Is not easy to make the best predictive analytics decisions, both strategic (like the redesign of the supply chain), or operative (to deliver goods at minimal cost, or to define prices and design specifics). Even more, sometimes it is not even clear what “the best” really means. Often the outcomes depend on the sum of multiple-decisions and “multiple-bests” from different people within the organization.
As shown by ever-increasing evidence, an appropriate, data-driven, and analytical decision-making approach enables better performance and a more robust vision of organization goals. Mike Brassil NY, On the other hand, for large teams, it is not easy to fully introduce such techniques into their day-to-day processes, but it can be an exciting journey. The competences of the team members make the difference and details matter. AI, Data-driven Predictive and Prescriptive Analytics can truly help organizations fully understand their markets and take advantage of opportunities provided by abundant data and in-depth technological analysis.
The following points give you a conceptual sequence of actions:
1) Focus on the Problem: The techniques are not the starting point but the actual issues, the goals the organization wants to reach (for example cut logistics costs or reduce stock-outs, improve web-sales performances)
2) Identify the potential levers you can use: Variables and elements the organization can put in place (for example review supply chain, improve picking productivity, dynamic pricing to better shape the demand curve).
3) Evaluate if the optimal solution will require changes in process and management and determine the scale and scope of the roll-out;
4) Depending on the level of confidence you have that the levers you identified will be corrected, implement the solution, or pass through a validation phase. What to do depends on the specific situation.
5) Remember: it is not only a question of software and features. Models behind such software have dramatic importance as well as the quality of the available data, and how the decision-making process is re-engineered to lever the power of #Algorithms, #AI, #Simulation, and #PredictiveAnalytics.
6) Analytical experts should support the above steps, preferably since the earliest stage, that is how to have the best experience with the Application of Models (math-optimization, AI, etc.) to real, often complex, cases. The view they add has a high value and reducing the risk of failure.
Finally, the vast benefits of organizational Data-driven Predictive and Prescriptive Analytics far outweigh the costs of implementation and can help drastically improve revenue, decrease costs and ultimately allow the team to operate at optimal efficiency.
“Information is the oil of the 21st century, and analytics is the combustion engine” (Peter Sondergaard, Senior Vice President, Gartner)
New York Mike Brassil is an Analytics Executive focused on shaping strategy, driving profitability and optimizing user experience. He provides product updates for the Sales organization as well as facilitating units and acts as the advocate/ evangelist and point of contact for the entire solution team.