Recessions, Big Data, Data Science, And The Liberating Power Of “Own It”
There, I said it. I said the “R” word. And no, I’m not talking the political “R” word. I’m talking about the potential of a… r-e-c-e-s-s-i-o-n. There are many indicators pointing to the potential of a worldwide recession, and unfortunately, during a recession, many organizations hunker down, cut spending, and try to ride it out – all the wrong things to do if you want to actually avoid a recession.
Figure 1: What Is a Recession? Examples, Impact, Benefits
Leading organizations see a recession as an opportunity to capture new customers and expand their value-creation ecosystem to grow market share. They sweep up the best talent and training that focuses on deriving and driving new sources of customer, product, and operational value.
Yes, a recession separates the “sheep” organizations – where management is just worried about surviving the “musical chairs” of desperation – from the “wolf” organizations where management aggressively seeks opportunities to embrace innovation to create new sources of customer and market differentiation.
Let’s explore how your organization can become the wolf.
A recession forces a focus of urgency
A recession forces a focus on creating a sense of urgency. A recession would create a focus on delivering measurable and material business value in the next nine to 12 months. It would short-circuit those AI and machine learning “science experiments” and give these initiatives a kick in the butt to start delivering measurable and meaningful business value today!
The Big Data Business Model Maturity Index provides a roadmap for those organizations that are serious about leveraging data and analytics to power their business models. Wannabes need not apply (see Figure 2).
Figure 2: Big Data Business Model Maturity Index
There is no need for organizations to commit to big bang technology investments and hope that something of value squirts out at the end.
A recession forces a focus on collaboration
A recession forces a focus on embracing an IT-business collaborative engagement methodology that is focused on identifying, validating, valuing, and prioritizing the organization’s key business and operations use cases. A key to creating an effective and efficient data-science community is to teach your business stakeholders to “think like a data scientist.” This enables business stakeholders to understand how best to collaborate with a data scientist and a data engineer to uncover the customer, product, service, and operational insights that will drive business success (see Figure 3).
Figure 3: “The Art of Thinking Like a Data Scientist”
Data science is a team sport comprised of data engineers, data scientists, and business stakeholders. And like a baseball team can’t win with only shortstops and catchers, your data science initiative MUST clearly articulate the team’s roles, responsibilities, and expectations. If the goal of your organization is to become more effective at leveraging data and analytics to power your business models, you can’t win that game with a team full of pitchers.
A recession forces a focus on value creation
A recession forces a focus on a value engineering framework that delivers on the promise of the “4 M’s of Big Data: Make Me More Money!” To drive the focus on value, we use the Data Science Value Engineering Framework (see Figure 4).
Figure 4: Data Science Value Engineering Framework
The Data Science Value Engineering Framework starts with the identification of a key business initiative that not only determines the sources of value but also provides the framework for a laser-focus on delivering business value and relevance in the immediate term.
The heart of the Data Science Value Engineering Framework is the collaboration with the different stakeholders to identify, validate, value, and prioritize the key decisions (use cases) that they need to make in support of the targeted business initiative.
A recession forces a focus on sharing and reusing
A recession forces a focus on sharing, reusing, and refining your data and analytic assets – assets that never deplete, never wear out, and can be used across an infinite number of use cases at near-zero marginal cost. The Economic Digital Asset Valuation Theorem exploits the “Economics of Learning” that rewards those organizations that take an incremental approach to building out their data and analytics capabilities that yield business value by 1) accelerating time to value (by monetizing incremental learning) while 2) de-risking business investment risks (see Figure 5).
Figure 5: Economic Digital Asset Valuation Theorem
In the digital era, the “economies of learning” is more important than “economies of scale.”
Summary: the liberating power of “owning it”!
Organizations, like people, can choose their own destiny – but only if they are willing to “own” their current situation. If you are a victim of the consequences of others (the sheep), then you have abdicated control to others. But if you “own” the situation (the wolf), then you put yourself in control of your own destiny.
This article originally appeared on LinkedIn and is republished by permission. Hitachi Vantara is an SAP global technology partner.