As data becomes the ubiquitous source of economic value creation, organizations need an end-to-end “data-driven value creation lifecycle” that speaks to and aligns the business executives and the data and analytics teams around the CEO business mandate to unleash the business (or economic) value of the organization’s vast data reserves. The over-arching challenge is:
How do we transform “Data Management” into a Business “Value Creation” Discipline that is worthy of C-suite attention, focus and strategic investment (and not just another technology activity)?
I recently ran a poll on LinkedIn where I asked folks for their thoughts and rational for finding a better term than “Data Monetization” as the practice for unleashing the business value of the organization’s data. The response was stunning, with over 150,000 views, 1,100 votes, and 350 comments.
I read and processed every comment and responded to the ensuing conversation. And while I am not sure I did justice to all the great and provocative comments and suggestions, I aggregated and blended the comments with my own biased perspectives to come up with the “Data-driven Economic Value Creation Lifecycle” in Figure 1.
Figure 1: “Data-driven Economic Value Creation Lifecyle
The goal of Figure 1 was to create an end-to-end lifecycle that integrates data management (data activation) with data science (insights discovery) and business management/innovation (data-drive value realization), and then feeds back (backpropagates?) the analytics and business outcomes effectiveness to data activation in a continuously-learning and adjusting value creation process.
I am positive that our journey together is not done, so consider this an intermediate step in developing a data and analytics lifecycle (the data and analytics equivalent to the Agile software development framework?) that reframes the role and importance of the Data and Analytics to the roles of business management and business innovation.
I believe that the framework in Figure 1 is the starting point for driving that C-suite / business stakeholder and Data & Analytics team collaboration and alignment around leveraging data to deriving and driving relevant, meaningful, and quantifiable, business outcomes and economic value.
There are several critical concepts outlined in Figure 1 that we can blend and build upon to create something even more powerful. These concepts include:
Figure 1 highlights the critical relationships between the practices of Data Management, Data Science, and Business Management (hey, I got that MBA for some reason) and the supporting professions of data engineering, feature engineering, and value engineering:
Unlike what is normal from me, that is all for now. I hope for continued comments, conversations, debates, and maybe even some kicking and screaming as we sort through what we call each of the practices and professions that comprise our “Data-driven Economic Value Creation” Lifecycle. Yes, this is “our” framework because each of you is having an important role in this definition process. We truly do stand on each other’s shoulders.
 Data Wrangling the process of cleaning, structuring, and enriching raw data into a desired format for better decision making
 Data munging is the process of transforming and mapping data from one "raw" data format into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics