Our thoughts on the future of business
3 Stages of Decision Analytics Maturity
Jonathan | March 21, 2018
Enterprises increasingly have a mandate to become more “data-driven” across every function, but what does that actually mean? Data is only valuable if it is used to change the course of action, be it for a single customer or an entire company. Therefore, being data-driven means that data must directly impact decision making processes.
Clearly, applying data can improve decision outcomes, and companies will win by leveraging it across every critical decision they make.
At SnapStrat, we have found 3 basic levels of maturity in data-driven decision-making. Over time, all high-value decisions will need to make use of intelligent analytics and machine learning. As you look at your company’s critical decisions, assess how they currently stack up against the maturity stages of decision analytics in the table below – and what can be gained by leveling up:
This maturity model can be used to assess any type of strategic decision, be it around customer, product, network, portfolio management, etc.
Let’s take an example decision, “How should we allocate our portfolio spend to different projects?” All companies make this decision, and there are typically a wide set of criteria such as ROI, risk, dependencies, strategic alignment, etc. which stakeholders would like to use to select which projects will be funded.
A descriptive portfolio management tool is able to show how projects have been performing. It helps you react to issues as they arise, but doesn’t help you see them coming, nor does it give guidance on how to solve them. When evaluating proposed new projects, a descriptive decision support tool provides no assistance on determining how they will perform and where we should invest. There is no consideration of decision criteria or business constraints.
Best Use: Portfolio monitoring and intervention
A predictive portfolio management tool helps you evaluate probable outcomes based on past performance of similar projects. For example, it could look at staffing mix, project attributes (e.g. technology used), duration, risk, and function focus, and predict if the project will be on-time and on-budget based on correlating with results from similar projects. This gives an organization a much higher degree of confidence in whether a project will achieve its projections. However, it does not consider different strategic criteria, dependencies, resource/staff constraints, and other critical factors that will ultimately impact portfolio success.
Best Use: Improving the accuracy of project estimates and business cases
An intelligent portfolio management system adds two important elements to the predictive tool: 1) it introduces the concept of accessing the decision criteria that we want to use to determine a solution, and 2) it introduces an optimal solution based on those criteria and any relevant system constraints. Advanced intelligent decision systems can also use machine learning to improve the decision over time by continually testing and improving the premise under which the decision was made.
Best use: Strategy-driven decision making
It is only when we begin to apply intelligent decision analytics that we can truly begin to link strategy and execution in today’s rapidly-changing business climate. While descriptive and predictive analytics can improve the outcome of a decision, there is far greater incremental value in making the right decision. To do so, a proper data-driven process requires intelligent decision analytics.
Moving up the levels of decision analytics is a process that requires both intelligent support tools and effective change management efforts. Sequencing is important: start by maturing decisions that are both high-value and have forward-thinking stakeholder to ensure that early efforts achieve visible outcomes.
SnapStrat’s analytics platform helps stakeholders bring the power of data science and machine learning to their most critical decisions in an intuitive, user-friendly way. Contact us at email@example.com to learn more. You can follow SnapStrat on LinkedIn here.