DECISION MODEL INNOVATION: An Introduction
The RDA Business Environment
Many companies that I work with are strongly affected by three major developments:

1. Robotics & Globalization: The automation of more and more physical work in combination with global trade & investment opportunities lead to a situation where companies are increasingly locating their production sites as close as possible to their respective sales markets because the cost of human labour becomes less relevant in such decisions. Even more important, especially companies from/in emerging markets can faster and more effectively produce the same quality levels as their competitorsoriginating from developed markets.
2. Digitizaiton: Digitization including Social Media, Industry 4.0, Industrial Internet of Things etc. is finally always resulting in increased Transparency and enables companies across industries to increasingly collaborate based on a common denominator – data.
3. Access-based Business Models (Sharing Economy): The Financial Crisis and the ongoing Climate Change Challenges are pushing for different consumption models such as access-based business models; i.e. Sharing Economy. This changes the way people value brands, quality and customer experience. 

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: 

• DMI emphasizes the importance of DECISION MAKING SUPPORT for your CUSTOMERS as the KEY VALUE PROPOSITIOIN of your own company given the increased transparency that can be achieved in a DATA-driven economy. 
• DMI emphasizes the importance of business ecosystems to integrate all necessary technologies required for the continuous improvement of your DECISION MAKING SUPPORT for your customers = DECISION MODEL INNOVATION.
  
  

In sum, DECISION MODEL INNOVATION…

• …focuses on creating value for a company’s stakeholders based on the transparency that new developments along the DATA VALUE CHAIN offer (Data Value Chain: data gathering (i.e. sensor technologies), data transmission, data storage, data analytics, data visualization).
• …provides a company with the opportunity to differentiate itself from competition in converging industries; when facing an increased "Commoditization of Products & Services". 
• …requires a company to consider itself as a member of multiple ecosystems serving multiple customer segments - each with a different decision-making support value proposition and value capture strategy.
  
  

More on the implementation of Decision Model Innovation and case examples in the next posts.​

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: 

• MINDSET
• ACTIVITIES
• RESULTS

RESULTS
Let me first introduce the RESULTS Level including:

• DATA: Facts collected for a purpose
• INFORMATION: Data arranged in a structure
• KNOWLEDGE: Information in a specific context
• INSIGHTS: Knowledge relevant to make a specific decision
• DECISIONS: A conclusion/resolution reached after considering all insights
• ACTIONS: Process of doing something, typically to achieve an aim

 
 ACTIVITIES
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:

• CREATING FACTS -> DATA: When human beings create facts and collect & store them in one way or the other, they actually create data.
• STRUCTURING DATA -> INFORMATION: Data that is stored in a structure way in order to further process it can be called information.
• PUTTING INFORMATION into CONTEXT -> KNOWLEDGE: Information that is interpreted and put to use in a specific context might be labelled as knowledge.

So far, most people would probably agree. However, in today’s Knowledge Society & Data-driven Economy much knowledge has actually become a commodity-like product as it is basically freely available through the internet or the costs of accessing it via a data bases are marginal. Thus, in today’s world that is also often characterized by an information overflow any decision maker’s major challenge is to apply filters to sort out the ‘noise’ and focus on the essential parts of knowledge in order to make a specific decision. In our framework, we label knowledge that is relevant for a specific decision as insights. While many of my students and executives agree on this understanding, we have never found, so far, a common understanding of HOW we should describe the FILTER that is separating relevant from non-relevant knowledge for a specific decision-making challenge. After numerous discussions I have not been able to come up with something that everybody could agree upon. However, a framework that most students & executives could somewhat agree upon looks as follows:

• APPLYING WISDOM to KNOWLEDGE -> INSIGHTS

To many executives and students, it is very uncommon to use the term wisdom in a business context and specially to label it as the filter to sort out the ‘noise’ from a constant knowledge and information flow. However, let’s have a closer look how wisdom is aggregated.

• AGGREGATING PROFESSIONAL EXPERIENCE & EXPERTISE -> WISDOM

Simply put, wisdom is a combination of professional expertise in the form of rich knowledge and professional experience that has been accumulated over time. Based on the insights that a decision-maker has selected through his/her wisdom he/she has to make a decision.

• APPLYING OPINIONS to INSIGHTS -> DECISIONS

The question then arises how a single or a group of decision-makers filters again the available insights to make the final decision. This kind of filtering is mostly happening in the form of personal and semi-professional opinions (as the professional experience & expertise in the form of wisdom has already been applied to select the relevant knowledge; i.e. insights). Opinions about what matters almost automatically give a weight to each single insight when considering them in the actual decision-making.

• AGGREGATING PERSONAL EXPERIENCE & EXPERTISE -> OPINION

Similar to our understanding of wisdom, opinions are formed based on a combination of personal experiences and expertise. They form what we might call the opinions of a person which again influences how each single insight is put into consideration when making a specific decision.

• IMPLEMENTING DECISIONS according to your OBJECTIVES -> ACTIONS

Finally, decisions need to be implemented. This requires the allocation of time, money and other resources which are normally prioritized according to the major objectives that the decision-maker(s) have.

MINDSET
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:

• Results that start with DATA and end with ACTIONS.
• Activities that primarily take the form of FILTERS
• Mindsets that shape the configuration of these FILTERS

These mindsets might primarily consist of a private and a professional mindset that are partially built upon the private and professional experiences & expertise as well as the personalities of the decision makers.

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…

• …to analyze the intelligence requirements you have based on the changes in your business context
• …to reflect on the insights that you need to make decisions
• …to improve the way you access different forms of data, information and knowledge and finally…
• …to avoid most biases when turning insights into actual decisions

Based on our research, we can – and our corporate partners also agree – empirically prove that in today’s ever changing world those companies are primarily able to create competitive advantages which are able to create an optimal FIT between the levels of intelligence requirements in the multiple business contexts they are operating in AND their intelligence processing capacities.
 
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

• there is an increasing uncertainty due to numerous political, social and technological changes,
• that there is increasing ambiguity – this means that even if we know what we need to know we face the challenge to draw the right implications
• that the dynamics in almost any industry are dramatically increasing –  and finally
• that we see many industries converging and - as a result – the way they operate, collaborate and make money.  

As result, the rules of the game – somethings even the game – in most industries are changing fast and substantially but companies and executives have only limited resources to gather and process data, information and knowledge. Therefore, executives need to first develop an understanding where they and their companies stand we respect to uncertainty and ambiguity in their business context as well as the level of industry dynamics and industry convergence. That’s what we call the “contextual mindset” of an executive.
 
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.

• Framework Proficiency means that executives are able to select suitable business context analysis frameworks based on the Contextual Mindset they have developed in step 1. For example, when I ask executives in our courses to conduct an industry analysis about 90% of them turn directly to Porter’s 5 Forces analysis.
  Why?

Because they have often never been exposed to any other industry analysis tool, it’s working just fine in many cases and – to be honest – many executives are not really aware what Porter’s 5 Forces tool is actually measuring…it’s power only.

• The problem now comes with industries that are converging or where other profit mechanisms rather than power towards suppliers, customers, or competitors matter most.
• I then often ask them whether they are aware of a concept called “Key Stone Player” based on the business ecosystem analysis approach. I guess that more than 75% of them are not aware of this concept. However, if you want to understand how companies like Apple, UBER, Google and many others are building their business and market power, Porter’s 5 Forces is still useful but does not identify the industry mechanisms that really matter in the sectors these companies operate in. 
  
• The second element is then “Intelligence Access”. It simply means that there is little impact of your advanced “Framework Proficiency” if you are not able to actually get the data, information and knowledge you need for each element of your business context analysis.
• In short, we differentiate between big data and small data in the form of access to experts.
  Here at the University of St.Gallen we are currently developing a platform where we work with companies in order to understand how they can leverage different forms of intelligence access and integrate it into the business context analysis – efficiently and conveniently.

The combination of Framework Proficiency (this means filtering and selecting the most important dimensions of the business context) and Intelligence Access (which means that I can also generate the necessary data, information or knowledge for each dimension of my business context analysis) – this combination then leads to insights. Insights to make decisions.
 
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.