Making the most of analytics: Building a corporate culture for analytic impact (Part 2)

April 23, 2014

In this follow-up to her earlier article regarding corporate culture and analytic impact, Dr. Shawna Thayer outlines the key organizational elements a company needs to adopt when it wants to make successful use of its business analytics.

Part Two: Building a Corporate Culture for Analytic Impact

In Part One of this article, we highlighted elements of corporate culture that help drive -  as well as impede -  impact from business analytics. In Part Two, we provide tips for building an analytics-friendly corporate culture in your organization.

So what does a corporate culture that makes the most of its business analytics look like?  In our experience, business analytics drive the greatest impact in organizations that have the following:

1. Senior Management embraces data-driven decision making when setting corporate strategy. Having a leadership team that values measurement and analytics and understands how to leverage analytic insights for driving corporate strategy is a huge asset for any business. In these organizations, decisions are made with integrated and balanced analysis sourced from data analytics, research, and the experience of trusted staff. When a company’s senior leaders value all of these inputs for driving corporate strategy, top-down leadership organically drives the business culture for the remaining points below. If you do not have leadership support for analytics in your organization, you need to start building the case for analytics now.

It is our experience that the best approach to gain leadership support is to prove out the value of analytics through “Quick Wins.” Identify 1-2 business questions that could be analyzed quickly and resulting decisions could beimplemented quickly. Keep in mind the balance between analytic need and outcomes discussed in Part 1 of this article – in order to build support in a timely manner, we suggest keeping these introductory projects focused in scope but with immediate impact. Even if the impact is relatively small (e.g., analysis to generate sales lift for a small division of your business), the purpose of this work is to make a small business decision supported by data that can be an internal case study for garnering organizational support. Use the results of these small case studies to prove out the value of analytics for your leadership organization and build up to larger, more impactful, analytic projects over time.

2. Cross-functional stakeholders align on objectives and expected outcomes up front. As with most things, a little planning up front can prevent a lot of trouble later on. Stakeholders need to come to agreement early on regarding the scope of work needed, the timeline required to implement change, and the plan of action after results are delivered. Ensuring that all parties agree to the KPIs of interest as well as the ultimate audience for the work can help you take action quickly on the results.

This is a common problem across industries, unfortunately. For example, one manufacturing business developed a rich psychographic customer segmentation scheme to leverage for Product Development; however, since this segmentation was not tied to internal data it was useless for immediate Marketing executions. We have witnessed instances where ROI definitions differ between the CFO and the CMO offices resulting in recalculations and delaying implementation. Positive outcomes are challenged when, for example, Merchandising recommendations cannot be realistically executed by the Operations team due to existing contract terms.

Avoid these pitfalls and take the time early on in analytics development to align on objectives and outcomes. Although it is not always easy to convince people in your organization to take time out of busy schedules for alignment, we have found this to be a key step in implementing data-driven decisions quickly when the analysis is complete.

3. Stakeholders agree to an open dialogue regarding potentially controversial insights and commit to continual improvement. Sometimes, the greatest advantage to objective business analytics is that it provides a new perspective on a “known fact" for the business. Corporate myths can become cornerstone beliefs for decision makers, crippling the ability to think outside business norms. Companies that embrace new perspectives rather than dismissing them derive benefits early on. In the book, Denial, Richard Tedlow documents several business case studies ranging from Ford and Pepsi to DuPont and Intel that demonstrate how embracing new business insight can spur business growth. Dismissing such facts, on the other hand, can lead a business on a downward spiral.

Change Management: Making the case for change through small, successive wins

So how do you get your organization to open up to new ideas from your analysis? One strategy that works well is to create a test-and-learn roadmap for your business. Starting with the “Quick Wins” discussed above, create a plan for experimentation and measurement for incremental, continual improvement. With this approach, you can test new strategies implemented in a few low-risk markets/divisions and measure the impact to build the broader case for change. This is particularly useful if your company has a low risk tolerance or is particularly tied to traditional corporate beliefs about business drivers. Starting small, but committing to a continual trajectory offers the opportunity to manage change and ease concerns within the organization.


Even the most sophisticated and cutting-edge business analytics are worth little if an organization cannot act on the findings. By 1) striking a balance of analytic need and business outcomes, and 2) working to garner internal organizational support for analytics, your organizational culture can adapt to embrace data-driven decision making.

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