Should Analytics Be Focused on Small Questions Versus Big Questions?
Should the analytic resources of your company be focused upon small questions or big questions? For many, answering this question is not an easy one. Some find key managers preferring to make decisions from personal intuition or experience. When I worked for a large computer peripheral company, I remember executives making major decisions about product direction from their gut even when there was clear evidence that a major technology shift was about to happen. This company went from being a multi-billion dollar company to a $50 million dollar company in the matter of a few years.
In other cases, the entire company may not see the relationship between data and good decision making. When this happens, silos of the business collect data of value to them but there is not a coordinated, focused effort placed toward enterprise level strategic targets. This naturally leads to silos of analytical activity. Cleary answering small question may provide the value of having analytics quickly. However, answering the bigger questions will have the most value to the business as a whole. And while the big questions are often harder to answer, they can be pivotal to the go forward business. Here are just a few examples of the big questions that are worthy of being answered by most enterprises.
- Which performance factors have the greatest impact on our future growth and profitability?
- How can we anticipate and influence changing market conditions?
- If customer satisfaction improves, what is the impact on profitability?
- How should we optimize investments across our products, geographies, and market channels?
However, most businesses cannot easily answer these questions. Why then do they lack the analytical solutions to answer these questions?
Departmental BI does not yield strategic relevant data
Let’s face it, business intelligence to data has largely been a departmental exercise. In most enterprises as we have been saying, analytics start as pockets of activity versus as an enterprise wide capability. The departmental approach leads business analysts to buy the same data or software that others in the organization have already bought. Enterprises end up with hundreds of data marts, reporting packages, forecasting tools, data management solutions, integration tools, and methodologies. According to Thomas Davenport, one firm he knows well has “275 data marts and thousand different information resources, but it couldn’t pull together a single view of the business in terms of key performance metrics and customer data” (Analytics at Work, Thomas Davenport, Harvard University Press, Page 47)
Clearly, answering the Big Questions requires leadership and a coordinated approach. Amazingly, taking this road often even reduces enterprise analytical expenditure as silos of information including data marts and spaghetti codes integrations are eliminated and replaced with a single enterprise capability. But if you want to take this approach how do you make sure that you get the right business questions answered?
The strategic approach starts with enterprise strategy. In enterprise strategy, leadership will define opportunities for business growth, innovation, differentiation, and marketplace impact. According to Derek Abell, this process should occur in a three cycle strategic planning approach. This approach has the enterprise doing business planning followed by functional planning, and lastly budgeting. Each cycle provides fodder for the stages that follow. For each stage, a set of overarching cascading objectives can be derived. From these, the businesses can define a set of critical success factors that will let it know whether or not business objectives are being met. Supporting each critical success factor are quantitative key performance indicators that in aggregate say whether the success factors are going to met. Finally, these key performance indicators derive the data that is needed to support the KPIs in terms of metrics or the supporting dimensional data for analysis. So the art and science here is defining critical success factors and KPIs that answer the big questions.
As we saw above, the strategic approach is about tying questions to business strategy. In the capabilities approach, we tie questions to the capabilities that drive business competitive advantage. To determine these business capabilities, we need to start by looking at “the underling mechanism of value creation in the company (what they do best) and what the opportunities for meeting the market effectively. (“The Essential Advantage”, Paul Leinwand, Harvard Business Review Press, page 19). Typically, this determines 3-6 distinctive capabilities that impact the success of their enterprises service or product portfolio. These are the things that “enable your company to consistently outperform rivals” (“The Essential Advantage”, Paul Leinwand, Harvard Business Review Press, page 14). To optimize key business capabilities over time, and innovate and operate in ways that differentiate the businesses in the eyes and experience of customers (Analytics at Work, Thomas Davenport, Harvard University Press, Page 73). Here we want to target analytics investments at their distinctive capabilities. Here are some examples of potential target capabilities by industry:
- Financial services: Credit scoring
- Retail: Replenishment
- Manufacturing: Supply Chain Optimization
- Healthcare: Disease Management
So as we have discussed, many firms are spending too much on analytic solutions that do not solve real business problems. Getting after this is not a technical issue—it is a business issue. It starts by asking the right business questions which can come from business strategy or your core business capabilities or some mix of each.
Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Solution Brief: The Intelligent Data Platform
Author Twitter: @MylesSuer