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When correctly configured, ‘analytics’ adds tremendous value to companies. Having a background in this domain, I am happy to see that more companies now recognize its potential and invest resources into building tools and processes around it. Here is my take on how to approach projects that involve analytics in order to maximize ROI. My examples are from a planning perspective but observations apply to other areas as well.
Initial design is critical - Many operational decisions will have a systematic dependence on the initial analytical assumptions and it could be very difficult to change these later. It is good practice to involve a capable cross-functional team who can blend analytical and domain knowledge in design stages.
Plan for user interaction & estimate workload - Unless the goal is to manage every decision algorithmically, you will need some level of user input (often referred to as 'blending art & science'). You have to estimate how often users will need to review/revise these touch points. If the level of interaction can stay as exception management, adoption will not be an issue. Otherwise, users will be overwhelmed with the required effort and can end up developing their offline workaround solutions.
Make analytics available where users need it - If the goal is to support a decision making process, any manual effort to bring analytics from another source means lost time and potential mistakes. Data is needed at the right level of granularity and at the point of decision making.
Own end-to-end how analytics is consumed - Options could be limited due to dependencies on other systems but having direct access to how users consume analytics (both estimates and overrides) would ensure it is used consistent with behind-the-scenes assumptions driving the calculations. Without knowing these critical assumptions, some other team can design a downstream workflow that uses your estimates with unintended and possibly wrong consequences.
You will make mistakes - There is no off-the-shelf standard approach to analytics projects and mistakes are unavoidable. In most cases the design will need to take existing workflows into account, some of which are ingrained into organizational structures. Use an iterative approach and learn from your mistakes. If your planning software vendor is not offering a capability to quickly revise initial assumptions, there is a considerable risk that the end-result may become irrelevant sooner than planned.
Spreadsheets help - Blackbox approach often leads to user questions. Some of the analytical calculations will no doubt require sophisticated algorithms and it is not practical to explain every detail, but users still need guidance in terms of how underlying assumptions work. Easy access to spreadsheets (either as a tool itself or the ability to seamlessly export) will help users validate calculations and identify unexpected behavior.
Periodic calibration is a must - Embrace the fact that leveraging analytics to deliver business value is a journey. Even if there is no major change in system dynamics, you will still need to calibrate models regularly. Measure accuracy of key metrics and use these calibration efforts as an opportunity to assess user satisfaction and to identify early signals of any foundational changes that might be needed down the road.
Poorly configured analytics hinders - The ever-changing dynamics of business make analytics a critical component of planning. The alternative may not even be feasible and you may have to design workflows centered around analytical assumptions. This dependency also means that a poor effort in design or execution could exacerbate instead of helping.
Özgür
March, 2024
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