At DecisionPath Consulting, we have advocated for nearly a decade that business intelligence – which encompasses business analytics – should be seen as a profit improvement tool and a potential source of competitive advantage. This article looks at the opportunities – and potential pitfalls – associated with investing in business analytics and also outlines steps you can take to avoid those pitfalls and optimize your investment in analytics.

What is Business Analytics?

Simply put, business analytics – or “analytics” for short – is a term for data-based applications of quantitative analysis methods in use in businesses for decades. In the mid-1980s, I read “Quantitative Methods in Management,” which was written in 1977. There are dozens of books that apply various quantitative analysis, operations research and discrete mathematics methods to specific business domains, ranging from sophisticated customer segmentations and predictions of customer lifetime value to demand forecasting and supply chain optimization. So the field of analytics, per se, is not new. Rather, tried and true quantitative analysis methods have been implemented as packaged software applications that can be leveraged to build a wide range of company-specific analytical applications that address common business challenges.

Business Analytics and BI – What’s the Difference?

We define business intelligence as the use of business information (data) and business analyses to support business decisions in the context of core business processes that drive profit and performance.
BI has always been about analysis, and business analyses come in a wide range of types and uses, from simple analyses such as accounts receivable aging reports to the sophisticated anti-fraud analytics used by major credit card companies. Our focus for this article is on the BI subcategories advanced analytics and predictive analytics – which we will refer to as business analytics or analytics.

Business Analytics Opportunities

There are a large number of potential opportunities for leveraging analytics to create competitive advantage – and ultimately to drive profit improvement. Analytics can be used across organizations for such purposes as:

  • Customer segmentation.
  • Category management.
  • Risk analysis.
  • Inventory optimization.
  • Demand forecasting.
  • Sales trend analysis.
  • Statistical process control.
  • Cash flow forecasting.
  • Market analysis.

Business analytics are essentially a toolkit that sophisticated business analysts can use to glean insight regarding a wide range of business decisions in different parts of companies – all with a goal of increasing revenues, reducing costs or both. And while these opportunities are many, so are the potential pitfalls.

Business Analytics Strategy: A Case Study

We recently worked with a company, which we’ll refer to as “BLDR,” to help develop its analytics strategy and roadmap. BLDR is a publicly traded leader in its industry, with more than $2 billion in sales. The goal of its analytics initiative is to enhance its competitive advantage and thereby increase profits and its stock price.

To learn more about business analytics, BLDR turned to leading BI/analytics software vendors and consulting firms that provided various proposals to sell/license BLDR various combinations and permutations of:

  • packaged BI applications for purposes such as sales analysis, pricing analysis, marketing analysis, financial analysis and supply chain/order analysis;
  • packaged BI platform tools;
  • packaged multidimensional analysis technology;
  • packaged advanced/predictive analytics software;
  • pilots of an advanced/predictive analytics application; and
  • consulting services and training.

The vendors and consultants’ bids were diverse, with prices ranging from $700,000 to more than $4 million. So how should BLDR sort through its strategic options for deploying analytics? We will use the BLDR situation and a rather instructive proposal from Vendor X totaling approximately $3.5 million to illustrate some of the strategic choices and potential pitfalls associated with enterprise deployment of business analytics.

Potential Pitfalls

Pitfall #1: Lack of Clarity Regarding Investment Hypothesis

The term “analytics” is becoming ubiquitous in the vendor, analyst, and consulting community. In the case example, Vendor X proposed that BLDR invest:

  • $2.2 million up front and $250,000 annually thereafter for packaged software applications for financial, sales and purchasing analysis and the consulting services to implement the packaged applications; and
  • $1.0 million up front and $100,000 annually thereafter for advanced/predictive analytics software and the consulting services required to develop a non-production predictive analytics pilot.

Being new to BI and analytics, BLDR was unclear about the value propositions associated with the proposal. More specifically, BDLR didn’t know how to determine:

  • Whether $2.2 million was reasonable for the packaged BI applications being sold as analytics and that consisted of pre-defined reports;
  • The cost of the upfront data integration work they would have to do, which was not included in Vendor X’s proposal and which is often the riskiest and most expensive part of an enterprise BI and/or analytics initiative;
  • Whether $1.0 million was reasonable for the non-production predictive analytics pilot;
  • The extent to which the pre-defined reports and the predictive analytics pilot matched their actual business requirements for BI and analytics; and
  • How much business value would be created as a result of the proposed $3.5 million investment.

Based on these factors, BLDR did not know if the “I” in ROI was reasonable, and they had no idea of the “R” in ROI. It is a situation of information asymmetry, and all the economic risk lies with the purchaser.
Pitfall #2: Lack of Clarity about Deployment of Advanced/Predictive Analytics
Vendor X and others proposed that purchasing a large number of licenses up front would allow BLDR to rapidly change its culture by widely deploying analytics across the enterprise. While that approach could have merit in some circumstances, it is important to understand other strategic options for deploying analytics.

Simply put, analytic capabilities can be expensive and therefore risky for companies that lack appropriate experience or a supportive culture. At BLDR, a company with no enterprise data warehouse, limited BI experience, limited in-house expertise in sophisticated quantitative analysis, and no serious competitive threats, the proposal was that BLDR invest $400,000 in enterprise analytics software licenses and $600,000 for consulting. Key considerations BLDR needed to investigate included:

  • Whether enterprise licenses were required up front, or whether a time-phased approach of licensing a few desktop analytics packages (for roughly $12,000) should be deployed to test the use of analytics to create business value;
  • Which business processes BLDR would seek to improve via leveraging sophisticated analytics, and how much process change would be involved in order to create business value;
  • How analytics would be used within BLDR, e.g., by four to six domain experts who would develop the analytical insights, predict the improved results and then disseminate suggested process improvements to create value, or by a wider population who would need tool training as well as training in quantitative analysis; and
  • Whether development of the non-production pilot could be accomplished more economically by using an independent consultant or academic with relevant domain expertise.

The proposed approach was again a situation of information asymmetry, with the economic risk resting with the purchaser. While an argument can be made for a more aggressive rollout, BLDR was under no serious competitive threat, and thus a more measured, lower cost and lower risk approach could also make sense.
Pitfall #3: Lack of Attention to Strategic Barriers to BI and Analytics Success

While every company has different strengths and challenges when it comes to BI and analytics, we’ve identified five strategic barriers that can cause companies to struggle with capturing the demonstrable business benefits.   Based on BI strategy experience, we’ve noticed that:

  1. Confusing terminology can make the value of BI/analytics hard to determine.
  2. The mission and strategic importance of BI/analytics may be unclear.
  3. There may not be a clear link to business strategy and critical business processes.
  4. Upper management may not have created a sense of urgency to deploy BI/analytics.
  5. Internal people, processes and technology may not be aligned.

While not all of these barriers were present at BLDR, enough of them were, creating a substantial risk that an investment in advanced and/or predictive analytics may not deliver business value commensurate with the $1 million upfront investment.
Ultimately, there is no analytics silver bullet: Companies still have to do the hard work of figuring out how they want to use analytics to have a business/competitive impact, and they still need to build the right analytics skill sets and culture.

A Framework for Avoiding the Pitfalls

A number of strategic considerations come into play when determining the appropriate investment level and best road forward for your particular company as it seeks to leverage analytics. Figure 1 shows these strategic considerations. It is important to approach formulation of a business analytics strategy from two primary perspectives:

  • A top-down perspective that determines an analytics mission based on a company’s business strategies and the strategic importance of analytics given the way the company elects to compete within its industry or industries; and
  • A bottom-up, risk-reward perspective that identifies specific analytics opportunities related to critical business processes that impact profits (rewards), and that takes a hard-nosed look at the business, organizational and IT barriers (risks) to being able to succeed with analytics.

These perspectives are shown in Figure 1. The five colored and numbered circles correspond to five strategic barriers to analytics success.
The first barrier occurs when businesspeople don’t understand how analytics can improve processes that drive profits. The second arises when a company has not conducted a structured opportunity analysis linking specific uses of analytics to critical business processes in a way that business leaders can understand and validate. The third occurs when companies have not determined a suitable analytics mission for their company (e.g., does the company need to have industry-leading analytics that confer competitive advantages or could competitive parity or even lagging capabilities appropriate). The fourth and fifth barriers arise when businesspeople don’t accept responsibility for analytics and lead its adoption, and when IT does not align its strategy, systems, processes and people to support the business in its quest to leverage analytics to drive profits.

These barriers, and more, can be readily identified and addressed via a well-structured analytics strategy. Further, a pragmatic analytics strategy helps to build buy-in from business and IT leaders, a key prerequisite for funding. Of these barriers, the lack of an explicit analytics mission that is accepted by top management may be the most damaging.

If the company has not determined how strategically important analytics are, it is difficult to establish a burning platform, to decide what level of investment analytics merit, and to determine what degree of change to IT policies, methods and processes are in order. Further, it is hard to define an analytics strategy and investment pattern that is suitable if there is no defined and accepted mission. A mission to simply avoid impeding efficient and successful execution of the business strategy requires less investment and urgency than if the analytics mission is to create a competitive advantage. Companies tend to drive change effectively if they understand the importance of changing, and the other strategic barriers are more readily overcome if analytics has a defined mission.

Business Analytics Strategy

The path forward toward effectively leveraging analytics depends on where your company stands today. To avoid the common pitfalls, and depending on the analytics mission, your company can take the steps identified in Figure 2 (at left) as part of formulating a business analytics strategy.

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