Some organizations are obsessed with spending money on Big Data without any concern for the value it represents. John Weathington tells you what sequence of actions to follow to mitigate this.
Obsessive-Compulsive Big Data (OCBD) is an organizational anxiety disorder wherein leaders have unwanted and repeated thoughts, ideas, and behaviors that compel them to spend money on big data for no rational reason. This condition has been growing at an alarming pace in organizational America, so it's worthwhile to spend some time on treating this condition. In a previous post, I talked about ambitious leaders that attach to the upside potential of their strategy without considering the cost of capability; however, the reverse condition is true as well. Some organizations are obsessed with spending money on Big Data without any concern for the value it represents. Return on investment lies at the intersection of Big Data's rational contribution to strategic purpose (return) and capability (investment). To understand the value of your investment in Big Data—and mitigate uncontrolled spending—it's important to follow the correct sequence: 1) understand the value of your strategy, 2) determine Big Data's contribution, and 3) adjust for risks.
Value and return on investment are critical in making the right strategic decisions. As a management consultant and expert in information strategy, it's important that my clients understand the value of any strategic intervention we undertake together. Sometimes I advise my clients not to proceed with a strategy because, although the upside looks appealing, the investment (i.e., cost of building out capabilities) is too large and/or the risks carry devastating impacts. In other cases, like those with this OCBD condition, I encourage them to step back and start with the correct step of the sequence—assessing the value of their strategy.
The value of your strategy drives the purpose of your strategy, which is why it's such a powerful construct. In a later step we'll discuss the contribution of big data; however, this step is important regardless of your intent to involve big data in your strategy. Strategy development takes a significant amount of money, time, and energy from the most important people in your organization, so there better be a good reason why your organization is committing resources to this pursuit.
The reason of course, is to set the direction for the company. However, why does the company's direction need to be set? What would happen if you did no strategic planning? Answering this question will help you determine the value of your strategy. If you take my advice, and follow the process of cinematic visionography, determining the value of your strategy is pretty easy. In short review, the steps in cinematic visionography are: 1) paint a picture of your future macro-environment (political, social, technological, etc.), 2) paint a picture of your competitive environment (customers, suppliers, competition, etc.), and 3) paint a picture of your organization and where big data fits in. To determine the value of your strategy, branch off at step 2, and in addition to step 3, create step 3a) paint a picture of your organization if you did nothing at this point. Once steps 3 and 3a are complete, quantify the difference in terms of profit and/or other key performance indicators to determine the value of your strategy. Once you have a good sense for the overall value of your strategy, it's time to determine big data's contribution.
Big Data's contribution
The value big data plays in your strategy depends on your company's driving force, or the framework within which you make fundamental decisions about what products you offer and the markets you enter. The great majority of companies fall into two categories: companies that serve a particular market and companies that offer a compelling product, service, and/or relationship.
For companies that focus on a particular market, the primary contribution of big data is market clarity. Market-driven companies that effectively use big data have a clearer picture of their market than those who do not use big data. So the internal question to evaluate is how much incremental benefit big data brings to market clarity. In your strategic vision, you might have small data or even non-analytic methods for better understanding your market. Or, you might decide to structure your future capability solely around big data analytics. This is a qualitative decision that depends on your attitude and orientation on the promise of big data. The outcome is a percentage from 1 to 100 on the relative contribution of big data to the clarity of your target market.
For companies that focus on a particular product or service offering, the primary contribution of big data is product distinction. For these companies, big data adds an element of competitive distinction to their products that competitors don't have. The internal question to evaluate is how much incremental distinction big data brings to the company's product, service, or relationship offering. Again, this is best done as a qualitative exercise that produces a contribution percentage from 1 to 100.
The risks of Big Data
An important part of this process is bridging an understanding with top management about the risks involved with employing big data. This is an often overlooked exercise that can greatly affect your decision to pursue its path. All risks have three characteristics: probability, detectability, and impact. Of the three characteristics impact carries the most weight.
Regardless of probability, if the impact of using big data is too great, you shouldn't pursue the strategy. For instance, if the culture that effectively supports big data clashes with your existing culture, it could be a catastrophe. Remember, analysts love exploration, so if you bring that element in contact with a bureaucracy, the best outcome is that the bureaucracy overcomes the new culture, and the initiative fails; however, the worst outcome is that the bureaucracy that supports your core capability is compromised, and now you have a completely dysfunctional organization. In this situation, my advice is to stay away from big data—there's too much at risk.
Once devastating impacts are ruled out, consider the possibility that the big data effort just doesn't work. As enthusiastic as you are with your big data endeavors, as with anything, there's always a chance that big data won't produce any value. By definition, using big data for competitive purposes is a risky proposition. Once again, this is typically a qualitative assessment that's rarely supported with significant quantitative analysis, but it's still a valuable component in assessing the value and ROI of big data. The objective is to agree on the probability of success—from 1 to 99—that big data will serve its intended strategic purpose. Because of the risky nature of big data exploration, I recommend you choose a conservatively low value between 25 and 50 percent.
Putting it all together
The best treatment for obsessive compulsive big data is strict adherence to a highly effective sequence that systematically puts a value context around big data spending. The first step forms the outermost context: the overall value of the strategy. Of course, the overall strategy involves more than just big data, so the next step establishes the potential value big data can contribute to the strategy. The last step adjusts this value for risk, which brings us to the real value of big data or your expected return for employing big data in your strategy.
For example, let's say your cinematic visionography paints a picture of an organization with a net profit of $500 million per year, which can reasonably be sustained for 3 years. You're in moderate growth, so if you do nothing at this point, based on projected macro-economic and competitive factors, you'll probably net $200 million per year over the same time period. The value of your strategy is then $300 million over 3 years, or $900 million. As a market-driven company, you determine that big data will play a 65% role in the overall capability of gaining market clarity. This puts big data's contribution to the strategy at $585 million (65% of $900 million). Of course, your big data ideas may not obtain as expected, so to be conservative, you and your top management team agree that there's a 45% chance of success. This brings your risk-adjusted value of employing big data to $263 million. With this value to frame your spending, you can invest $25 million in data scientists, analytic managers, process experts, clusters, analytic software, and one very good information strategy consultant; and still get a better than 10 to 1 return on investment.
Following this sequence of activities makes it almost impossible to overspend on big data. Even if some of your assumptions don't prove out, there's always a rational value context within which to justify spending. If you're currently spending money on big data, and you haven't already gone through this exercise—stop! Determine the expected value of your big data involvement first, and then feel confident that you're spending wisely.