1. What is Decision Intelligence?

Cassie Kozyrkov, the chief decision scientist at Google, describes Decision Intelligence as a new academic discipline concerned with all aspects of selecting between options.

It brings together the best of applied decision theory, data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them.

It’s a vital science for the AI era, covering the skills needed to lead AI projects responsibly and design objectives, metrics, and safety-nets for automation at scale.

Decision intelligence is the discipline of turning information into better actions at any scale.

One can also see decision intelligence as Managerial Science augmented by applied decision science and data science.

1.1 Managerial Science

Managerial Science is the broad interdisciplinary study of problem-solving and decision making in human organizations.

And it is concerned more about designing, developing, applying new and better decision models of organizational excellence in all aspects.

Managerial science research can be done on three levels

• The fundamental level lies in three mathematical disciplines: probability, optimization, and dynamical systems theory.
• The modelling level is about building models, analyzing them mathematically, gathering and analyzing data, implementing models on computers, solving them, experimenting with them — all this is part of management science research on the modelling level. This level is mainly instrumental and driven mainly by statistics and econometrics.
• The application level, just as in any other engineering and economics disciplines, strives to make a practical impact and be a driver for change in the real world.

What if this model construction is empowered by applied decision science & data science, then one call this Decision Intelligence.

1.2 Decision Science

While most fields of research focus on producing new knowledge, decision science is uniquely concerned with making optimal choices based on available information. The disciplines making up the qualitative side have traditionally been referred to as the decision sciences

Decision science seeks to make plain the scientific issues and value judgments underlying these decisions and to identify tradeoffs that might accompany any particular action or inaction.

The decision sciences concern themselves with questions like:

• “How should you set up decision criteria and design your metrics?” (All)
• “Is your chosen metric incentive-compatible?” (Economics)
• “What quality should you make this decision at and how much should you pay for perfect information?” (Decision analysis)
• “How do emotions, heuristics, and biases play into decision-making?” (Psychology)
• “How do biological factors like cortisol levels affect decision-making?” (Neuroeconomics)
• “How does changing the presentation of information influence choice behaviour?” (Behavioral Economics)
• “How do you optimize your outcomes when making decisions in a group context?” (Experimental Game Theory)
• “How do you balance numerous constraints and multistage objectives in designing the decision context?” (Design)
• “Who will experience the consequences of the decision and how will various groups perceive that experience?” (UX Research)
• “Is the decision objective ethical?” (Philosophy)

It also includes decision analysis, risk analysis, cost-benefit and cost-effectiveness analysis, constrained optimization, simulation modelling, and behavioural decision theory, as well as parts of operations research, microeconomics, statistical inference, management control, cognitive and social psychology, and computer science.

1.3 Data Science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

Since It is a hot topic in today’s time, I don’t want to explain much.

2. Why do we need decision Intelligence?

Humans are not optimizers, we’re satisficers.

Many business leaders don’t think they require science for decision-making and also they think they’re being data-driven when they look at a number, form an opinion, and execute their decision. Unfortunately, such a decision will be “data-inspired” at best.

Data-inspired decision-making is where we swim around in some numbers, eventually reach an emotional tipping point, and then decide. There were numbers near that decision somewhere, but those numbers didn’t drive the decision. The decision came from somewhere else entirely. It was there all along in the unconscious biases of the decision-maker.

To be truly data-driven — order matters! You need to frame the decision model context upfront, then you need to collect data.

Now you are able to sense the scope of application of Managerial Science, Decision Science and Data Science which I discussed earlier in this article.

Decision Science is Important To Your Organization as much of Data Science

Data Science focuses on finding insights and relationships via statistics. Decision Science is looking to find insights as they relate to the decision at hand. Many businesses that invest in data science teams then load their plate with decision science problems. So it is clear that at some point in time considering both decision science and data science while building decision models will become inevitable. The umbrella term for all this process together is Decision Intelligence. Simply, Decision intelligence is the discipline of turning information into better actions at any scale.

Unveiling the power of Artificial Intelligence

Computers are the ultimate reliable workers. They do only what they are told. No more and no less. They don’t think for themselves. They don’t think at all! They don’t want anything except what you told them to want.

Machine learning and AI is so powerful. Unlike traditional programming, they allow you to solve a problem even if you can’t think up the solution’s steps yourself. AI allows you to automate what you can’t express.

In the Decision Intelligence model, Data & Insights collected from quantitative and qualitative research will be used for applied AI purposes. Simply Decision Intelligence is a lever that scales the wishes of human decision-makers.

We need to learn to think from systems thinking perspective to understand this kind of multidisciplinary field and its benefits.

3. Design Thinking

Design thinking is a human-centred approach to design — anchored in understanding customers’ needs, rapid prototyping, and generating creative ideas — that will transform the way you develop products, services, processes, and organizations. By using design thinking, you make decisions based on what customers really want instead of relying only on historical data or making risky bets based on instinct instead of evidence.

Design thinking brings together what is desirable from a human point of view with what is technologically feasible and economically viable.

• Desirability(People): What makes sense to people and for people?
• Feasibility(Technology): What is technically possible within the foreseeable future?
• Viability(Business): What is likely to become part of a sustainable business model?

3.1 How does Decision Intelligence helps Design Thinking?

Empowering the Design Thinking via Decision Intelliegence

Traditionally Design thinking has more relied on qualitative research. But due to the internet revolution and abundance of data. The role of quantitative research becomes vital now.

Bringing actionable Insights by Triangulating Insights from Qualitative data and Quantitative data without any bias is a herculean task. That’s where decision intelligence plays a crucial role.

Design Thinking is a human-centred approach where decisions are taken in a way that outputs are aligning with human desired choices and behaviours.

Decision Intelligence is a decision centred approach that helps the decision-makers to analyze, make & implement those decisions that are aligning with human desired choices and behaviours.

4. Four Benefits of Decision Intelligence for Business

4.1 Actual Data-driven decisions.

While 91% of companies believe that data-driven decision-making can boost their business growth, only 57% of them rely on their data. To get a competitive advantage, you have to correctly analyze the available data, make some predictions, and choose the best option. AI can take a better look at the data array and find invisible patterns and possible anomalies that can significantly influence the outcome.

4.2 Faster decisions.

According to the McKinsey survey, only 20% of organizations are happy with their decision-making speed. Others admit they waste too much time on making the right choice, which is not always actually right. AI decision-making systems speed up the process as much as possible since they are able to process huge amounts of data almost instantly.

4.3 Multiple problem-solving options.

AI-powered decision-making algorithms can also be quite flexible and highlight several outcomes of a certain decision when one of the parameters is changed. This feature can help the business to make the best choice from a multitude of options, taking into account their current goals and growth strategies.

4.4 Mistakes and biases elimination.

There are at least five types of biases that can directly influence business decision outcomes. Decision intelligence allows avoiding them all since the correctly programmed algorithm takes an ultimately objective look at the available data.

Decision Intelligence makes you and your systems smarter, when they are performed properly.