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Re-engineering the Decision – Our Storyline for Data and Analytics

By Andrew White | April 12, 2021 | 0 Comments

Re-engineering the DecisionDecision MakingData and Analytics

Ever year our data and analytic conferences develop a theme.  These themes capture the moment, the focus and thinking for data and analytics at the time.  For our overall research theme and story we tend to focus on something that is both business relevant, aspirational, but also long lasting.  Late last year our newly minted research storyline for data and analytics was unveiled and it is called re-engineering the decision.  The two publications that heralded this storyline were these:

If you didn’t get it at the time you will now: Our connected storyline for data and analytics is re-engineering the decision.   We define re-engineering the decision as: “The fundamental rethinking of business decisions to achieve dramatic improvements in critical, contemporary measures of performance such as business value, cost, quality, service and speed.”

The high level story goes like this:

Decision-making no longer occurs solely along functional lines within enterprises. It occurs along collaborative pathways across multiple communities that are engaged based on the context of what is happening, the related outcomes and the decisions to be made, where collaborations are increasingly between humans and machines.  

Boards of Directors prioritize AI and analytics as their top two game-changing capabilities.  See Board of Directors survey, published as Survey Analysis: Board Directors Say Pandemic Drives Increased Investments in IT“, G00728158.  AI and analytics are used to help people (and machines) take decisions.  However, according to a recent Gartner survey, 65% of respondents agreed that decision-making has become more complex. Decision making needs to become more connected, contextual and continuous. It needs to be re-engineered to reflect the new complexities and take advantage of new opportunities and capabilities. IT leaders, including Data and Analytics leaders, play a key part in re-engineering the decision and helping business leaders be even more successful. 

Given the priority outlined above, how decisions are taken is at the center of digital business, and data and analytics is at the heart of how decisions are taken: To succeed in today’s digital economy, organizations must take data-driven decisions that are: 

  • Informed by external happenings
  • Enriched by collective knowledge
  • Repeatable using communal learning
  • Tap into and build on communal learning
  • Rapidly adaptable to new scenarios

Traditional enterprises do not function this way. Many decisions are not made using infusions of data and insights, from diverse sources both internal and external, much less informed by flows of information, assisted by machine augmentation and enriched by collaborative knowledge sharing. Despite significant investments in technology, and top priorities set by CEOs and Boards, most of our data-driven decision-making capability is still devoted to functional automation and operational understanding rather than building the ability to sense and respond as am organization-wide digital nervous system. 

However, data and analytics leaders, including chief data officers (CDOs), and CIOs and other data and analytics leaders, who seek to deliver the promise of digital business, are confronted with a battery of roadblocks. Digitalization is hard to deliver and sustain. It requires fundamental changes to existing data and analytics practices, many of which have been successful for achievement of the organization’s previous goals. This essentially means that data and analytics leaders and their business peers need to reengineer how they take decisions. Digitalization also demands different data and analytics strategies, culture, skills, governance practices, organizational models and cultures. Thus, the promise of digitalization is great, but so are the challenges facing data and analytics leaders.  

Re-engineering the decision speaks directly to digital business acceleration and aspiration.  At the heart of digital business is the recognition that a business moment is that unique opportunity every organization has to delight its customers or its citizens.  Every organization can experience business moments; but not all organizations have he ability to exploit them to create value for customer or citizen and themselves.  Most perceptibly a business movement should be thought of as a decision.  Your customer or citizen has to make a decision – should they work with you.  You have a decision too – how to serve the customer or citizen. The world is awash with decisions.  Even a business process or value stream is really little more than a series of connected tasks and decisions.  We unveiled our research direction with respect to digital here with this new note: Use Decision Modeling to Capitalize on Business Moments.

Considering when, where and how to re-engineer your decision making capabilities is a constant challenge, only more so today than ever before.  Once you can do this at will, any organization will want to be able to execute change at the speed of business.  This is where a lot of other research comes into play that has been building for some time.  A old idea has been reformed with a modern data fabric (see Infographic: An Intelligent Composable Business Demands a Data Fabric) and composable capability.  This will lead to a complementary storyline: Intelligent Composable business.  This is how data, analytics, AI, applications and software engineering work together closely, powered and supported by infrastructure and operations, security, and strategic portfolio management.  See Top Strategic Technology Trends for 2021: Intelligent Composable Business.

We think this is business relevant since the phrase does not talk to technology but what data and analytics can do for an organization.  More specifically what data and analytics can do for business leaders and their organizations and departments.  It is an aspirational story since it is not easy to do or something you just sign up for.  It will take time to master the skills, data, and techniques needed to work out where to re-engineer your decision making capability and how to do so.  It is a story with legs – not least because decision making has been around as a persistent challenge forever.  It so happened that 2020 brought to the fore many challenges about decision making and many organizations are struggling with it today more than ever.  2021 and 2022 will be different to 2020 though the pressures to make decision making a core competency, one again, are only increasing.

Here are some examples from recent press stories I spied that speak to the need or capability to re-engineer decision making capability.

  • Re-engineering Decisions Impact Lives
    • WSJ, April 2, 2021: UK Sees Gain in Covid Vaccine Gamble
    • Tony Blair, former Prime Minister
    • “The traditional way is ‘Well, we can’t be sure, we need more data, blah, blah blah.’ OK, fine, but you know in a pandemic you’re losing lives and livelihoods every week.”
    • ‘The British government “took punts on things before there was data to support them,” said Jeremy Brown, an expert on respiratory disease who advised on the UK vaccine deployment.’
  • Synthetic Data Helps Re-engineer Decision Making
    • WSJ, April 7, 2021: Made-Up Patients, Real-Life Medical Results
    • Allan Tucker, a professor at Brunel University London and author of a study published in Nature in November showing the validity of using synthetic data as a substitute for real healthcare data.
    • “The key advantage that synthetic data offers for healthcare is a large reduction in privacy risks that have bugged numerous projects [and] to open up healthcare data for the research and development of new technologies,” says Allan Tucker, a professor at Brunel University London and author of a study published in Nature in November showing the validity of using synthetic data as a substitute for real healthcare data.
  • Local Events Drive Global Re-engineering Decisions
    • WSJ, April 2, 2021: Supply Chain Strains Sharpen Focus on AI
    • Mario Harik, CIO, XPO Logistics
    • “As a supply-chain provider, as a logistics provider, we are very much in the data business.”
    • “We collect millions of data points from our operation…to help forecast where demand is going to come from…or to optimize a certain part of the supply chain to be as effective as possible.”
  • Partnerships can lead to Re-engineering Decisions
    • WSJ, April 2, 2021: Big Brands Retool Their Data Strategies
    • Sebastian Micozzi, SVP, Digital Transformation, Bacardi
    • “We will need to hold and own a lot more of our own consumers’ data ourselves, and rely less on the gatekeeping of Google and Apple or borrowing in other people’s data.”
    • “This challenges us to look at new data sets”
  • The Covid Crisis Taught David Farr the Power and Limits of Leadership
    • David Farr, CEO Emerson
    • WSJ, Dec 5-6, 2020: You Have to Take Jumps
    • “I might be wrong, bet we get paid to make decisions and make calls.”
    • “I would never jump off a cliff without knowing a bit about what is at the bottom.  But I know you have to take jumps.”

I would love to hear your thoughts on the storyline and more important hear your examples and stories for how you re-engineered how your organization takes decisions.

Our combined data and analytics key initiatives that support this storyline include:

The Gartner Blog Network provides an opportunity for Gartner analysts to test ideas and move research forward. Because the content posted by Gartner analysts on this site does not undergo our standard editorial review, all comments or opinions expressed hereunder are those of the individual contributors and do not represent the views of Gartner, Inc. or its management.

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