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 asking folks for their thoughts and rationale for finding a better term than “Data Monetization” for the practice of unleashing the business value of an 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. 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 create the “Data-driven Economic Value Creation Lifecycle” in Figure 1.

Figure 1: Data-driven Economic Value Creation Lifecycle

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-driven 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 optimistic 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 fostering collaboration and alignment among C-suite / business stakeholders and the Data & Analytics team regarding leveraging data to derive relevant, meaningful, and quantifiable business outcomes and economic value.

Triaging the Data-driven Economic Value Creation Lifecycle

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:

• Data-driven to reinforce that data is the most valuable resource in today’s world; in the same way that oil was the fuel that drove economic growth in the 20th century, data will be the catalyst for economic growth in the 21st century.
• Economics is the overarching frame because It is about the creation and distribution of wealth or value (plus, I love talking about economics). Economics gives us the frame against which we can leverage common economic concepts like the economic multiplier effect, marginal costs, marginal propensity to save, and marginal propensity to consume with new economic concepts like Nanoeconomics and the Marginal Propensity to Reuse.
• Value can be defined across financial, operational, customer, employee, environmental, and societal / diversity dimensions. Plus, we can use the “Value” definition to define the AI Utility Function, which are the metrics around which the AI ML models will seek to optimize “value,” which can be defined across more robust dimensions of value, including financial, operational, customer, employee, environmental and societal.
• Creation in the application of the data to the business to drive quantifiable value. More than just realization, explicit time, effort, money, and management attention (and fortitude) must be invested to create value from one’s data.

Integrating Disparate Data and Analytic Practices and Professions

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:

• Data management is the practice of ingesting, storing, organizing, maintaining, and securing the data created and collected by an organization.
• Data engineering is the profession focused on aggregating, preparing, wrangling[1], munging[2], and making raw data usable to the downstream data consumers (management reports, operational dashboards, business analysts, data scientists) within an organization.
• Data Science is the practice of leveraging Feature Engineering to build ML models that identify and codify the customer, product, and operational propensities, trends, patterns, and relationships buried in the data. Included in Data Science is ML (Model) Engineering, which is the practice of integrating software engineering principles with analytical and data science knowledge to manage, monitor, operationalize, and scale ML models within the operations of the business
• Feature engineering focuses on selecting and mathematically transforming data variables or elements to create ML Features to develop predictive models using machine learning or statistical modeling (such as deep learning, decision trees, or regression). The practice involves collaborating with domain experts to enhance and accelerate ML model development, leveraging domain experts’ heuristics, rules of thumb, and historical judgment experience.
• Business Management is the practice of planning, organizing, managing, and controlling an organization’s resources and directing business activities to achieve the organization’s stated objectives and initiatives.
• Value Engineering focuses on decomposing an organization’s Strategic Business Initiative into its supporting business (stakeholders, use cases, KPIs), data, and analytics components. Value Engineering determines the sources of an organization’s value creation activities and identifies, validates, values, and prioritizes the KPIs against which the effectiveness of that value creation is measured.

Unlike what is typical of 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 you have a vital role in this definition process.  We truly do stand on each other’s shoulders.

[1] Data Wrangling is the process of cleaning, structuring, and enriching raw data into a desired format for better decision-making.

[2] Data munging is the process of transforming and mapping data from one “raw” data format into another format to make it more appropriate and valuable for various downstream purposes, such as analytics.