How can creating a hassle map help you build your Digital Economy Platform Strategy?

Over the last six months, there has been a surge in interest in establishing digital platforms – primarily multi-sided and create an ecosystem to amplify/accelerate value capture. In most of these discussions, people can articulate the challenge they face from competition that will mean they need to cannibalise their existing business to survive the onslaught from start-ups. In a previous blog, I talk about creating certainty as one way of finding the purpose of the platform. There are a number of other techniques that are valuable here. You can use the 5 patterns from the HBR paper I co-authored to help identify the strategies.

Another very useful technique is hassle mapping. I first came across in Adrian Slywotzky’s Book – Demand. This forces you to think from the perspective of the final beneficiary of the platform. Creating persona using a technique such as IBM’s Design Thinking can help here. The hassle map will help in identifying the experiences or service that will be of sufficient value to be paid for.

There is a lot of information on hassle mapping on the web:

I am keen to learn from others in this area. How are you developing your strategies in this area?


Excellent example of data to outcome

Remember the 3 equations that summarise the logic for how data can create an outcome. I just came across an analysis done by Venkatesh Rao where he explores the 1854 London cholera outbreak. What is most interesting about this is how the raw data when put into the context in the final map shows how Dr Snow’s hypothesis is validated by plotting the data onto the map. The map in this case is providing the context and provides the insight that the Broad Street pump is most likely contaminated. His action to remove the pump validates the actions and cholera cases subside.

Excellent analysis and provides a valuable illustration of how data can create remarkable outcomes.

Is the removal of uncertainty at the heart of the digital economy?

Creating magical client experiences that become viral and scale exponentially summarises the approach of digital economy leaders. But what is a magical client experience? As I considered this question I started looking at examples of “magical experiences”.

The first example that came to mind was seeing the expected arrival of the next bus on a digital display at the bus stop. When I first saw this in London I remember discussing this with everyone I met that day. This is now readily available and anyone can build an app to provide this information using the TFL as an Open Data API. However, at the time this was a magical exerience.

The second example is using Flight Stats to check on the status of flight arrivals. I was travelling to the airport to meet visiting relatives and we were stuck in a traffic jam. Previously the only means of checking the flight status was to phone the airport, some provided an information service. The first time I was able to open the FlightStats website and see the arrival information, I knew that the flight was delayed and I did not need to worry about getting to the airport in time.

Considering Uber – what was magical about their experience? If you remember having to phone for a taxi and the controller saying the taxi will arrive in 10 mins. After 15 mins you would get frustrated and phone the taxi company again to ask when the taxi was due to arrive. The polite operator would re-assure you that the taxi was on the way and will be with you soon. You were relieved that you had requested the taxi with plenty of time as you had little confidence in predicting how long it would take for he taxi to arrive. With the Uber experience, you are able to see the location of the taxi, get details of the taxi and the driver. This removes the uncertainty that existed before and creates the magical experience.

As we consider ways to create value in the digital economy, the uncertainty that exists in a process or for an individual provides an ideal starting point. Questions such as, when will this device need servicing? What is the condition of the critical elements of the machine? Will the house be warm when I return? When did I last meet this person? These kinds of questions can help identify the outcomes that can be achieved and also estimate the value to be captured. It also helps identify the data required and the supporting systems/technology.

So is the removal of uncertainty at the heart of the digital economy? What is your view?

Is the economic value data derived from connections?

Life sometimes seems impossible to rationalizas discrete objects or eventsBut, if you start to consider the connectivity around youtapestry of links emerges based on, what appears to be, a finite set of rules. In this way hands are connected to arms and friendships are made through the casual sharing of ideas and kinship. Considering data like this can therefore help uncover previously hidden patterns. That is, if some established scientific approaches are appreciated alongside. For example, the four laws of thermodynamics point the way to some very useful techniques for understanding the inherent value in data (or more precisely information). 
At surface inspection such laws might seem detached from the idea of information, helping instead to describe physical properties like temperature, energy and entropy. But history tells us how they proved essential in the early development of Information Theory. This then provided a valuable way-point on the road towards more contemporary ideas on how information chooses to connect and structure in the wild. One such idea is that of scale-free networks – networks that maintain their essential characteristics regardless of how large they are. These can be found in the the distribution of popularity of websites across the World Wide Web, or the way that consensus spreads through a social network. Similar patterns emerge from the analysis of economics data, or most other fields of human endeavor.
Uncovering patterns like these is critical to the progression of digitalisation and extracting value from digital data at scaleOne can think of uses such as predicting market crashes and developing laws for prediction surely gives us an indication of the advances needed to uncover the underlying laws of human behaviour and the complex systems that intertwine our lives. Interdisciplinary collaboration is needed to advance our understanding of complex data sources, however. As explained by the authors of Superforecasting: The Art and Science of Prediction increased accuracy comes from combining insights from multiple sources and fine tuning the probabilities. 
To find your space in the digital economy a combination of skills and techniqueswe have depended on for many years, needs to be applied in creative ways to find those hidden patterns, develop the laws and thereby  help reduce inefficiencies and create value from the data.
Written in collaboration with

What is digital infrastructure?

Many governments and cities are discussing the importance of investment in digital infrastructure to maintain competitiveness. Sounds a very sensible and effective strategy, but this rises a number of questions.

What is infrastructure?

This might be obvious to many people, however, if you ask a group of people, you would get a range of answers. This wikipedia article elaborates the ideas of infrastructure and introduces telecommunications as a contemporary infrastructure.

What is a digital infrastructure?

Often government leaders assume that digital infrastructure refers to the internet or high speed broadband. The analogy is that of roads that allow the movement of goods between locations.  The digital infrastructure needs to allow the rapid movement of digital data between locations. So it is natural to think that super fast broadband is the answer. This analogy is a good starting point, but misses some differences between road networks and data networks.

Why does data need to move between locations?

Data needs to move to the place where a person can view or make use of that data. For example viewing a webpage or launching a connected app on a smartphone. This is at the edge of the network, however, there are much more complex interactions that are not seem by the consumers of data. Drawing connections between pools of data, analysing data requires the data to move to algorithms or algorithms need to move to the data. A search engine such as Google is constantly accessing websites to capture changes or identify new websites. Data is being backed to be re-accessed in the event of failures.

The cities data infrastructure needs to cater for a wide range of interaction patterns as well as data storage and  analysis. Using the road analogy, the design needs to consider car parking, placement of warehouses for trucks, and pedestrian zones. In the same way the city data infrastructure needs to consider data centres, open data platforms, mobile phone network access etc.

What is the value of the data and what infrastructure can the city afford?

Firstly, we need to remember that it is the use of the data that helps  create value. The three equations I described in a previous blog post help in identifying the role of digital infrastructure.

  1. Capturing data and making this available for analysis. Open data is particularly important in creating ecosystems of innovators.
  2. Access from analysis. Allowing all types of appropriate access to algorithms that will turn the raw data into information that in its own right can allow a range of decisions and uses.
  3. Creation of models or context system that identify new insights. Identifying anomalies, deviations or correlations that will inspire new actions. Ultimately the actions are the means of creating value from the data.

So the digital infrastructure for a city needs to cater for these uses and be priced at an affordable level for the organisations or individuals.

Cities should start by understanding what data is needed to satisfy the citizens, the business and institutions. Then the range of data facilities that will be satisfy these requirements. Then determine which of these elements are best provided via a city wide digital infrastructure.

Are digital platforms essential for growth?

The discussions at the Academy of Management annual meeting symposium – “A Multi-Disciplinary Perspective on Platform Ecosystems Research” made a strong case for platforms as a primary model to understand business models. Industry Platforms and Ecosystem Innovation also makes a strong case for the central role of platforms in evolution of a firm.

At the firm level using the 5 patterns described in “The New Patterns of Innovation” provide a simple way to identify innovation ideas and explore opportunities for growth. Considering these ideas as candidate platforms allows the full market potential of the idea to be explored. In addition, the challenges in creating the ecosystem of complementors, design of the digital platform, impacts to the current organisation and financial case can be created.

Adding the structure of Component Business Modelling  can provide an analytical framework to substantiate the financial case and develop the change programme.

These tools all seem to point to the digital platform as being the primary value creation tool for established business and there is clear need for unpacking this idea into a practical toolbox for managers.

Is there a business case for the next generation of digitisation?

As I reflect on the emerging 3 laws of digitisation, I have been asked many times if there is an overall “business case” for the shift in labour or GDP.  The way I think about this is to start with some very simple high level metrics.

Global GDP is 75 Trillion approximately. (World Bank)

The high-level industry breakdown extrapolating the UK data:

Agriculture 2%
Manufacturing 23%
Services 75%

Digitisation in the form of the pattern 1 : augmented product pattern  will allow the manufacturing industries to grow into the service industries and capture some of this GDP. If they are wildly successful, they could increase the GDP by 20% over a 20 year period and grow to 28% and take share from the service industries. This represents a $3.5tr shift in GDP as a direct result of the augmented product pattern.

For the service industries digitisation will lead to pattern 5: codify services. If we consider the service industries as consisting of three broad groups – creative, routine and personal. Creative includes graphic designers, engineers etc. Personal includes hair dressers, local retailers etc. Routine includes those with a significant element of routine work that is highly likely to be digitised using combinations of Cognitive technologies such as AI or Machine Learning. If we make the assumption that these represent an even distribution of the service industries i.e. 25% each of the 75% GDP. The impact on the 25% of the routine service industry is the question we need to explore further to understand the business case.

If the manufacturing industries are successful in their augmented industry pattern, we can see the routine service industry being reduced by 5%. The remaining 20% will be subject to further digitisation. A working assumption could be that 50% of this will be displaced in the 20-50 year period and open a new industry for services digitisation. If we assumed creating the platform for digitisation will be the key to success, and there will be an exponential adoption, we could argue 25% of the transformation will happen in the first 20 years and so a further reduction of 5% is possible.

The conclusion is that the digitisation industries have the opportunity to represent 10% of the global GDP over a 20 year period. Which based on current GDP value is $7.5tr.