One of the biggest challenges that enterprises face today is navigating through the flood of data they have available to gain insights, drive quality decisions and generate revenue. This has made decision architecture, data science and guided analytics vital to the success of digital businesses, according to the authors of Monetizing Your Data: A Guide to Turning Data into Profit-Driving Strategies and Solutions.
The book, written by Andrew Wells and Kathy Chiang and published by Wiley in March, suggests strategies, offers frameworks and recommends tools for organizations looking to monetize their data. Wells is the co-founder and chief executive at management-consulting firm Aspirent; Chiang is the VP of business insights at digital agency Wunderman Data Management and partner at WaterWheel Data Advisors.
This excerpt is taken from Chapter 3, titled Decision Architecture Methodology: Closing the Gap, and details decision architecture. The decision architecture methodology offers a framework for organizations to unearth actionable information from data, and guides organizations through building analytics into data monetization strategies. This excerpt also explicates the five phases of decision architecture methodology: discovery, decision analysis, monetization strategy, agile analytics and enablement.
A high-level overview of each of the phases may be in order before we go too deep into the specific areas. Let's begin with the Discovery phase. The Discovery phase starts with aligning your project goals to organizational objectives to ensure alignment. Next we identify the business priority, which may be a problem or opportunity. From our business priority we develop one or more hypotheses we believe articulate the priority in an actionable manner. Once we know what we are looking to address, we conduct interviews and working sessions to ramp up on the subject matter, understand existing systems, and fine tune the hypothesis and scope.
Decision Analysis, the next phase, is designed to capture questions, key decisions, action levers, metrics, data needs, and a category tree. We leverage specific facilitation techniques in working sessions designed around topics and compile this information into the various Decision Analysis components. This information drives the building of category trees, key decisions, action levers, and success metrics, providing requirements for the Agile Analytics and Monetization Strategy phases.
During the Decision Analysis and agile Analytics phases, you build and refine your monetization strategy. In this phase you develop specific strategies, identify business levers, and assign actions from the earlier phases to deploy to drive revenue or reduce costs.
The Agile Analytics phase encompasses building a solution from the requirements gathered in the Decision Analysis and Monetization Strategy phases. This phase is composed of several process steps: Data Development, Analytical Structure, Decision Theory, Data Science, and Guided Analytics. These components may vary in size and length depending on the level of automation and technology.
Finally, in the Enablement phase, once a solution has been developed, it is rolled out to the user base. Adoption, a key theme in this phase, only occurs if adequate testing and training have been successfully conducted.
Reprinted with permission of the publisher. To read more about the five phases of decision architecture methodology, download/read the full excerpt.
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