You and the 10 Stages of Data Readiness for Analytics

You and the 10 Stages of Data Readiness for Analytics

You and the 10 Stages of Data Readiness for Analytics
You and the 10 Stages of Data Readiness for Analytics
The term “data readiness” means different things to different people, especially with respect to analytics. In this article we’ll take a high-level tour through various stages of data readiness in order for you to evaluate what’s right for you and your enterprise when engaging a new analytics project.
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If you total up all the stages with which you feel proficient, a score of 7-10 means you’re “ready” for analytics. How do you stack up?
Stage 1: Business Case. The enterprise must first have an idea of what it is that they are trying to measure: does the enterprise have a clearly defined use case for the use of analytics? What business problem needs to be solved? Unleashing analytics tools on data without goals usually will result in achieving little.
Stage 2: Infrastructure Readiness. The enterprise must be prepared to invest in the infrastructure to easily access and analyze data. There also should be an investment in analytical software tools that will enable these tasks. Further, the enterprise needs to make an investment in training, recruiting, and dedicated staff possessing the technical skillsets to leverage the data.
Stage 3: Cultural Readiness. The enterprise thought leaders must be ready to make data-driven decisions as opposed to just using intuition or “gut feeling.” The enterprise should have internal processes and appropriate culture to embrace insights exposed by the analytics team.
Stage 4: Operational Readiness. The enterprise should be ready to measure success and identify KPIs and other metrics to evaluate progress towards the organizational goals with analytics. Further, the enterprise should develop a clear propagation plan, i.e. a way to bring analytics insights to those who need them.
Stage 5: Ingest. If you’re weighing in on your state of data readiness, you’ve most likely recognized the competitive advantage of data to your enterprise. Your enterprise undoubtedly has a wealth of data, but it may be in formats that are inconvenient for analytics. Further, you’re eager to take advantage of your data assets to gain useful insights, the question is whether you have all the data you need. You need to set up your data ingest pipeline so you can work with large, open data sets and combine your internal data with external information to make your insights even more meaningful. Satisfying the ingest stage means you’re ready to build your data lake with the data you need, ready for analytics.
Stage 6: Analyze.

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