Autonomous Decision Science™ and the Digital Enterprise

Stephen DeAngelis

August 3, 2021

The business world is flush with stories about big data, artificial intelligence (AI), machine learning (ML), and digital transformation. The cord that ties those things together is data analysis. As a result, a lot is written about data science. As the staff at Corsair’s Publishing observes, “Data science has all the buzz. If you read the headlines, businesses everywhere are hiring PhDs to build sophisticated machine learning algorithms. Massive repositories of big data are everywhere.”[1] They also bluntly state, “Decision science is probably more important to your organization than data science.” That’s a bold statement; nevertheless, they are not alone in their belief.

 

Bain analysts, Michael C. Mankins and Lori Sherer (), assert, “The best way to understand any company’s operations is to view them as a series of decisions.”[2] 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.” If Mankins and Sherer are correct about company operations being a “series of decisions,” then the Corsair’s Publishing staff is correct in asserting decision science is probably more important than data science. Below I will discuss how Autonomous Decision Science™ (ADS) is the next step in decision science.

 

The Difference Between Data Science and Decision Science

 

Andrew White (@mdmcentral), a distinguished analyst and Vice President at Gartner, has noted there can be a lot of “misunderstanding about the differences between AI, machine learning and decision making.”[3] This misunderstanding compelled him to ask, “What exactly is a decision?” He also began wondering what role artificial intelligence can and should play in decision-making. He observed AI only makes sense in decision-making if it’s not a black-box (i.e., if AI “takes decisions and changes outcomes that humans don’t understand”). If AI does make black-box decisions, he predicts, “Humans will likely turn off the box. So understanding the decision to some degree is very important.”

 

When he was asking himself that question several years ago, White concluded, “At this end of the benefits scale we are not asking AI to take a decision. We are asking or employing AI and ML to discover insights. We are not performing automation. Once the pattern has been discovered, humans will then decide how to employ those insights. We are not seeking to let the algorithm find the insight then automate its application directly. That would be conflating two uses cases into one.” Today, however, we can talk about autonomous decision-making, thanks to breakthroughs in cognitive computing. To be clear, we are not talking about all decision-making — but making some important decisions nonetheless.

 

K. V. Rao (@avisokv), founder and CEO of Aviso, observes, “Data science is often used in conjunction with many other science-related terms — algorithms, machine learning, artificial intelligence and predictive analytics. All of these terms are intended to indicate when computers are being used to detect signals or patterns in data that drive better business outcomes. While data science is perhaps the most broadly used term, ‘decision science’ seems like the more fitting description of how machines are assisting business leaders in solving problems that have traditionally relied on human judgment, intuition and experience.”[4] So is there a difference between data science and decision science?

 

The Corsair’s Publishing staff offers this succinct description about the differences between data science and decision science. They write, “Data science is for optimization and refinement. It is built on big numbers and big data. Decision science is for decision-making and hypothesis development. It is built on logic and probability.” Rao adds, “Simply put, decision science is a marriage of technology and business perspective to solve complex challenges.”

 

The Future is Autonomous Decision Science

 

Bill Waid, General Manager at FICO Decision Management, writes, “It’s easy to confuse digitization — be it a sleek app design or automated business intelligence — with digital decisioning. Both play a role in the larger concept of digital transformation, but the difference is digital decisioning is at its center.”[5] He adds, “Digital decisioning is active and anticipatory. It moves you to act — to take the next step before you know that step exists — by showing you the bigger picture of your business, your products and your customers. With digital decisioning, split-second actions are continuously optimized for the best outcomes.” In order to make some obvious decisions at the speed of business, they need to be made by AI systems without human intervention.

 

During a recent podcast with Lee Barrett, Accenture’s Managing Director/Applied Intelligence Lead, Barrett asked me to define artificial intelligence. I stated, “A lot of people have a misnomer about what artificial intelligence is. The way we define artificial intelligence at Enterra Solutions® is: having a machine reason in a human-like fashion about data in order to make decisions.” I went on to explain that Enterra® is interested in Autonomous Decision Science™ (ADS™), which we believe is the next step in the development of decision science. The Enterra ADS™ system analyzes data, automatically generates insights, makes decisions with subtlety of judgment like an expert would, and executes those decisions at machine speed with machine reliability.

 

Like a human, Enterra’s ADS System can gain enough domain knowledge to make nuanced, judgment-based decisions in a constrained market environment. The system uses location, consumer insights, market insights, and supply data in industries our companies serves, namely consumer packaged goods and retail, to generate insights and make decisions at the speed of today’s business. As I explained to Josef Schneider, host of the Next Normal Show, our ADS system combines the power of some of today’s most advanced mathematical computations with semantic reasoning.[6] It’s that combination that makes ADS so powerful. Schneider commented that ADS is analogous to combining muscle (mathematical computation) with brains (semantic reasoning and symbolic logic).

 

The companies we work with are awash in data, and, when that data is combined with rule-based knowledge obtained from company experts, it can be used by our system, which leverages semantic reasoning and symbolic logic, to make decisions like the best experts. For example, in the area of trade promotion optimization, our system was able to reduce the time to re-plan a trade promotion campaign from three weeks to three-and-a-half minutes with 95% accuracy. This increase in decision speed meant that instead of re-planning a single campaign, our client was able to run hundreds of scenarios in order to select the best option in a greatly reduced timeframe. Just as importantly, our system operates in a glass-box, rather than a black-box, fashion.

 

We are excited to be partnering with Accenture in order to bring our technology solutions to a wider range of clients. Accenture offers world-class change management expertise to its clients and Enterra’s technology can help ensure Accenture’s clients implement the best technologies in their digital transformation journeys. To learn more about autonomous decision science, and how the Enterra Solutions/Accenture partnership can benefit clients, listen to the podcast I did with Lee Barrett, Managing Director, Accenture Applied Intelligence – North America Northeast Lead. You can access the podcast by clicking on this link.

 

Concluding Thoughts

 

Elif Tutuk (@elif_tutuk), Head of Qlik Research, asserts, “Businesses must find a way to bring together the wealth of real-time, hyper-contextual data while building on the most powerful human capabilities of experience, collaboration, and contextual awareness. In doing so, they will be able to make intelligent and highly informed decisions that fulfill goals by better serving their customers.”[7] Cognitive computing and Autonomous Decision Science enables businesses to achieve that goal. The Corsair’s Publishing staff concludes, “In the end, if you are looking to optimize, to shave seconds or basis points, to mine vast data repositories, and operate on giant denominators — hire a data scientist. If you are looking for step-wise growth, to re-engineer, to create transparency, and to make better decisions — build a decision science function.” Or, you could leverage a cognitive computing system with embedded autonomous decision-making capabilities and have the best of both worlds.

 

Footnotes
[1] Corsair’s Publishing, “Why Decision Science Is Probably More Important To Your Organization Than Data Science,” Comprehension 360, 17 December 2018.
[2] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[3] Andrew White, “The difference between decision making and AI,” Information Management, 9 August 2018 (out of print).
[4] K. V. Rao, “Why Decision Science Matters,” TechCrunch, 4 December 2015.
[5] Bill Waid, “The Anatomy Of A Digital Decision,” Forbes, 21 April 2021.
[6] Stephen DeAngelis, “Autonomous Decision Science™ Discussed on the Next Normal Show,” Enterra Insights, 29 July 2021.
[7] Elif Tutuk, “Make Informed Decisions and Better Data Outcomes Will Follow,” Dataversity, 26 May 2021.