Dataiku’s blog also recently posted an article by Lynn Heidmann, which focused on one of the oldest use cases for statistics, namely forecasting and planning. While businesses have been using computer-based modeling techniques since the 1950s, these methods don’t produce results as precise as needed by modern companies. The age of AI and algorithms allow for a wider variety of data sources.
Traditional forecasting and planning methods are also prone to unintended bias according to Heidmann. This is because of the proliferation of manual processes. Removing manual processes and decisions can allow for truly data-driven decision making.
In the article, Heidmann included results of an Institute of Business Forecasting (IBF) survey that showed 70% of respondents agreeing that AI will be the dominant technological element in demand planning. According to specialist consulting firm EyeOn, in order for organisations to get to the point where AI helps their demand planning, they should focus on the following three elements.
Emphasise data quality.
Know the importance of bringing business knowledge to data projects.
Focus not just on delivering accurate predictions, but better decisions.
Heidmann included a link to a case study on how EyeOn itself brought efficiency to its own data projects and data team in order to halve the on-boarding time of new employees using project templates and automation. The company used the lessons learned from this effort to help bring similar efficiency gains for their clients.
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