As data turns into the ubiquitous provide of monetary value creation, organizations need an end-to-end “data-driven value creation lifecycle” that speaks to and aligns the enterprise executives and the data and analytics teams spherical the CEO enterprise mandate to unleash the enterprise (or monetary) value of the group’s big data reserves.  The over-arching downside is:

How will we rework “Data Management” proper right into a Business “Value Creation” Discipline that is worthy of C-suite consideration, focus and strategic funding (and by no means merely one different know-how train)?

I latterly ran a poll on LinkedIn the place I requested folks for his or her concepts and rational for finding a larger time interval than “Data Monetization” as the observe for unleashing the enterprise value of the group’s data.  The response was attractive, with over 150,000 views, 1,100 votes, and 350 suggestions.

I study and processed every comment and responded to the ensuing dialog.  And whereas I’m not sure I did justice to all the good and provocative suggestions and techniques, I aggregated and blended the suggestions with my very personal biased views to provide you with the “Data-driven Economic Value Creation Lifecycle” in Figure 1.

 

Figure 1: “Data-driven Economic Value Creation Lifecyle

The purpose of Figure 1 was to create an end-to-end lifecycle that integrates data administration (data activation) with data science (insights discovery) and enterprise administration/innovation (data-drive value realization), after which feeds once more (backpropagates?) the analytics and enterprise outcomes effectiveness to data activation in a continuously-learning and adjusting value creation course of.

I’m constructive that our journey collectively should not be achieved, so take note of this an intermediate step in creating an data and analytics lifecycle (the data and analytics equal to the Agile software program program development framework?) that reframes the place and significance of the Data and Analytics to the roles of enterprise administration and enterprise innovation.

I think about that the framework in Figure 1 is the place to start for driving that C-suite / enterprise stakeholder and Data & Analytics group collaboration and alignment spherical leveraging data to deriving and driving associated, important, and quantifiable, enterprise outcomes and monetary value.

Triaging the Data-driven Economic Value Creation Lifecycle

There are plenty of essential concepts outlined in Figure 1 that we’ll combine and assemble upon to create one factor rather more extremely efficient.  These concepts embrace:

• Data-driven to reinforce that data is the most pricey helpful useful resource in at current’s world; that in the an identical method that oil was the gasoline that drove the monetary progress in the twentieth century, data will most likely be that catalyst for the monetary progress in the twenty first century.
• Economic as the overarching physique because of Economics is about the creation and distribution of wealth or value (plus you acknowledge I actually like talking about economics). Plus, economics affords us the physique in opposition to which we’ll leverage frequent monetary concepts like monetary multiplier impression, marginal costs, marginal propensity to save, and marginal propensity to devour with new monetary concepts like Nanoeconomics and the Marginal Propensity to Reuse.
• Value which may be outlined all through the dimensions of financial, operational, purchaser, employee, environmental, and societal / selection. Plus, we’ll use “Value” definition to define the “AI Utility Function” which can be the metrics spherical which the AI ML fashions will search to optimize. value” which may be outlined all through additional sturdy dimensions of value along with financial, operational, purchaser, employee, environmental and societal
• Creation in the software program of the data to the enterprise to drive quantifiable value. More than merely realization, categorical time, effort, money, and administration consideration (and fortitude) have to be invested to create value from one’s data.

Integrating Disparate Data and Analytic Practices and Professions

Figure 1 highlights the essential relationships between the practices of Data Management, Data Science, and Business Management (hey, I acquired that MBA for some trigger) and the supporting professions of information engineering, perform engineering, and value engineering:

• Data administration is the observe of ingesting, storing, organizing, sustaining, and securing the data created and picked up 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 prospects (administration critiques, operational dashboards, enterprise analysts, data scientists) inside an organization.
• Data Science is the observe of leveraging Feature Engineering to assemble ML fashions that decide and codify the purchaser, product, and operational propensities, traits, patterns, and relationships buried in the data. Included in Data Science is ML (Model) Engineering, which is the observe of integrating software program program engineering guidelines with analytical and data science data to deal with, monitor, operationalize, and scale ML fashions inside the operations of the enterprise
• Feature engineering is the profession focused on deciding on and mathematically transforming data variables or data elements to create ML Features that are used to create predictive fashions using machine learning or statistical modeling (comparable to deep learning, decision bushes, or regression). The perform engineering observe entails collaborating with space consultants to enhance and pace up ML model development leveraging space consultants’ heuristics, pointers of thumbs, and historic judgement experience.
• Business Management is the observe of planning, organizing, managing, and controlling the group’s property and directing enterprise actions to acquire the group’s acknowledged enterprise targets and enterprise initiatives.
• Value Engineering is the profession focused on decomposing an organization’s Strategic Business Initiative into its supporting enterprise (stakeholders, use cases, KPIs), data, and analytics elements. Value Engineering determines the sources of an organization’s value creation actions and identifies, validates, values, and prioritizes the KPIs in opposition to which the effectiveness of that value creation is measured.

Unlike what’s common from me, that is all for now. I hope for continued suggestions, conversations, debates, and presumably even some kicking and screaming as we form by the use of what we identify each of the practices and professions that comprise our “Data-driven Economic Value Creation” Lifecycle.  Yes, that’s “our” framework because of each of you is having an very important place on this definition course of.  We truly do stand on each other’s shoulders.

 

[1] Data Wrangling the strategy of cleaning, structuring, and enriching raw data proper right into a desired format for larger decision making

[2] Data munging is the course of of reworking and mapping data from one “raw” data format into one different format with the intent of establishing it additional acceptable and invaluable for numerous downstream capabilities comparable to analytics