| | JUNE 20219CIOReviewanalytical requirements. Moreover, schedule regular meetings on progress and findings to enable the business to leverage this. If your organization is at a low analytical maturity level, don't hire top-notch data scientist who are solely interested in the latest ML applications. Instead, hire analysts who can wrangle data, provide rudimentary insights and work with the business to use these insights for better decision making. Second, focus on impact instead of the most sophisticated models. Although this sounds trivial, it is easier said than done. Most data scientist are more interested in building advanced models using the latest technology then ensuring that a model is integrated in an end-to-end process with a proper process to monitor performance and maintain the model. How to focus on impact? - Regularly define priorities for Data Analytics with management team. Reviewing the impact of past and current initiatives as well as estimating the impact of potential new initiatives is a key aspect of this process.- Be realistic of the contribution of Data Analytics for the problem at hand. In some cases, e.g. re-targeting models for digital marketing, analytics can be fully descriptive. While in other cases, e.g. decline payment due to fraudulent claims, require a human to review the case. The balance between Analytical models versus human depends on the nature of the decision, the available data, the complexity of the decision etc. - Take an end-to-end view on the usage of the analytical insights. Actually capturing impact is as important as building analytical insights. E.g. predicting customer churn can help increase retention only if the organization takes actions on the prediction; a dynamic pricing model is extremely useful but only if it can be applied in the market. Third, embedding Analytics in the organization requires top-management support. They have to lead by example, foster understanding and conviction, build a re-enforcement mechanism and develop skills:- Connecting the Analytical teams with leadership teams helps to create mutual understanding of the barriers to fully capitalize on analytics. - Sharing success cases will help to show-case the opportunities of Analytics- Trainings at all levels in the organization creates awareness, conviction and willingness to experimentFinally, Analytics is not a one-off intervention but a transformational journey. Like with any transformation, it takes times and stamina to be make it successful. Or even better phrased: "There are eminently complex questions to be answered to fully utilize analytics. These issues will be resolved not by taking a conceptual approach, but by embracing an experimental approach. Those companies that take the plunge, test, reproduce their successes on a larger scale and learn from their failures could very well establish an unbeatable lead over their more hesitant competitors." (McKinsey, Carpe Data, 2017) Hiek Van Der Scheer
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