A: There are four main challenges related to prescriptive analytics. The first issue companies experience is trying to do prescriptive analytics without a strong foundation in descriptive and predictive analytics. For example, a company might want to expand their product line. If they haven’t used descriptive analytics to understand their current revenue, or predictive analytics to establish a goal, prescriptive analytics will be unable to recommend what steps the company can take to viably and successfully add more products. You can’t predict or prescribe future action if you don’t have past data on which to base your predictions or prescriptions.
The second challenge companies face is identifying the right business problem to solve. There are multiple problems you could solve, but you have to define which problem will be the best to focus on to achieve the optimal results. Once you've defined the right problem, then you can go back and see what other variables are involved and optimize for those particular variables.
The third challenge goes hand-in-hand with the second. A company has defined the problem they need to solve, but they are not aware of the variables involved with that problem. For example, when developing a pricing strategy, a company may forget to include the external factors like export and import cost. Neglecting to define all the necessary variables will negatively impact the whole model, as well as the whole recommendation. It will not provide the best answer, and the company will be left pursuing a course of action based on erroneous or incomplete information.
The fourth challenge revolves around having the right people and tools working with the data and analytics. Companies should hire domain experts who understand both the data side and the business side. Many companies work with data scientists who have a strong grasp of the data itself, but they are less savvy about the business problem they need to solve. Hiring domain experts with deep business professionality leads to more strategic success.
Companies also need to have high-quality data governance and storage to ensure they are working from the most relevant and up-to-date information. If they can’t trust their data, they can’t make helpful recommendations. And if they don’t have the right technology, they can’t successfully run a prescriptive model. But by working closely with domain experts to find the right solutions and investing in their data, they will have a strong foundation to begin optimizing their decisions.