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. Your enterprise is ready to start extracting insights that will affect the bottom line. Predicting the future is no easy task. Without a rigorous process in place, conclusions seemingly backed by data can fall short in practice, costing you money. You may be interested in getting a handle on evaluating marketing campaigns, determining customer sentiment, taking a deeper look at workforce performance, etc. You’ll need a comprehensive background in statistics and predictive modeling to transform your data into reliable and usable conclusions.
Stage 7: Governance. You’ve embraced data as a critical part of your enterprise’s strategy. You regularly collect a variety of data essential to operations, but you encounter barriers to analysis including dirty, inconsistent, missing, and unconsolidated data. Perhaps the security measures of your organization are prohibitive to an otherwise straightforward collection process. Whether you need to improve speed, quality, or security of your data, you need to define a data strategy that works for your unique enterprise. Strategies range from better documentation and workforce education practices, to an entire overhaul of warehousing and analytics processes.
Stage 8: Real-time Insights. Data-driven decisions are commonplace in your enterprise. Your business is scaling, and you’re ready to move from real-time data access to real-time insight. You need to deploy tools, accessing real-time insights, freeing up more of your enterprise’s resources. You’ll need experience in algorithm design that will help transform your existing practices into a data intelligence product.
Stage 9: Big Data. You’ve built your data pipeline from the ground up. You have your pulse on real-time insights. Your enterprise has deployed a data intelligence tool; however, it’s reaching its performance limit with the amount of data you continue to collect. You need to scale. You need experience in parallel distributed computing to take your analytics to the next level.
Stage 10: Data Storytelling. Valuable new insights lose their power if the message isn’t properly communicated to enterprise decision makers. This stage of readiness requires you to have data scientists in place who are experienced in refining the results of analytics into a concise and actionable form. In a very real sense, data visualization is as much of an art as it is science at this stage. Finesse with the end result of analytics means your hard work with capitalizing on the value of your enterprise data assets is optimized.
So, what's your organization's readiness score?