According to Buckminster Fuller’s “Knowledge Doubling Curve,” human knowledge doubled approximately every century. Today, it is estimated that human knowledge is doubling every 12 to 13 months. IBM is estimating that, with the build out of the “Internet of things,” knowledge will double every 12 hours.
The explosion of information is clearly accelerating. Data is flooding companies and the problem is only getting worse. As the next big explosion heats up, “the Internet of things” — when our machines talk to each other — the rate of information growth will go exponential.
Data is quickly becoming one of the most critical business assets. The challenge most leaders face at this point is how to monetize their ocean of data. Having masses of information is of little value unless it is leveraged to give the company a competitive edge. Below are five key steps leaders can take to monetize their company’s data assets:
Decision Architecture
When thinking about analytics, most organizations think about the questions that answer how their business is performing and what information they gather to answer that question. While this helps to inform and describe what is occurring in the organization, it does not enable action. Rather, leaders should look to capture the decision architecture of a particular business problem and build analytical capability to develop diagnostics that enable decisions and, therefore, actions. In short, leaders should focus on decisions driven by data rather than simply asking questions of their data. This is a fundamental shift in how most organizations view analytics and is the key component to driving the maturity of companies higher on the analytical maturity curve.
Monetization Strategy
Organizations should develop monetization strategies and maintain them as valuable corporate assets. In the same way an organization might develop KPI’s to help manage and understand business performance, monetization strategies leveraging corporate data assets that drive competitive advantage should be developed continuously. The power of a good monetization strategy is the ability to take a good decision and make it a great one. A “monetization strategy” is a plan to achieve one or more business goals through tactics or actions that have a quantified benefit. They should be developed from decision architecture and linked to the corporate business levers that align strategic objectives.
Data Science and Decision Theory
It’s best to use both data science and decision theory to power monetization strategy. Data science helps organizations derive insights from their data to address a particular business problem or opportunity. Whereas data science helps turn information into actionable insights, decision theory helps structure the decision process to guide a person to the correct choice. Decision theory, along with behavioral economics, is focused on understanding the components of the decision process to explain why we make the choices we do. It provides a systematic way to consider tradeoffs among attributes that helps us make better decisions.
Analytical Structure
Data is the lifeblood of any analytical exercise and usually one of the bigger challenges. Sourcing, organizing and stitching together data is typically where a large amount of time is spent in building an analytical solution. When putting together datasets for analytics, the quality of the data is key. If data is missing, incorrect or inconsistent, the results of the analysis will be unclear or, worse, incorrect. Once the data is compiled, determining the right analytical structure is important for performance, integrity and scalability for the organization’s monetization strategy.
Repeatability and Scalability
Building one-off analytical solutions is more the norm for corporate America. Hours are poured into solving difficult problems to capture a revenue opportunity, only to have the analytics lie dormant or never used again. Leaders should look to develop monetization strategies that are automated, repeatable and scalable throughout their organization. This approach will lead to analytics that other departments can utilize versus having to build their own version.
These five key components will enable organizations to build monetization strategies and analytical solutions that help managers and executives navigate the vast amounts of data to make quality decisions that drive revenue. Building capabilities around each of these five keys will give an organization the power to tap into the value of its data and build analytical solutions that give the company a competitive edge.
Andrew Roman Wells is the CEO of Aspirent, a management-consulting firm focused on analytics. Kathy Williams Chiang is VP of Business Insights at Wunderman Data Management. They are the co-authors of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions.
