How To Avoid Data Disappointment

A few months ago, the Chief Executive Group joined with AWS to survey CEOs on their use of Big Data to understand customer needs and trends within market segments. The resulting article, “New Poll: CEOs Find Challenges in Using Customer Data to Drive Innovation,” carried this summary in its deck: “Ability to harness and sort through data for meaningful insights remains a hurtle, many say, ‘The key is…finding what is actually relevant.’”

This is a pervasive problem in Big Data. Recently, two very senior, astute individuals contacted us about this—one a senior IT industry analyst, and the other a CEO of a major company. They both had the same question: If Big Data actually becomes available, what will be the consequences?

Our answer—Big Data offers big opportunities, but it carries the very strong likelihood of creating really big disappointments. It doesn’t have to be this way. The underlying problem is that many executives start in the wrong place.

When Jonathan advises his thesis students at MIT, his primary guidance is that there is a wrong way and a right way to do research. The wrong way is to pile books and articles up on a table, read them and try to make sense out of them. The right way is a three-step process.

First, think hard about your research questions—what you want to find out, prove or disprove. This may require some preliminary field work to sort through the possibilities. Second, make a detailed outline of your finished thesis, filling in the information and data that you have and identifying the gaps. Third, get to work gathering the information and data that you need, being willing to modify your research questions and outline as you learn more in the process.

The problem with Big Data in most companies is that it starts by piling books and articles up on the table, and then wondering “what is relevant.” Actually, Big Data as an objective is misnamed: it should be “Big Questions and Focused Data.”

What’s important

In our experience growing a SaaS company that generates Big Data (identifies every component of the profitability of every transaction in a company), we have found that there are three critical keys to success:

— Focus on what counts most

— Organize for profit

— Avoid dead-ends and pitfalls

By keeping these in mind, you can convert your Big Data into sustained profitable growth.

Focus on what counts most

In the Chief Executive article, one executive says, “The key is sorting through all the data and finding what is actually relevant.” Another CEO says, “While accessible and transparent, a bigger element for most businesses, including ours, is to know how to harness the data.”

The problem here is two-fold. First, a company can’t do everything, because it takes significant

time and resources to manage the change required to harvest any IT-based initiative—change management is the gating factor. Second, the initiatives have to be coordinated and focused on the right long-term strategic goals to be effective. If the availability of Big Data encourages a massive flock of independent tactical initiatives, it will do more harm than good.

This underlines the critical importance of starting with the right “research questions” for the entire company, not just the departments that comprise it.

The ultimate objective of every company is to maximize its sustained profitable growth. Tactical projects should be steps in this direction. The right staring point, however, is not to design a wide array of grassroots tactical projects, but instead to be clear about your overriding strategic and management priorities.

Your profit segments are the key to establishing your overarching priorities. Each profit segment needs different metrics, different data and different management game plans. If these are mixed together, the company will wind up with an ineffective result.

Enterprise Profit Management (EPM) is the key to effective profit growth, customer responsiveness and innovation. EPM is a SaaS software system that is extremely accurate, and creates a full, all-in P&L for every transaction (every invoice line) in a few weeks.

Traditional financial metrics like revenues, gross margins and costs tell you whether you are making money, while EPM tells you where you are making money, losing money, and only doing marginally.

In our experience, this is the profit segmentation that almost always emerges:

• Profit Peak customers—typically about 20% of the customers generate 150% of a company’s profits;

• Profit Drain customers—typically about 30% of the customers erode about 50% of these profits; and

• Profit Desert customers—typically the remainder of the customers produce minimal profit but consume about 50% of a company’s resources.

We have seen this profit segmentation in all aspects (e.g. products, suppliers) of almost every company in every industry. This is gives managers the critical prioritization into which they can slot projects.

• Profit Peak customers. These customers provide the core profitability of your company. They are your most important high-revenue high-profit accounts. This is where you need intense, focused information on each customer’s needs, growth direction and service satisfaction. They are so important that you need both data and personal relationships to monitor their purchases both to discern new opportunities and to see if competitors are nibbling in an account.

You should field a set of multicapability teams to work directly with each account in order to find new ways to create value and switching costs (e.g. through intercompany ties like vendor-managed inventory and joint product management). These customers generally are very open to innovations, and willing to pay for the value you provide.

• Profit Drain customers. These customers are large, but erode a surprising portion of the profits that you earn from your Profit Peak customers. They are usually bargain-hunters, and may request more and more services without paying the cost—by arguing that the volume of their business justifies the extras. For each of these customers, you need to use EPM to identify ways to bring down the cost to serve in order to make the relationship profitable.

In these accounts, you need intense account-by-account profitability and cost-to-serve data on each customer to move the relationship from money-losing to at least break even. Here, it is beneficial to field a set of teams, like a bank workout group, that is highly skilled in identifying cost drivers and reducing them. If these customers simply express a desire for more services, but are unwilling to pay for them, you should flag the request as problematic.

• Profit Desert customers. These are low-revenue, low-profit (or loss) accounts. A few may be high-potential customers using you to discipline their prime suppliers; others may be important development accounts. Your customer Big Data needs to identify these high-potential accounts.

Importantly, the top quartile of Profit Desert customers (arrayed by profits generated for you) is usually quite profitable in aggregate. It is valuable to use Big Data to identify product opportunities and trends for this subsegment of customers because they are too numerous to engage individually.

The bottom quartile is very unprofitable in aggregate, and the upper and lower quartiles provide negligible profits. Profit Desert customers in these three quartiles may express wants and needs, but it will not be economic to fulfill them. Instead, you should focus on lowering the cost to serve them through automated processes like portals and menu-driven services.

Organize for profit

In a fascinating book, Wired for War, P.W. Singer traces the robotics revolution and the use of robots in 21st century conflict. In a particularly telling chapter, he describes how the real-time video feeds from drone aircraft—Big Data—led to the systematic leadership problems that we call “managing at the wrong level.”

Over many years, improved communications technology has enabled military commanders to command increasingly at a distance from the actual battles. This led to a very effective management structure in which top commanders focus on strategy and personnel, mid-level commanders on operational initiatives and local officers on tactical issues. This parallels the leadership structure of the most effective companies.

However, the widespread availability of real-time drone aircraft information feeds has led to serious and systematic command and leadership problems. The ability of top commanders to see battlefield video feeds in real time has rapidly increased the centralization of command and led to an explosion of micromanagement.

This direct meddling by military leaders has led to the rise of what Singer calls the “tactical general,” as the line between timely supervision and micromanagement has become blurred.

But the biggest problem with top-level micromanagement in the military—just like in business—is the huge hidden opportunity cost of failing to manage at the right level: a leader ignoring the critical issues of high-level strategy and organizational capability because he or she is so caught up in real-time micromanagement. This causes two very big, related problems.

First, the top managers fail to plan for the future. For example, in business, top executives should primarily be focused on defining and developing the company as it should be in three to five years, since that is the time it takes to develop a new set of capabilities. Their other critical responsibility is developing the next generation of leaders.

In the absence of this hierarchical discipline, the company is in grave danger of getting mired in the present, and falling further and further behind.

Second, when top managers—or generals—take over tactical decisions, the lower-level leaders cannot develop their skills. Instead, they must be empowered to act with initiative, even if it means making a few mistakes along the way.

The answer is what Singer calls “enlightened control,” a concept he credits to the great Prussian generals of the 19th century, whose ideal was that the best generals gave their officers the objective and left it to them to figure out how best to achieve it. He cites the commanding general who so trusted his officers that the only order he supposedly issued on the eve of the Prussian invasion of the Danish province of Schleswig was, “On February 1st, I want to sleep in Schleswig.”

The action question for managers is: Will Big Data be too compelling for top officers, or will they have the insight and discipline to double down on “enlightened control.”

The answer is to institute an explicit profit management organization that ensures that managers throughout the company manage at the right level.

• Top managers should be focused primarily on two things: planning for the company’s competitive positioning in three to five years, and ensuring that the company has a cadre of managers with the right skills and capabilities to meet the needs of both the present and the future.

• Upper-midlevel managers—directors and perhaps some vice presidents—should be the locus of profit management. They should coordinate closely with each other, and together devote about half of their time to managing profitability (including working with Enterprise Profit Management Big Data, developing coordinated change priorities and initiatives and monitoring results). The other half of their time should be devoted to developing and mentoring the operating managers that report to them.

• Operating managers should be in charge of the company’s day-to-day operations, under the guidance of the director-level managers.

Avoid dead-ends and pitfalls

Two common problems snare many executives: paving the cowpaths and low hanging fruit.

• Paving the cowpaths.In many older cities and towns, the downtown streets wind and wander in ways that seem to have little logic. Boston and New York typify this. In these same cities and towns, the newer sections are often laid out in a grid-like pattern that is easy to navigate and efficient to drive.

The reason why the streets in downtown Boston and New York appear to be an illogical maze, is that years ago, the town officials simply paved the cowpaths that the original settlers used to drive their cattle to pasture and visit their neighbors. In the newer sections, like Back Bay in Boston and Midtown in New York, the officials had a blank slate to develop a more effective street pattern.

When the town officials paved the cowpaths, they were making an ineffective street pattern more efficient. In the newer sections, they were able to start from scratch, doing things right.

When managers concentrate on using Big Data to make their long-standing processes more efficient, they are in danger of paving their cowpaths. Instead, they need to rethink their business processes from scratch, understanding what the enormous new capabilities offered by Big Data would enable them to do, and restructure the processes to take advantage of their new capabilities.

• Low-hanging fruit.  This raises an important related problem. Managers have an almost overwhelming tendency to focus first on opportunities that are near to hand, and have quick, visible payoff. Projects that pave the cowpaths are an example. Sometimes these are called “low-hanging fruit.”

The problem is that these relatively small, parochial projects will absorb the organization’s resources and capability to change, even while they give the illusion of progress. The huge opportunity cost is losing the opportunity and ability to focus on the really important initiatives with the really big long-term payoffs.

The analogy breaks down because the big money is not in harvesting fruit more efficiently, but rather in changing the location of the orchard and the type of trees you plant.

This dissipation of effort, with its focus on a large number of small, incremental projects—rather than on the smallest number of game-changing, high-payoff initiatives—is the ultimate danger of Big Data.

Key to success

Big Data offers great opportunities—and huge dangers. How can you navigate toward the benefits while avoiding the hazards?

In short, the key is management insight and discipline.

The true promise of Big Data is to make your company better, not just to make parts of it more efficient. To accomplish this, you need one part technology to nine parts vision and great management.