In this step we develop one of the most important interim products for our decision model; the analytic user profile. A profile is a way of classifying and grouping what the user community is actually doing with the analytic information and services produced. We develop a quantified view of our user community so we can evaluate each platform or tool for optimization quickly and produce meaningful results aligned with usage patterns.
Continuing the series on Nine Easy Steps to Unlock Breakthrough Results, we now assign relative weights to each of the critical capabilities groups for each operating model uncovered earlier. This is done to assign the higher weightings to capability groupings most important to the success of each model. Having the quantified index means we can evaluate each platform or tool for optimization within quickly and produce meaningful results.
A deeper dive into defining the critical capabilities used across the four operating models discussed in an earlier post (Big Data Analytics – Unlock Breakthrough Results: Step 2). Describes each of the baseline capability groupings and a high-level taxonomy to be used in the decision model.
This post is part of a larger series to provide a detailed set of steps you can take to unlock breakthrough results in Big Data Analytics. This step addresses identifying the type and nature of the operating models used within the analytic community along with the most important capability each demands.
You’ve made the big data investment. Now it’s time to realize value. This series of posts is going to provide a detailed set of steps you can take to unlock this value in a number of ways. As simple use case I’m going to address the perplexing management challenge of platform and tool optimization across the analytic community as an example to illustrate each step.
Nine easy steps to unlock breakthrough results in Big Data Analytics which can be performed concurrently by both business and technical professionals working together to arrive at the suggested platform and tooling optimization decisions.