AllAnalytics recently asked its readers “What is the greatest danger spot for analytics projects?” and the results are pretty clear. Here’s a snapshot (the percentages have been pretty stable):
- Top of the heap is “Identifying the Business Problem” with over 40%
- Then it’s a close run thing between “Data sourcing” and “Putting data into action”, both at 20%
What’s interesting about this is that these problems don’t line up with the functionality in your typical analytic tool. What they do line up really well with, however, is decision modeling.
- Decision models scope and define the business problem for an analytic project really effectively. They focus everyone – business, IT and analytics teams alike – on the decision-making to be improved and put that decision-making into context by showing which organizations, goals. KPIs and processes are involved.
- Our experience is that decision modeling also really helps with data sourcing. Not directly but because it helps clarify what data you really need, what is really used to make the decision, and so reduces rework and wasted work around data.
- Finally decision modeling helps operationalize analytics, showing how the data-driven analytic can and should be used to make decisions and defining the automation necessary to make the analytic work.
So, if these problems resonate with your analytic experience, check out decision modeling as a cure:
- A white paper on Framing Analytic Requirements with Decision Modeling
- A brief for Analytics Teams: 6 Questions To Ask Your Business Partner Before You Model
- A brief for Analytics Teams: 5 Things You Need to Know Before You Deploy Your Model
or register for the matching webinar on November 15
