http://www.mckinsey.com/insights/business_technology/big_data_whats_your_plan
This article has been on my to read list for a couple of months (I found this around March-April), and although it’s longer than I initially hoped, it brings up a couple of good points. The article is about a simple concept: in order to use big data and advanced analytics, one must develop a plan.
The answer, simply put, is to develop a plan. Literally. It may sound obvious, but in our experience, the missing step for most companies is spending the time required to create a simple plan for how data, analytics, frontline tools, and people come together to create business value. The power of a plan is that it provides a common language allowing senior executives, technology professionals, data scientists, and managers to discuss where the greatest returns will come from and, more important, to select the two or three places to get started.
It goes over what the writer believes to be a successful data plan, which involves three elements: data, analytic models, and decision support tools, in order to create business value (the one graphic within the article shows how the three elements come together through examples). The data portion involves assembling and integrating the data, which involves knowing what data one has, what data can be gathered, etc. The analytic models portion involves optimizing the data in order to create greater business value, such as predicting prices or finding the best combination of shipments to minimize cost. The last portion, the decision support tools, is an element that I was caught off-guard at first, but realized that it’s practically common sense. It’s about applying and implementing the analytic models so that it can make a difference within the company. The output of the data and analytic models is great, but it’s worthwhile only if it can be understood and applied to tangible business actions. For example, it’d be great to create a complex dashboard with many filters and features, but if it’s too complicated for the end user, the dashboard won’t make as much of an impact as a straightforward, non-confusing dashboard.
The article goes more in depth where it explains the challenges of creating a plan, which involves topics such as balancing time investment priorities and focusing on models that not only has high capabilities, but will also be more likely to be accepted by the end-user.
Ultimately, I enjoyed this article because it does a good job articulating points that I believe can be taken for granted, even though they should be kept in mind when using data analytics.
