding the right solution to the right problem.

Many data science projects do not go into production, why is that? There is no doubt in my mind that data science is an efficient tool with impressive performances. However, a successful data project is also about effectiveness: doing the right things as Russell Ackoff would write in “A systemic view of transformational leadership”.

Successful problem solving requires finding the right solution to the right problem. We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem — Russell L. Ackoff (1974)

How do you focus on your projects and make sure they will bring value to the company? Are you strategically thinking about how to bring your project to fruition?

NB: I will use golf — a strategic sport — as an illustrative analogy here.

OKR: Setting objectives that you commit to achieve

*Objectives and Key Results (OKR) *have been adopted insuccessful organisations to drive tremendous growth (Intel, Google, …). They were initially introduced by John Doerr to increase focus that produces value.

The general idea is to set Objectives *that motivate you. Imagine you are passionate about golf and next Friday there is a big competition. In the last few years, nobody won it performing well on more than 15 holes out of the 18 on the course. Setting yourself to win it is a good objective — it is specific, ambitious, and happens at a given time. You then set **Key Results *that can measure how you are doing on this objective.**

business-strategy data-strategy data-science data analysis

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