Modern organizations are over-complex and hyper-connected, traditional command-control, gut feeling decision-making style is losing its steam, analytics is at top agenda of CIO in any forward-thinking business today, however, what is needed to make Analytics as much as part of the corporate culture?
- Try to understand the audience you are dealing with before you try to build an analytics program. Leaders got where they are doing what they do. When you use analytics and data and facts to contradict what people "know", you could run into a situation where your audience feels that if facts are contradicting accepted belief and theory, then maybe the facts are incomplete/wrong. It is so easy to lose executives in the technical side as well as in the politics and multiple truths. To make analytics pervasive within an organization, analytics needs to be used as a way of reducing, not eliminating, or explaining uncertainty in the daily decisions.
- Then try to get the team on the same page with the "why, what, how" thinking to ensure that the point of analytics is not just to run the number, create new algorithms, etc., but to provide new and unique insights that drive decisions and strategies. Everything needs to be framed in terms of how business objectives/goals will be achieved. Few get excited about multivariate analysis, linear regression or star schemas. They do get excited when you talk about ROIs, efficiency gains, market-share, and ability to select the most profitable customers, managing risk, and identifying new profitable niches. Your task is to demonstrate how analytics can deliver those benefits. Only then will you be able to talk about buying those expensive tools that take you beyond reporting.
- “Think big, start small" is critical to advancing analytics in most organizations. The small successes and efficiency gains can position you for advancing to the next levels of analytics (forecasting, segmentation analysis, predictive and optimization). The long term plan now has credibility because of those successes and there is trust that the employees will individually benefit which leads to the company benefit. And then the funding you need will be easier to attain as it will be the support to push forward.
- Make analytics more accessible. The problem with traditional advanced analytics solutions is that they are not accessible by decision makers. The key is to understand the business processes. Think like the user and then develop back to the sources. Analytics uses algorithms, reports use visualization. Visualization should not be a static report. It should be an interface for both steering analytics and seeing analytical results. The difference in reporting and analytics is like this: Reporting focuses on the known and the past; analytics focuses on the unknown both past and future.
- Build a 3-tier Analytics model of "Visibility", Understanding", and "Guidance". Almost all stats methods and analytics business questions can be broken down into one of those three groups (or at least a combination). Make reporting the base level type of analytics. "Reporting" would fall into the Visibility category. It answers all questions about "what is the state" of something, or "where are we at" with something, or "how much" of something.... It doesn't give you deeper insights ABOUT anything or PRESCRIBE a recommendation which analytics should fill the gap. Also, analytics is not about getting to a 100% certain outcome. But rather it needs to be used as a tool for reducing uncertainty in business. Whether that uncertainty relates to possible strategies, operational processes, or improved decision making.
- A truly analytical organization not only measures the right things at the right time to facilitate quality decision making but also measures for the sake of improving the business not punishments or rewards. However, the causes companies to shy away from analytics are "technical complexity".
Ideally, the handy analytics tools are like GPS devices, permeating business's daily life, tell you where you are, how you get there, what is your heading, speed, and final destination. The good data visualization is central to Analytics. The logic steps also help embed analytics into business culture seamlessly.