Are you in a dilemma about buying and consuming analytics?

Having an Analytics Success model is the key to improving decisiveness across the organization. But what makes for a good model?

Having too many choices becomes a distraction sometimes. It has never been easy to be an analytics buyer; there is no respite even after making that decision to go ahead with a particular toolset; the actual work only begins subsequent to that. No doubt, it’s a rigorous process to qualify different offerings available in the market. The sourcing of analytics is a project in itself, only becoming a day in life operation upon Go-Live. The most common questions that come up during the acquisition phase are: "What role does the IT and business play in acquiring analytics" and also other important questions like "Why do you think we need to look at data?" or the most difficult question: "How do we measure the ROI?” This is the narrative playing out in most enterprises today. The C suite dichotomy is evident given that the very tools that are meant to improve decision making are a reason for indecisiveness.

What your Analytics tool vendor does not tell you?

Decision making governance is an internal process within the buying organization, and by far the only foundation for a successful implementation of analytics in any enterprise. What is required is a simple log book to record certain types of decisions that are considered to have a significant impact on the P&L of the organization. And against each decision a declaration whether or not analytics tools were consulted.  This will bring to the top the areas where analytics is not contributing as expected. Most of the analytics tools in the market don’t support feature functionality to implement a decision making governance process. Of course, it’s something that can be easily coded into the ERP or CRM systems. A regular audit of decisions that are not backed by analytics will bring to the front the data capture requirements or metadata changes or genuine reasons for not taking the support of data. Of course, there will always be some business decisions that are possible based on data alone, in which case you will like to know the scope for capturing related data that can be monitored to avoid getting into such situations.

Role-based analytics is something beyond the control and influence of the vendor. After all he is either given the licenses or implements the solution. A careful study needs to be done to ensure that all users have access to only the set of data that they really need to perform their duties and take independant business decisions. Giving more data than required can lead to indecisiveness if the end user has limited decision rights.

The distracted buyer

A lot of product marketing around the analytics tools and use cases can distract buyers and they might end up buying tools that they don’t need in the first place. For example, a lot of companies have acquired dashboarding solutions for supporting their planning & budgeting processes. The automation of variance reporting is fine and reduces a lot of effort. Bare reporting tools don’t support colloborative planning, which is the biggest driver of the planning process costs. What the buyer could have chosen is a single system that supports the planning workflows, transactions and also the variance reporting automation. We can count similar examples in the big data space, which is witnessing tremendous shifts and a relatively new category for enterprise IT buyers.

The Silver lining

Bringing meaning and purpose to your analytics investment is not difficult when you have a success model. But what is an analytics succcess model?

Analytics success model is a working file or an evergreen document that outlays ways and means to achieve the desired business outcomes. For instance, if you are a QSR, you might want to improve the average turnover of your retail outlets, leading to better same store sales growth numbers. Or if you are in B2C business, you might want to improve the customer satisfaction levels or improve the customer lifetime value. There could be competing ways and means to achieve these desired outcomes.

 A good analytics success model would be a collection of all leading indicators, and reporting their interplay in attaining the business outcomes set by the management.

In summary, having an analytics success model is the key to improving decisiveness across the organization.

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