Business experts continue to encourage companies to become data-driven enterprises. Personally, I prefer the term decision-driven enterprises. Data only becomes valuable when it provides insights that lead to action (i.e., a decision). As Bain analysts, Michael C. Mankins and Lori Sherer, explain, “The best way to understand any company’s operations is to view them as a series of decisions.”[1] They add, “We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision-making and execution have the results to show for it.” However, according to Keith Hartley, CEO of LevaData, many companies confront a chasm he calls the “decision abyss.”[2] He observes that the abyss is created when various corporate planning teams are unaware of each others’ plans and act independently thanks to “a lack of data visibility and communication.” He adds, “If this scenario sounds familiar, you’ve likely fallen victim to the ‘decision abyss’ — the disconnect between engineering and product design, supply chain planning and procurement within organizations that hinders quick, informed decision-making and visibility.”
Crossing the Decision Abyss
Hartley reports, “You’re not alone when it comes to data visibility issues across organizations. Data can be difficult to manage and utilize — the decision abyss is a common problem. A full 99% of companies Talend surveyed recognize that data are crucial for success, but 97% face challenges in using data effectively, and a third aren’t even using data to make decisions. The reasons companies struggle leveraging data to drive informed decision-making vary — some point to poor-quality data, whereas others acknowledge a reliance on manual processes. While many industries have undergone a significant digital transformation to improve workflows and lower costs, that is not the case for most supply chain and procurement professionals, who are left to the mercy of disconnected software tools, utilizing spreadsheets to try and form actionable insights from complicated and disjointed data. Too many companies fall into the decision abyss when it comes to their supply chain.”
The world is complicated enough without adding unnecessary internal confusion to decision-making processes. According to Gartner analysts, companies that can get their decision-making ducks in a row stand to make considerable gains against their competitors. They report, “Only 9% of supply chain organizations expect to achieve revenue gains due to uncertainty, according to a survey by Gartner, Inc. Supply chain organizations that achieve an antifragile state not only survive, but benefit, from uncertainty. Antifragility can transform how supply chains perform in uncertain times in support of a growth agenda. Antifragility provides the ability to gain because of exposure to uncertainty. The bigger the uncertainty exposure, the more opportunity to gain.”[3] Although that may sound counterintuitive, the fact remains that companies best-equipped to deal with uncertainty will always make better decisions. Gartner analysts conclude that implementing effective decision processes and collaboration can have significant impact. How big of an impact? They insist implementing dynamic decision processes during uncertainty can result in nearly 5 times greater impact on positive revenue.
Uncertainty characterizes today’s business landscape. And, like Gartner, Enterra Solutions® believes that companies best able to deal with uncertainty will be the most successful in the years ahead. To help clients do that, we created the Enterra Revenue Growth Intelligence System™ (ERGIS™). ERGIS is a system that performs holistic optimization and autonomous decision-making across the drivers of revenue for consumer packaged goods and retail companies, delivering more revenue growth and profitability and increased efficiency with limited human interaction at the speed of the market. It is part of the larger Enterra System of Intelligence®. In an interview with James Maguire, eWeek’s Editor-in-Chief, I explained that corporate systems designed to capture transaction data were never engineered to dynamically think about changeable data in the marketplace or to help companies navigate in a real-time environment.[4] I told him, “Today we have uncovered a tens-of-billion-dollar market niche for a thing that we call a system of intelligence. Think about a system of intelligence that sits on top of the data and process layers that reside within the transactional systems of record. Now you have a system of intelligence which creates the ability to dynamically Sense, Think, Act, and Learn® about the environment that businesses are operating in. [With this capability,] you can navigate … a changeable landscape dynamically and bring the concept of autonomy to the enterprise.”
In a separate interview with Antoine Tardif, a founding partner of unite.AI, I explained that the Enterra System of Intelligence is powered by the Enterra Autonomous Decision Science™ (ADS®) platform and noted that the platform brings together three previously siloed technologies.[5] Those technologies are:
• A Semantic Reasoning and Vector Symbolic Logic-based Artificial Intelligence that enables human-like reasoning, decision-making and learning. This unique capability combines common-sense and industry knowledge with inference reasoning to create a system that can make decisions with subtle, human-like reasoning and then learn from the outcomes.
• Glass-Box, explanatory, transparent machine learning in the form of the proprietary Representation Learning Machine™ (RLM). The basis of the RLM is high dimensional mathematics and functional analysis. RLM uniquely identifies a function that describes the combination and contribution of variables in the data set that describe the observable effects through multiple layers of interaction with a high degree of precision. This is classified as a “glass-box”, explanatory algorithm that generates a function, whose output is visible as opposed to “black-box” algorithms that merely generate patterns, but do not offer any explanatory description of the dynamics of system/data set, nor have any substantive “understanding” of what the pattern means.
• Constraint-based, non-linear optimization capability that incorporates the RLM derived formula, along with semantic reasoning constraints and logic, to perform fast optimization that reflect complex multi-dimensional real-world considerations to derive highly actionable recommendations. This capability breaks the dimensionality barrier that is associated with linear models.
As I told Tardif, “The unique combination of these techniques has enabled Enterra to provide clients with significantly differentiated capabilities.”
Concluding Thoughts
Since I’m President and CEO of Enterra Solutions, you might be skeptical of any claims I make. Such skepticism is natural in a hype-filled world. However, tech journalist Marcus Law reports that a study by IDC commissioned by Decision Intelligence company Aera Technology, concluded, “Leading organizations are using AI, analytics, and data to generate value for their customers, employees, partners, investors and communities. … Decision Intelligence drove up to 20% improvement across product and service innovation, employee productivity, customer and employee retention, and more.”[6] Fred Laluyaux, CEO at Aera Technology, observes, “IDC’s research clearly shows the value creation divide among enterprises operationalizing decision intelligence and those that are not. The next wave of enterprise digital transformation can no longer be another data lake or planning tool. People must be empowered with innovative, intuitive technology that accelerates accurate decision-making at scale and enables companies to be self-driving, self-learning, and ready to compete in a digital world.” I couldn’t agree more.
Footnotes
[1] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[2] Keith Hartley, “Bridging the Decision Abyss,” SupplyChainBrain, 3 January 2024.
[3] Staff, “Gartner Says Just 9% of Supply Chains Expect to Gain Value from Uncertainty,” Gartner, 8 November 2023.
[4] James Maguire, “Enterra Solutions CEO Stephen DeAngelis on AI in Legacy Software,” eWeek, 31 August 2023.
[5] Antoine Tardif, “Stephen DeAngelis, Founder & CEO of Enterra Solutions – Interview Series,” Unite.AI, 11 September 2023.
[6] Marcus Law, “AI-enabled decision intelligence driving enterprise success,” Technology Magazine, 10 November 2023.
