Decision Model Innovation: The next frontier in creating competitive advantages in converging industries
DECISION MODEL INNOVATION: An Introduction
The RDA Business Environment
Many companies that I work with are strongly affected by three major developments:
These three developments ( Robotics & Globalization, Digitization, Access-based Business Models) lead to an increasing “Commoditization of Products and Services”. For example, the automotive sector has been among the first to both experience a massive industry convergence into the so-called mobility sector and to fully embrace the potential of robotics to harmonize their manufacturing processes around the world and locate their assembly plants next to their major sales markets. This development also allowed OEMs from emerging markets to catch up in quality levels and at least reduce the margins of established players in the BRIC countries. Similarly, the sharing economy is reducing the number of cars sold and, even more important, disconnecting the actual mobility consumer from a specific car brand (e.g. a subscriber to a ‘premium’ car sharing service doesn’t care whether the car he/she gets access to for each ride is a BMW, Mercedes, or Audi). In addition, digitization in the form of artificial intelligence, massive cloud computing or detailed digital maps and new data transmission standards allows for new forms of mobility such as autonomous driving; i.e. the ultimate form of transparency that a mobility consumer can get from a car).
As a consequence of this “Commoditization of Products and Services” through industry convergence, companies face the problem to identify new ways to create customer value & capture in order to sustain their profit margins in the future.
Our suggestion is to think of “Decision Model Innovation” as a posssible answer.
DECISION MODEL INNOVATION (DMI)
In the past, companies have created COMPETITIVE ADVANTAGES through product/service innovation, process innovation and business model innovation.
However, with the rise of the DATA-driven economy & KNOWLEDGE societycompanies need to consider another form of innovation. In our observations of industries and our interactions with companies we have experienced that companies are increasingly struggling to find ways to differentiate their value proposition from competitors in converging industries due to the “Commoditization of Products and Services”. During the last decades, companies first focused on product innovation, later in combination with process innovation, and finally followed the concept of business model innovation.
We do not propose DECISION MODEL INNOVATION (DMI) as being a completely new model compared to business model innovation but differentiate DMI as follows from product, process and especially business model innovation:
From FACTS to ACTION: A Starting Point for Discussions
Ever since I have been teaching “Decision Intelligence” to students and executives I integrate an exercise where I let them think about a few key terms that strongly affect the way we see and talk about the world today. These terms include Data, Information, Knowledge, Insight, Decision, Action as well as Facts, Experience, Expertise, Wisdom, Opinions among others. As much as we normally disagree in class about the exact definitions and the interdependencies between these terms, we also agree on the importance to create an advanced understanding in order to navigate in a Knowledge Society & Data-driven Economy.
In the following, I summarize the results of multiple discussions with students & executives with NO CLAIM to have the best possible framework (i.e. understanding) already.
Following really smart people like Cassie Kozyrkov, Chief Decision Scientist at Google, one of the most promising but also challenging tasks in today’s world is to turn DATA into ACTION. In order to do that, I have created a FRAMEWORK that summarizes the logic we have developed.
Let me explain its elements:
We have three levels:
Let me first introduce the RESULTS Level including:
While the RESULTS are rather easy to define and understand (although multiple variations exist in the academic and real-life world for each term), the ACTIVITIES that actually transform DATA into ACTION are much more difficult to agree upon or even define. In the following I try to provide an framework that might add to your understanding and your own interpretation of how to turn DATA into ACTION. The key ACTIVITIES are:
I want to emphasize again that the proposed framework is nothing else than a suggestion based on the numerous discussions with the students and executives in my “Decision Intelligence” courses at the University of St.Gallen, Switzerland. The framework consists of three levels:
Your suggestions to improve the framework are highly appreciated at: firstname.lastname@example.org
Decision Intelligence for Farmers: The Pioneering Role of IoTAg in Australia
A great example of Decision Intelligence is IoTAg, an Australian start-up in the agri-tech sector.
IoTAg supports farmers through different forms of insights to support their decision making. The core technology they focus on is called LoRa (Low Power Wide Area Network). LoRa allows IoTAg to track the movement and behavior of cows based on license-free networks that they can establish for small and large farmers according to their intelligence requirements. However, IoTAg's focus on LoRa would not be sufficient to provide all necessary insights that their customers - the farmers - really need. Therefore, IoTAg has decided to develop a business ecosystem which includes other companies such as SatSure, another company with a strong Decision Intelligence focus, offering insights based on advanced satellite data analytics.
For more information about IoTAg's ongoing work and that of similar companies: SproutX Accelerator
Their famous "Bring home the bacon" pitch you can find here: "Bring home the bacon"
Mr. Sauerbrey: Why do executives need a Decision Intelligence Navigator?
Dr. Moser: We understand “Decision Intelligence” as the capability of an individual or organization to create a FIT between its intelligence requirements in form of data, information and knowledge and its intelligence processing capacities in the form of intelligence gathering, analysis and communication activities.
Our Decision Intelligence Navigator is simply put a mental model that allows you…
Mr. Sauerbrey: How does the Decision Intelligence Navigator support executives?
Dr. Moser: In today’s world companies face ever-increasing levels of uncertainty, ambiguity, industry dynamics and industry convergence – a popular term for this is “VUCA”. It simply means that the business context of companies in almost any industry is changing dramatically.
Now, if the business context is changing fast and substantially, a company needs be able to adapt the way its gathers and processes intelligence in order to understand how the political, macro-economic, social and technological environment is evolving, which industry dynamics are still relevant or changing and what kind of resources are key for a company to create a value proposition for its customers in the future.
However, many companies have not invested into conceptually sophisticated and technically advanced intelligence gathering and processing structures and activities often leading to an information overflow and, in the worst case, to strategic decision based on irrelevant information – which still works for companies if they are operating in protected industries or nothing is changing. But where is this still the case today?
So what the Decision Intelligence Navigator does is to guide executives along the entire process to make sure that they and their companies focus on the insights they really need and to improve that these insights are turned into – let’s call it – wise decisions.
Mr. Sauerbrey: Let us then talk about the different elements of the Decision Intelligence Navigator. Where do we need to start?
Dr. Moser: Well, the first step of the Decision Intelligence Navigator is to understand the intelligence requirements that you or your company have. For this purpose we have developed different tools and concepts to create - what we call - the right “Contextual Mindset”.
In a second step you need to improve your Framework Proficiency and your Intelligence Access to create the insights you need to make specific decisions and finally you work on your Decision Proficiency to ensure that your valuable insights are turned into bias-free decisions.
Ok, you have talk a lot about changes in the business context so far – why is the understanding of changes in business context so important for executives?
Some executives still tend to deny that the context is changing much. But many executive strongly agree that
Mr. Sauerbrey: Ok, given that I have developed the contextual mindset I need - how do I proceed then?
Dr. Moser: Companies and executives then have to find a way how to address all the information requirements they have identified in order to make decisions – for example about their future business model in China or how to integrate Artificial Intelligence applications into their value chain.
As briefly mentioned earlier, this requires two things: Framework Proficiency and Intelligence Access.
Mr. Sauerbrey: So, when I have created the insights I want, I still have to make decisions right?
Dr. Moser: Right, we call this element of the Decision Intelligence Navigator “Decision Proficiency”. Decision Proficiency is simply a set of tools and concepts which helps executives to turn insights about the future business context into specific resource allocation decision down to the functional level but also to support executives to avoid any bias that is likely to occur during the actual analysis and decision making process.
Mr. Sauerbrey: Let me try to wrap it up shortly: The Decision Intelligence Navigator supports executives to reflect about the business context changes that they currently face, to identify those dimensions of competition that matter in the future and to access relevant data, information and knowledge to really understand them and finally to turn these insights about the future into functional consequences today trying to avoid any bias during the decision making process.
A few years ago, when I first wrote about Decision Intelligence and presented my ideas during guest talks at universities abroad the concept was basically unknown. Today, it looks quite different and many companies and consultants of all sorts claim to have the 'right' definition of Decision Intelligence. We don't do that although I believe that we have been among the first ones that have used this term - a Google Search a few years ago resulted in less than 10 hits for the term "Decision Intelligence" but today there are thousands.
Especially since Mastercard has introduced its "Mastercard Decision Intelligence" solution a few months ago the number of hits has risen dramatically. However, I believe that we need to clarify what "Decision Intelligence" really means. There is an interesting website about a one way to understand "Decision Intelligence" by Dr. Lorien Pratt. I really appreciate her work although I slightly disagree with her definition of Decision Intelligence. Then there are a few other companies that have early picked-up this term but - based on my understanding - have not really developed a consistent concept for "Decision Intelligence".
In short, I believe that similar to terms like "Business Intelligence", "Competitive Intelligence", "Contextual Intelligence" etc. we will never reach a clear definition of "Decision Intelligence" that everybody agrees upon. However, I believe that our understanding of "Decision Intelligence" as the FIT between the intelligence (i.e. data, information, knowledge, insights) requirements of an organization/individual and the intelligence processing capacities of the same is flexible with respect to the tools and concepts it requires but still has a clear objective and projected outcome - the creation of competitive advantages.
This is why I believe that our "Decision Intelligence" approach is significantly distinctive from others that have a tendency to focus too much on (advanced) analytics and ignore the important questions upfront - for example, what insights (defined as knowledge required to make a decision) do I really need and which frameworks and intelligence access to I require to succeed.
The DECISION INTELLIGENCE (DI) Navigator
Dr. Roger Moser
Dr. Moser is an entrepreneurial academic who focuses on creating the FIT between the intelligence requirements of decision-makers and organizations and their intelligence processing capacities. He defines this FIT as the DECISION INTELLIGENCE of individuals and organizations.