Much is being written today about big data, and I have concerns about what is being said.
Wikipedia defines big data as “a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, analysis and visualization.” I believe this definition is misguided, as it misses an important point: value.
We need to shift the all the hoopla from big data to big value. To exploit the latent potential of what is contained in big data, it is necessary to embrace business analytics, and its amplifier, predictive analytics, to lead to big value creation.
Analytics as an Enabler
Analytics itself is not the total solution. But if analytics is an answer, then what is the question?
Analytics serves as a means to an end, and that end is decisions. Many assume that this implies executive decisions, but the relatively higher value for and benefit from applying analytics is arguably for daily operational decisions. Here is why.
Decisions can be segmented in three layers:
- Strategic decisions are few in number but can have large impacts. For example, “Should we acquire a company?” or “Should we exit a market?”
- Tactical decisions involve controlling an organization’s actions with moderate impacts. For example, “Should we modify our supply chain?”
- Operational decisions are daily, even hourly, and often affect a single transaction or customer. For example, “What deal should I offer to this customer?” or “Should I approve this bank loan?”
Operational decisions are arguably the most important layer for embracing analytics for several reasons. One reason is that executing the corporate strategy is not solely accomplished with strategy maps and the resulting key performance indicators displayed in a balanced scorecard and dashboards. The daily operational decisions are what actually move the performance measure dials more than big strategic decisions.
A second reason is that an organization’s exposure to risk does not come in big chunks. Although much is now written about enterprise risk management, ERM deals more with reporting. Risk is incurred one event or transaction at a time, and each one typically involves daily decisions.
A third reason is that in the sales and marketing functions, operational decisions maximize customer value more than policies do. For example, what should a customer-facing worker do or say to a customer to gain profit lift?
Many Small Decisions Add Up
Operational decisions scale from the bottom up, and in the aggregate they can collectively exceed the impact of a few strategic decisions. A penny per decision transaction adds up to large amounts.
The baseball book and movie “Moneyball” highlighted the use of quantitative analysis to maximize results for the Oakland Athletics baseball team. But what many readers and viewers, including enthusiastic analysts, do not realize is the statistics were used by the baseball team in two steps, with larger payoff in the second step:
- First, their statistical analysis identified which mix of lower salaried players to acquire and trade away. But after completing that step, the team still lost games.
- It was not until the next step that they educated and trained each ball player at the pitch-by-pitch and play situation level that they began winning games. The second step is comparable to operational decisions. Good decisions add up to achieve the enterprise’s goal – execute the strategy.
Business Analytics are the Next Wave
Today many businesspeople don’t really know what predictive modeling, forecasting, design of experiments or mathematical optimization mean or do. But over the next 10 years, if businesses want to thrive in a highly competitive and regulated marketplace the use of these powerful analytical techniques will have to become mainstream. This is no different than the way use of financial analysis and computers have become mainstream. Executives, managers and employee teams who do not understand, interpret and leverage their data as an asset will be challenged to survive.
When we look at what kids are learning in school, executives and managers may be surprised. We were typically taught mean, mode, range, and probability theory in our first-year university statistical analytics course. Today children learn these concepts in the third grade! They are taught these methods in a very practical way. If you have x dimes, y quarters and z nickels in your pocket, then what is the chance of you pulling a dime from your pocket? Learning about range, mode, median, interpolation and extrapolation follow in short succession. We are already seeing the impact of this with Gen Y/Echo boomers who are getting ready to enter the workforce. They are used to having easy access to information and are highly self-sufficient in understanding its utility. The generation after theirs will not have any fear of analytics or need to seek an "expert” to do the math.
Analytics as a Competitive Edge
There is always risk when decisions are made based on intuition, gut feel, flawed and misleading data, or politics. In the popular book, “Competing on Analytics: The New Science of Winning,” Babson College Professor Tom Davenport makes the case that increasingly the primary source of attaining a competitive advantage will be an organization’s competence in mastering all flavors of analytics.
If your management team is analytics-impaired, then your organization is at risk. Business analytics, and its subset predictive analytics, is arguably the next wave for organizations to successfully compete. Analytics will not only be applied to predict outcomes but also to reach higher to optimize the use of their resources, assets and trading partners.
It is not just big data, but rather the big value that can be created by converting data into meaningful and relevant information for insight, foresight, decisions and actions.
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