The value of business analytics
Most organizations have a relatively immature understanding of what “business analytics” is, let alone how it creates value.
By Evan Stubbs
The intersection of statistically based insight and the realization that information can be an asset has had and will continue to have serious reverberations in the business world. Being smarter has always meant being successful; as far back as the 19th century, analytics was already generating competitive advantage.
Success requires more than just knowledge of statistics or ways of dealing with “big data.” Execution is essential, but without a plan and commitment, little happens. Success also requires an understanding of how analytics translates to competitive advantage.
Possibly the best-guarded secret in business analytics is that, in practice, its success comes down not only to organizational culture but also to the ability of managers to successfully sell the value of analytics. As researchers such as Thomas Davenport and Jeanne Harris have rightly pointed out, overall success can often be linked to a variety of factors including organizational structure, management commitment and successful strategic planning. However, it’s often “where the rubber hits the road” that the greatest impact can occur.
Analytics is a multi-disciplinary activity: the value from insight comes not from the activity but from the execution. Often, this crosses a variety of departments within an organization – few analytics groups have responsibility for both the insight creation and the execution of that insight. Because of this, selling the value of analytics isn’t just a goal for managers; it’s a necessary criterion for success.
For many managers, this can be challenging. Despite broad interest in business analytics as a discipline, most organizations have a relatively immature understanding of what “business analytics” is, let alone how it creates value. When projects fail, it’s all too easy to point to the organization as the reason why success wasn’t achieved. Unfortunately, this is only half the picture – the harsh reality is that we, as managers and analysts, all too often carry a large portion of the blame. In an ongoing straw poll held by the author across more than 1,500 people, only a handful ever feel that they could quantify the value they were creating through applying business analytics. When even the experts can’t explain why analytics is important to the organization, what hope does a layperson have?
The value of analytics lies in its ability to deliver better outcomes. And, when it comes to selling the value of business analytics, four mistakes stand out above all others as the biggest blockers of success.
Mistake No. 1: If you can’t define the value, how do you know what it’s worth?
Picture this: You’re the person who controls the purse strings for your company. In front of you is a long list of investment opportunities, all of which have very logical and persuasive reasoning behind why they’re important. Unfortunately, you only have so much money and resources to allocate. So, how do you prioritize?
Usually, it involves looking at the ratio of economic return to investment, as well as who’s supporting the initiative. Unfortunately, far too often business analytics champions fail to capitalize on these as effectively as they might. Instead, they try to gain funding through little more than smart ideas and good intentions.
This lack of value definition may seem paradoxical given the advanced mathematical abilities of most teams – after all, these are the teams that more often than not have the best modeling abilities in the entire organization! Typically though, it’s simple oversight combined with a lack of financial domain knowledge that leads to this pitfall. While it’s a given that business analytics has the capacity to create value, it’s hard to convince everyone else of that fact without being able to define this value. And, it’s often after the fact that the team realizes how important this value definition really is.
The Solution: Make sure you define the value.
Business analytics creates value. However, the only way to communicate that value is to quantify it. Defining the value needn’t be a complex process. Some of the most important things to consider include:
• Identifying the right measures of return
• Profiling both tangible and intangible return
• Considering a range of benefits
Return can be viewed from a variety of different perspectives. Sometimes, the time needed for an investment to pay for itself is the most critical measure. At other times, the leverage created from an investment is the most important measure. Irrespective of what the appropriate measures are, every business analytics initiative should generate returns in some form, regardless of whether those returns come from cost efficiencies, margin improvements or sheer revenue growth. Capturing and defining these returns in an appropriate manner is often a pre-requisite to even being considered for funding.
Profiling both tangible and intangible returns is also important. While investment decisions often require that the project create economic returns, political support often comes from the intangible returns of a given project. Simplifying process complexity in itself may not provide enough cost reduction to justify investment. However, if it helps the process owner get home in time for dinner two nights a week, their support is almost guaranteed. Given the importance of cross-departmental teams in most business analytics projects, the leverage this support can provide can’t be understated.
Finally, good business analytics initiatives typically deliver a number of benefits. One of the biggest advantages of business analytics comes from the ability of an organization to re-apply existing competencies across multiple business problems. Modeling skills can be applied to support improved customer retention, increased revenue through cross-sell and improved returns through better debt collection. A well-designed business case identifies each of these, recognizing business analytics as the broader growth driver it is.
Mistake No. 2: If people can’t understand the value, why should they care?
It’s fairly obvious that we prefer things we can understand. Unfortunately, it’s easy to forget that everyone views things differently. While our perspective may be the most familiar, it may not be the same as the one held by the person we’re talking to. All too often analysts will get caught up in the technical detail, explaining the intricacies of one algorithm over another when all their audience is interested in is, “What’s the benefit?”
Communication breakdowns can be one of the most frustrating things in any business analytics project. A typical project cuts across multiple groups within the business, often spanning IT, the analytics team and the operational execution team. It’s almost inevitable that miscommunications will occur. If these aren’t recognized and planned for, it’s usually the project (and the analyst’s sanity) that suffers.
The Solution: Make sure you communicate the value.
The best business analytics champions are more than just domain experts; they’re also evangelists. A key skill in their kitbag is their ability to understand their audience and tailor their message to suit. Making sure their message is relevant is a critical first step. Equally as important is recognizing that people view things differently and that an effective communication strategy requires working to different perspectives.
Four of the most common perspectives are:
• The analytical perspective
• The process perspective
• The personal perspective
• The strategic perspective
Most analysts are familiar with the analytical perspective. Usually, it focuses on how the initiative will work, often describing the quantitative value that will be created. It’s the most evaluative approach and normally revolves around establishing logical relationships and applying rational persuasion. For champions interested in communicating in this way, it’s often useful to use formal presentations and other structured methods.
The process perspective usually focuses on what needs to be executed. It tends to emphasize the steps that will be taken, the governance that will be needed and the responsibilities that will go along with the new approach. Quality, execution time and the implied change are often of key interest. Much like the analytical perspective, champions interested in communicating this way often leverage formal presentations and other structured methods.
The personal perspective focuses on how the initiative will impact individuals within the organization. Of key interest is often how the initiative will make people better off, regardless of whether it’s through personal or professional benefit. Intangible value is usually emphasized along with how existing teams will extend their responsibilities. Champions interested in communicating this way often prefer more informal methods.
The strategic perspective focuses on the holistic impact of the initiative. It’s the most “big-picture” approach, usually focusing on competencies, competitive advantage, and overall systems. Often, a key focus is on how the initiative will make the organization better off supported by evidence of best-practice, linkages to strategic objectives and reference to market influences. Champions interested in communicating this way often prefer a highly interactive approach, usually involving white-boards and open conversation.
In addition to evangelism, effective business analytics champions avoid the pitfalls of miscommunication by weaving these different perspectives into a holistic communication strategy, tailoring it to suit their audience. By communicating the value of business analytics in a way their audience can relate to, they gain traction and build support. Business analytics isn’t about statistics; it’s about delivering organizational change.
Mistake No. 3: If you don’t know where you’re going, how are you going to get there?
Business analytics is a double-edged sword. On one hand, it has almost unlimited potential for re-use across multiple business problems. On the other, there’s so much it can do that it’s easy to take on more than can ever be delivered. Achieving the balance between meeting strategic goals and delivering tactical returns is challenging. Too much of one and the team runs the risk of being perceived as a group that’s always “almost there.” Too much of the other and the team runs the risk of being seen as a fire-fighting unit that’s never in charge of their own destiny.
Few teams effectively link their tactical activity to larger growth opportunities. This often leads them down a path of operational firefighting and constrained investment, often because they can’t demonstrate a clearly defined growth path. If left unchecked, the team eventually suffers rising rates of churn because of general frustration and a heavily constrained budget.
The Solution: Make sure you deliver the value.
For a business analytics team, the pinnacle of success comes from not only delivering real economic value but also creating and leveraging sustainable competitive advantage. Key to this is understanding how tactical activity maps into strategic outcomes. Increasing the odds of success is relatively simple. For many teams, it involves:
• Establishing direction through creating a roadmap
• Freeing resources through leveraging tactical revolutions
For many teams, the end-goal is the creation of a sustainable competitive advantage for their organization. Common examples include the ability to respond to market conditions faster than their competitors or achieving the highest level of customer satisfaction through one-to-one engagement. Effective use of business analytics helps the organization create a differentiated approach, one that’s often hard to replicate. However, these competitive advantages must be nurtured; if they were easy, they wouldn’t be a sustainable competitive advantage.
Few organizations, however, can afford to have their analytics team spending a number of years working on creating this advantage without any immediate economic return! For many teams, success comes from understanding the interdependencies between enabling initiatives and growth initiatives, as well as how these map into competitive advantage in the longer-term. Ideally, these are then be mapped into a roadmap that balances short-term value creation with longer-term competitive advantage.
While the roadmap is critical in defining direction, most established teams struggle to get investment based on their roadmap. Usually, their growth needs to be organic with incremental investment based on historical returns, not future. Normally, the key challenge for these teams is figuring out how to do more work without any additional investment; without demonstrated returns, they can’t get more funding. However, the perception is often that without additional funding, they don’t have sufficient resource to generate greater returns!
One approach that can successfully overcome this Catch-22 is through leveraging incremental efficiency improvements to improve productivity. In sufficient number, these productivity improvements can be re-invested in growth initiatives, delivering economic returns and justifying further investment. Often, these improvements stem from more effective data management processes, streamlined model development or improved operational deployment of models.
These tactical revolutions help the team free up enough resource to start creating incremental economic returns. And, the roadmap helps create a tangible vision of their transformation. Together, they help many teams deliver value.
Mistake No. 4: If you can’t prove what you’ve delivered, why should people trust you again?
It’s one thing to deliver; it’s another to demonstrate to others that you’ve succeeded. Even teams that successfully achieve their outcomes run the risk of perceived failure if they can’t convincingly demonstrate their success to their detractors.
The Solution: Make sure you measure the value.
Measuring the value needn’t be complex – in practice, the best measurement frameworks tend to be the simplest. At a minimum, however they need to:
• Be able to be captured
• Minimize the overhead imposed on the team
• Be comparable across projects
Measures are only useful if they can be captured. That may seem obvious, but it still remains surprising the number of times teams establish outcomes which, despite being intellectually attractive, fail to have the underlying systems necessary for their capture. A classic example is trying to measure reductions in customer churn rates due to analytically based targeting. While this seems straightforward, it’s often the case that operational customer-relationship-management systems lack the ability to capture whether or not a recommended offer was made by contact center staff. In the absence of this, it’s impossible to know whether poor results are due to the targeting being used or simply because contact center operators neglected to make the offering the first place!
Equally, some of the worst measurement frameworks are the most comprehensive. During the design phase, it’s often highly attractive to build as many measures as possible into the reporting layer. Unfortunately, unless these measures are transparently and automatically created by the underlying systems, every additional measure that requires manual input by definition reduces the time spent on actual value-creating activities. At best, the heaviest frameworks are simply ignored. At worst, they actually reduce productivity.
Finally, these measures must be comparable across projects and initiatives. The biggest value of a measurement framework lies in its ability to benchmark highly varied initiatives. Without this ability to benchmark initiatives and activities, the measures captured are meaningless.
An effective measurement framework covers a good selection of business, analytical and technical measures. Business measures help demonstrate the value that’s been achieved, not the effort that’s been needed to get there. Analytical measures help focus attention where it’s needed by measuring the quality of analytical assets. And, technical measures help optimize by measuring the effort and time needed to achieve outcomes. Taken together, these help analytics professionals demonstrate their value-creation to the rest of the organization, prioritize their effort and investment, as well as identify potential sources for future optimization.
The Power of Information
Competitive advantage comes from capitalizing on uniqueness. Every organization is different and every organization has the potential to exploit that exact uniqueness in a way that no one else can match. Doing this means taking advantage of their single biggest resource: their data.
This tsunami of information is a real challenge at every level in society. At a personal level, we struggle to keep on top of everything that’s happening around us. Alvin Toffler coined the term “future shock” as early as 1970 to describe the overwhelming and disorienting impact from information overload. And, at a professional level, where we once struggled with a paucity of information, we now struggle to pick which pieces of information are important out of the millions of measures at our fingertips. Regardless of where you start, this ever-increasing amount of information has changed the way we view the world, the way we live and the way we do business.
Dealing with this data deluge requires being smarter. It requires developing the ability to selectively process information based on value, not sequence. It requires, more than anything else, the realization that brute-force and manual effort are, in the long run, an impossible solution. Quite simply, it requires the effective application of analytics.
The people who know how to manage this data deluge are our future. Being able to translate massive amounts of data into real insight is beyond magic – it’s competitive advantage distilled. Nothing else offers an equivalent level of agility, productivity improvement or renewable value. Being “smarter” than your competitors isn’t just hyperbole, it’s a real description of how significant the impact of applied analytics can be.
Armed with the ability to quantify, communicate, deliver and measure the value they create, these modern day magicians understand that statistical expertise alone is not enough. Instead they become change agents, transforming the organization around them.
Evan Stubbs (Evan.Stubbs@sas.com) runs the Advanced Analytics Lab for SAS Australia/New Zealand and has more than 10 years experience helping organizations extract value from business analytics. A recognized expert in innovation, Stubbs has a background advising as a management consultant with KPMG Consulting, providing architectural strategy with Deloitte, as well as managing innovation within General Motor’s research and development activities. This article is based in part on Stubbs’ book “The Value of Business Analytics.”