As data becomes the ubiquitous source of economic value creation, organizations need an end-to-end €œdata-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 €œData Management€ into a Business €œValue 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 €œData 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 €œData-driven Economic Value Creation Lifecycle€ in Figure 1.

Figure 1: €œData-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.

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; that in the same way that oil was the fuel that drove the economic growth in the 20th century, data will be that catalyst for the economic growth in the 21st century.
• Economic as the overarching frame because Economics is about the creation and distribution of wealth or value (plus you know I love talking about economics). Plus, economics gives us the frame against which we can leverage common economic concepts like 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 which can be defined across the dimensions of financial, operational, customer, employee, environmental, and societal / diversity. Plus, we can use “Value” definition to define the €œAI 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 is the profession focused on selecting and mathematically transforming data variables or data elements to create ML Features that are used to create predictive models using machine learning or statistical modeling (such as deep learning, decision trees, or regression). The feature engineering practice involves collaborating with domain experts to enhance and accelerate ML model development leveraging domain experts€™ heuristics, rules of thumbs, and historical judgement experience.
• Business Management is the practice of planning, organizing, managing, and controlling the organization€™s resources and directing business activities to achieve the organization€™s stated business objectives and business initiatives.
• Value Engineering is the profession focused 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 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 €œData-driven Economic Value Creation€ Lifecycle.  Yes, this is €œour€ framework because each of you is having an important role in this definition process.  We truly do stand on each other€™s shoulders.

[1] Data Wrangling 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 with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics