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Analytics Magazine

Upgrade the Corporation: The Business Analytics Revolution

May/June 2013

Four killer competencies of analytically mature corporations.

Randy Bartlett and Girish MalikBy Randy Bartlett (left) and Girish Malik

Welcome to the Business Analytics (BA) Revolution. We live in an age of fast-paced decision-making, assorted market disruptions and information and misinformation overload. CEOs used to make about six or eight critical decisions involving the organization and products per year. Now, they make that number of comparable decisions every month or so. Meanwhile, torrents of data relentlessly rush in, generated by a digital revolution, which is characterized by hyper-connectivity and real-time decision-making. It all begs the question: “Where can we make the time to absorb this much information?” or is it another case of “I appreciate that it’s there, but I am not sure what to make of it.”

Despite all the value-add that business analytics brings to the table, companies have hardly been able to realize its true potential. Such an attainment implies incorporating more facts into decision-making, facing the information deluge from big data and more nimbly adapting to the changing environment. By upgrading the corporation, we can better integrate business analytics into “how we do business” and thereby compete on BA. This article will clarify where we are in the BA revolution, how to measure our BA maturity and what killer competencies we can realize. We hope this information will enable corporations and leaders to upgrade.

By upgrading the corporation, we mean more than just adding quants to a conventional infrastructure. We are past the point where the usual additive approach will keep up. We need a holistic upgrade. Upgrades are needed in five major areas: culture, organization, people, statistics and data. Let’s start by recognizing where we are in this technological revolution.

Business Analytics Revolution Four killer competencies of analytically mature corporations
Figure 1: Report card.

Adapting to a technological revolution

The introduction of the automobile provides a familiar example of the changes needed to fully leverage a new technological innovation. Three areas of change – culture, infrastructure and the evolution of the BA toolset – are salient to the discussion.

First, the introduction of the automobile met with strong cultural resistance, particularly from 1900 to 1930. Road fatalities, which included children playing in the street, met with outrage. Eventually, most people accepted the fatalities in exchange for the benefits.

The resistance to BA is centered on change rather than fatalities. Implementing BA requires people to change and requires a change in people. Decision-makers need to: (a) plan for their information needs, (b) learn how to better incorporate statistical results into decision-making, and (c) digest a haystack of information by judging the accuracy and reliability of the results. This can be much more difficult than the earlier state of affairs when decision-makers could rely upon their years of industry experience and the opinions of their peers to draw conclusions.

While that experience is still relevant, it has a shorter shelf life and must be tempered with up-to-the-moment information. For managers, the need is to embrace more specialization from everyone and to identify credible business analysts and business quants. They need to change their game from checkers to chess.

Second, fully leveraging the automobile required constant improvements to the infrastructure. This innovation led to the gradual development of a full-blown ecosystem consisting of roads, gasoline stations, mechanics, traffic rules, traffic signs and driver’s licenses. (In the United States, the last state to require a driver’s license did so in 1954 and five years later that state required an examination.)

Likewise, organizations need a very different infrastructure to incorporate business analytics. Corporations need to ensure that the data are put to the best possible use and feed into optimal decision-making at all levels. While data warehouses have become more accessible and user-friendly, the greatest organizational challenge is to extend the BA team. We need to include senior management, probably a CAO (Chief Analytics Officer), analytics-based decision-makers and an effective business analytics leader who can complete the QLQT (Quant Lead Quant Team). We must focus on the need for specialization and placing the right people in the right roles.

Certifications are becoming available in the United States, and they will help managers identify business quants and leaders of quant teams from the other “chess pieces.” To help with discernment, ASA launched the PSTAT® in 2010 and INFORMS launched CAP™ this year. In a similar vein, Michael Rappa of North Carolina State University launched the first master of science in analytics (MSA) degree, which emphasizes practical and soft skills.

Third, automobiles have evolved from their less sophisticated ancestors. We have moved from wagon wheels to vulcanized rubber tires and now automobiles can park themselves, navigate, monitor tire pressure and talk. BA needs to evolve too. We need to: 1) expand the tool set, and 2) adapt the tools for corporations and for big data.

Expand the tool set: We need to start by leveraging more of the tools, which have already been built for scientific applications. For example, we need to more frequently pursue the data we need rather than always accepting whatever is convenient. In many situations, we can collect and generate more applicable data by leveraging statistical sample designs, designed experiments and planned simulations. These tools enable starting with the business problems and information needs and aggressively pursuing the best information.

Similarly, quality control is seldom used outside of manufacturing even though almost every corporation tracks several KPIs and can readily apply these tried-and-tested techniques. Furthermore, there are numerous data reduction techniques, which can readily subdue big data for specific applications, yet they remain mostly idle amid the hurried excitement.

Adapt the tools: Many of our ancestral scientific tools were customized to investigate causality within the confines of small, purposefully planned samples and in a methodical decision-making environment. There was a time when we had to deliberately misapply diagnostics designed for coefficient estimation because other applications lacked corresponding diagnostics. In recent decades, researchers have enhanced statistical techniques to better cover the corporate problem mix. This mix contains more data visualization, prediction, ranking and clustering (grouping) relative to coefficient estimation, which we perform to investigate causality.

Business datasets are frequently big and they tend to be conveniently collected rather than planned. This difference emphasizes the need for validation techniques and clarity around the difference between statistical significance and relative significance. Finally, the business decision-making environment is always fast-paced and sometimes lowbrow. This environment has created a conflict between being a steward of pristine information and playing more of an influencer role.

In expanding the tool set and adapting the tools, we will inevitably improve and broaden BA applications. One key application requiring better coverage is decision-making. We need to rethink what tools should be used in making and evaluating decisions. This effort can be further facilitated by changes in leadership to make the corporation more quant-oriented and thereby widen the breadth of business analytics as a whole.

By juxtaposing the BA revolution with past technological revolutions, we glean insight into where we are now and where we are going.

Measuring business analytics maturity

Measuring a corporation’s BA maturity can provide a map of strengths and weaknesses, generating a great deal of insight. A proper measurement must be objective and include sufficient expertise. The review team should be led by experienced external business quants who can benchmark capabilities relative to competitors (low hurdle) and needs (high hurdle). At present, holistic benchmarking of any kind is difficult to find; perhaps, TGaS Advisors is the closest. Table 1 categorizes five areas to watch for measuring BA maturity: culture, organization, people, statistics and data.

Table 1: Business analytics maturity model.
Table 1: Business analytics maturity model.
(Click here to view a larger version in a separate window.)

Table 1 provides insight into several aspects of BA maturity. So what does this investment buys us?

Four killer competencies of analytically mature corporations

The rewards for BA maturity are enhanced competencies, as successfully demonstrated in the field. Figure 2 presents four killer competencies possessed by corporations that compete on analytics. The arrows indicate the desired progression, e.g. passive corporations must satisfy themselves with convenient data only; analytically mature corporations can pursue the information they need.

Figure 2: Four killer BA competencies.
Figure 2: Four killer BA competencies.

Killer competency No. 1: pursuing information. Most of the data we use is generated either in the course of doing business or through customer behavior. There are no corresponding planned business applications for this convenient data. When a new business need is identified, we scour the data encyclopedia (portfolio of data choices with background extolling their virtues) looking for “best information.” In practice, there are serious gaps. For example, one common unmet need is competitor information.

When there is a gap, we want to be in a position to do more than use the best information available as a substitute. We want the super power to collect or generate information customized to fit the business needs – proactively pursuing information. This requires that the expertise to design samples, experiments and simulations be in the hands of people who understand the business and have earned our trust.

Killer competency No. 2: quant leadership. There are times when we want to wield the power of a large Quant Led Quant Team (QLQT). This requires a business analytics leader who understands what the team can do and how it functions. We want a leader who has adequate training, practice experience, business acumen and a grasp of the soft skills. We may need to find or develop this person, who might be standing right in front of us. The natural people to identify talent are the other BA leaders: CAO (Chief Analytics Officer) and analytics-based decision-makers.

As previously mentioned, many corporations are not realizing the true power of BA. They stop short by encumbering the quant group with an off-topic “manager” possessing every thinkable skill except BA practice experience. This compromise means that the corporation receives only diluted quant expertise. This is like having a manager of finance, legal or accounting who has only two semesters of relevant training and no practice experience. The quant team needs its own overall “leader.” Otherwise, each quant is left to lead themselves, and they lack cohesion, guidance and focus. If a large quant team is managed and not led, this is a sign of three possibilities: 1) the corporation is not ready for BA; 2) the corporation has more quant than it knows what to do with; and/or 3) the corporation can not identify an appropriate leader.

Killer competency No. 3: quant involvement. Statistics is a specialization. It is challenging to blend domain knowledge with the BA expertise needed to provide greater depth and breadth of analytics solutions. Integrating BA into the business requires a high degree of interaction between the quants and other professionals, such as analytics-based decision-makers. By fostering more interaction between quants and decision-makers, and providing leadership, we can expect greater synergy. This implies that quants present their own work, without an interpreter, and that they review analytics-based decisions and all analytics information flowing into decision-making.

Business analytics is, in sum, a science as well as an art. What we need is a set of deft craftsmen who can then renovate and help their corporations build analytics capabilities, achieve the desired mission and vision and, most importantly, be more customer and competitor ready.

Figure 3: Moving up the BA maturity curve by developing killer competencies.
Figure 3: Moving up the BA maturity curve by developing killer competencies.

Killer competency No. 4: analytics-based decision-making. Finally, the most powerful of the BA superhuman/supercorporate capabilities is to make smarter decisions by basing them upon facts. This requires sophisticated decision-makers who are properly supported. These decision-makers are driven to plan their information needs, are capable of discriminating differences in information quality, and are willing to wade into an ocean of information and misinformation if that is what it takes. They live in a fast-paced, decision-making environment, and they strive to incorporate analytics into their decision-making as much as possible while maintaining the pace. They understand what qualifications go into a good quant. Finally, they welcome direct interactions with quants and receiving feedback on better planning for, and leveraging of, the information.

By pushing their way up the business analytics maturity curve, corporations can develop killer competencies.

Conclusion

“The world hates change, yet it is the only thing that has brought progress.”
– Charles Kettering

Much as other technologies have revolutionized the way we live, business analytics is transforming the way corporations compete. To harness BA’s true potential and make the critical transitions from data to information to insights to analytics-based decision-making, corporations need to adapt. The key is to recognize and accept the current BA maturity and plan how to holistically change, or rather upgrade, the corporation in five main categories: culture, organization, people, statistics and data.

The first steps are to provide the mandate and enlist the leaders who can make it happen. The likely change agents include a Chief Analytics Officer (CAO) watching the horizon, a business analytics leader in the trenches with the quants (QLQT) and analytics-based decision-makers. Some corporations can leverage their Six Sigma team, which is an accelerator for change. Measuring BA maturity will provide a blue print listing the areas needing attention. These upgrades enable competing on business analytics using the four killer competencies listed above: pursuing information, quant leadership, quant involvement and analytics-based decision-making.

Randy Bartlett (Randy.Bartlett@BlueSigmaAnalytics.com), Ph.D., is the author of “A Practitioner’s Guide to Business Analytics” and a business analytics leader with Blue Sigma Analytics. He has more than 20 years of experience providing and performing advanced business analytics. His activities include organizing analytical resources, reviewing advanced analytics and providing analytical advancements.

Girish Malik, PMP, MBA, is an IT services professional with more than 10 years of experience across program management, client relationship management and technology consulting. He has managed customer expectations and delivery teams for BI and analytics projects across domains ranging from financial services and retail to telecom and the public sector.

References

  1. Bartlett, Randy, 2013, “A Practitioner’s Guide to Business Analytics: Using Data Analysis Tools to Improve Your Organization’s Decision Making and Strategy,” McGraw-Hill (ISBN 978-0071807593).
  2. Bennis, Warren, 2009, “On Becoming A Leader,” 4th Edition, Basic Books.
  3. Lewis, Michael, 2009, “The Man Who Crashed The World.” Vanity Fair, August.

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