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TDWI Upside - Where Data Means Business

5 Steps to Monetize Your Data

Having masses of data is only useful if it's leveraged to give your company a competitive edge. Here are five steps to get you started.

According to Buckminster Fuller's Knowledge Doubling Curve, human knowledge doubled approximately every century until 1900. 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. When our machines talk to each other, the rate of information growth will increase exponentially.

Data has become a critical business asset. The challenge most leaders face is how to leverage their ocean of data to give the company a competitive edge.

Here are five key steps you should take to monetize your company's data assets.

1. Decision Architecture

When considering analytics, most organizations want to know how their business is performing and what information they need to answer performance questions. Although this helps to inform and describe what is occurring in the organization, it does not enable action.

Instead, capture the decision architecture of a particular business problem and build analytics capabilities to develop diagnostics that enable decisions and therefore actions. Leaders should focus on making decisions based on data instead of simply answering questions about what already happened. This is a fundamental shift in how most organizations view analytics and is necessary to advance on the analytics maturity curve.

2. Monetization Strategy

Develop monetization strategies and maintain them as valuable corporate assets. In the same way an organization might develop KPIs to help manage and understand business performance, monetization strategies leveraging corporate data assets should be developed continuously.

A monetization strategy is a plan to achieve one or more business goals through tactics or actions that have a quantified benefit. It should be developed from your decision architecture and linked to your corporate business levers that, in turn, align with your strategic objectives. The power of a good monetization strategy is the ability to take a good decision and make it a great one.

3. Data Science and Decision Theory

Use both data science and decision theory to power your monetization strategy. Data science helps you derive insights from your data to address a particular business problem or opportunity. 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 and helps us make better decisions. In short, data science helps turn information into actionable insights, and decision theory helps you structure the decision process to guide a person to the best choice.

4. Analytics Structure

Data is the lifeblood of any analytics exercise and usually one of your biggest challenges. Sourcing, organizing, and stitching together data is typically where a large amount of time is spent in building an analytics solution.

When putting together data sets for analysis, the quality of your data is key. If data is missing, incorrect, or inconsistent, the results of the analysis will be unclear or worse, incorrect. Once you compile your data, determine the right analytics structure for the performance, integrity, and scalability of your monetization strategy.

5. Repeatability and Scalability

Building one-off analytics solutions is increasingly common in corporate America. Hours are devoted to solving difficult problems and capturing a revenue opportunity, only to have the analytics never used again. Leaders should develop monetization strategies that are automated, repeatable, and scalable throughout their organization. This approach will lead to analytics that can be utilized by many departments versus each building its own version.

A Final Word

These five steps will enable you to build monetization strategies and analytics solutions that help managers and executives navigate vast amounts of data and make quality decisions that drive revenue. Building capabilities around each of these five steps will allow your organization to tap into the value of your data and build analytics solutions that give your company a competitive edge.

About the Authors

Andrew Roman Wells is the CEO of Aspirent, a management consulting firm focused on analytics. He is the co-author (with Kathy Williams Chiang) of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions.

Kathy Williams Chiang is VP, business insights at Wunderman Data Management. She is the co-author (with Andrew Roman Wells) of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions.

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