Executive Edge: Leadership – The real game changer for using data analytics
By Bill Gossman
Small- and medium-size businesses (SMBs) are no different than their larger counterparts in recognizing the growing importance of data analytics. Some view the cost as prohibitive because data analytics, and specifically predictive analytics, is rooted in statistics and sophisticated mathematics that require special skills or expensive outsourcing. Others are reluctant to embrace data analytics because they question whether data can be strategically analyzed to create actionable responses rather than fishing expeditions that may or may not yield anything of value.
The business reality is that these positions have very little to do with data, data science or technology. It is a lack of executive guidance and direction – in other words, a people problem. With strong corporate leadership and managerial judgment, an effective analytics strategy is likely to emerge.
The Analytics Quandary for Leaders
An analytical focus can be an ultimate game changer for companies, which is why the concentration should be on established objectives rather than amassing data that may or may not be relevant. It is at this point that leadership finds itself at a crossroads of two conflicting scenarios, both of which have drawbacks.
One scenario takes the form of a “wait and see” approach before deciding if investment in analytics offers an acceptable return on investment – a risky approach. Delays can impact marketplace positioning and create an edge for competitors. The second scenario focuses on the here and now of tactical day-to-day operations in lieu of a broader business strategy that leverages data.
An underlying assumption on the part of upper management that applies to both scenarios is erroneous. The assumption: Analytics is only possible with big and costly infrastructures that require significant management and resource commitments. The truth is significantly different. While data is certainly a huge entity, size in this case is not as important as determining relevance. The idea is to keep matters manageable. The best way to do that is to focus on the low-hanging fruit of analytics – the data that is most easily accessible and valuable for establishing achievable goals.
The best-known example of this type of leadership is graphically displayed in the film “Moneyball.” Analyzing data about ballplayer skills that other general managers overlooked enabled Oakland’s Billy Beane to become one of the first baseball executives to leverage its value in a much more stringent, scientific way – a classic example of motivational leadership in analytics.
Leadership’s Data Usability
Cost has and always will be a factor in analytics decision-making, but thanks to decades of technological advancements, data analytics are now available at prices that small and medium-sized businesses should be willing to consider for the most obvious and important reason: positive impact on the bottom line. A report published by The Economist describes a “strong link” between a company’s financial success and its use of analytics. A survey released by the publication noted that 53 percent of respondents in its strategic group reported a higher financial success rate than their peers who did not take advantage of strategic data management (Briody, 2011). The report indicated that what it called “data collectors” (those who do not fully leverage the data at their disposal) and “data wasters” (under-users of collected data) are at risk of coming out on the short end financially.
There is, however, one other obstacle that is not so readily apparent. “The biggest barrier is the assumption that analytics is a data science and technology problem when it’s really a leadership and managerial problem,” says Florian Zettelmeyer, director of the data analytics program at the Kellogg School of Management, Northwestern University. “The managerial class has to understand the problems that need to be solved and how analytics can help solve them.”
When leadership fails to implement a decision management strategy and convey its importance throughout the company, it unwittingly bypasses a major growth opportunity. Data constantly flows into and around an organization. Leveraging its specific components requires analytical execution and deployment in areas of maximum benefit by a committed staff. This is the essence of opportunity management. Effective decision management uses analytics to achieve better outcomes from customer interactions. The value, of course, depends on how well all these factors are applied.
Recognize the Unrecognizable
Even limited analysis of minimal levels of data can make a significant difference to the bottom line, especially when leadership clearly establishes goals beforehand. The objective for data analytics is not quantity of data but quality of outcomes: e.g., solving business problems, generating customer information or analyzing market conditions. Results should be sustainable, which means placing the emphasis on strategic instead of tactical. Analytical approaches tend to vary depending upon the size of the company. Larger corporations generally engage statisticians in addition to a business intelligence team as part of their overall strategy. Most SMBs cannot afford to contract with statisticians, but have other avenues to maximize business intelligence, most notably automated programs. Some add BI expertise to their staffs, but are uncertain what their next big step should be. At this stage, leadership has to be prepared to resolve inevitable internal conflicts about analytical direction.
What is needed is a strategy to integrate analytics into decision management beginning with two very simple and achievable practices: focus and common sense. “You must have a long-term plan and a strategy that works with it,” says David Hufnagel, chief operations officer and vice president of Congressional Federal Credit Union (CFCU). “If you don’t work in concert with the plan, it becomes inoperable with no results.” The credit union has relied on automated analytics for the last three years to drive a proactive outreach to its 45,000 members. “It’s not about being unique with each individual,” Hufnagel says. “It’s about identifying what is relevant to our members.”
The CFCU experience is an example of focus and common sense for maximizing the value of data analytics through technological innovation. The idea is to have the technology focus on data that directly relates to company goals. There is no reason to go data fishing in the hopes of snagging information that may be vital someday. This isn’t to suggest that such effort has no value. Rather, it’s more about priority, available time and resources.
Clarify ROI and Analytical Options
Sometimes return on investment from automated analytical software is difficult to establish, which is why it can be the biggest barrier to a go/no-go decision. Nonetheless, analytics success stories are everywhere. Leslie Deich, a professional financial services specialist, says she found the technology to be extremely effective in detecting consumer fraud, which helped protect GE Consumer Capital, Genworth Financial and Fannie Mae from what could have been significant losses. “We needed to find fraud patterns in the data and this made everything easier because it was able to locate the patterns in very quick fashion,” Deich says. “If you don’t know the data or are unfamiliar with it, this will help you see the relationships.”
CFCU’s David Hufnagel relies on opportunity management “to get a 360-degree view” of its membership. “It’s meant better targeting (and) now we’re seeing greater acceptance of offers,” he says. “It allows for greater efficiency with each campaign.”
These experiences demonstrate the ability of companies to achieve their desired outcomes through programs that maximize qualitative and quantitative analytics without having to worry about programming and algorithms. “Analytics are not disembodied truth,” says Northwestern’s Zettelmeyer. “They answer those questions managers are most concerned about.”
Pathway to Opportunity
Leaders who take a strategic and proactive initiative when it comes to data analytics set the stage for the successful and actionable use of information. The starting point for this type of initiative is a focus on management and oversight.
Analytics may be among the most powerful tools for executive decision-making and business management. They are at their best when flexible, focused and applied in tandem with decision management best practices that can be augmented and enhanced as needed. Executives and managers have a pathway to opportunity management regardless of corporate size and the ability to streamline business flow to improve growth opportunities and increase profitability if they are willing to take advantage of it.
Bill Gossman (firstname.lastname@example.org) is president of Advanced Software Applications (ASA Corp.) in Pittsburgh, Pa. ASA provides analytical and decision solutions that help businesses grow revenue, improve efficiencies and mitigate risks.
- Briody, Dan, 2011, “Big data: Harnessing a game-changing asset,” a report by the Economist Intelligence Unit, sponsored by SAS.