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.


Use data and human intelligence in the right combination..

I just listened to Sebastian Wernicke’s Ted Talk: “How to use data to make a hit TV show”. The phrase Sebastian used which was “the data alone will not give you the future answer you need you use the intelligence from the brain as well”.

Often in discussions I have around the data savvy culture and implication of this evolution, people become fearful of their own role – thinking that these “data driven robots will replace everything they do and they will no longer be valuable”.  This is far from the reality. The human ability to ask the right questions, sense the weak signals and understand the implications are two unique skills that are seemingly impossible to replace at this stage.

What I took away from this video was how important it is for all us to develop and hone our question asking skills and start to learn how to best use the wide range to data analysis tools at our disposal. I would recommend reading “A more beautiful question” as this is the best book I found to develop your question asking skills. In addition, stopping yourself from doing the same thing in the same way is another approach I found valuable.

What are your thoughts on the balance between data driven and human intelligence/instinct driven?



Is there a set of Fundamental Data Types?

Is there a set of Fundamental Data Types?
Numerals and number systems have served us well as aids to quantify and represent the information around us, but today they are just one small part of the vast and complex digital shadow that is cast over our daily lives. Statistical methods help us explain how this shadow has evolved and can amazingly help predict its near-term future, its adjacent possibles. But why does that materially matter to us? So what?
Every piece of data that has ever been captured or created, had, has or will have a purpose, even if that purpose is not always immediately obvious. For instance it might help guide or trigger an action that will add value in the real world. That value might improve someone’s life, advance a product or process, or just turn a traffic light from red to green. The data associated with these acts is a catalyst to that value creation or is a characteristic of its outcome.
The term ‘catalyst’ is chosen to draw an analogy to chemistry and demonstrate how effective the idea of reacting data can be, when considered within the context of a fundamental data framework and is akin to the several branches of mathematics based on the same principle, using it to constrain how primitive number types work. A foundation of this framework, like with chemistry, has to be the elemental nature of data. That is its propensity to form in a fixed number of ways, or elemental types. These can also be thought of as exhibiting elemental properties. For instance, some data types must be highly volatile, while others must tend towards being inert, some must be heavy, while others lightweight and so on. Thinking like this not only simplifies the way that we perceive data, but also leads us to consider the ways in which these elemental data types might interact, or react, to make the world a better place. 
As for the fundamental data types themselves, we know of a few already, but there are many more out there to be labelled properly. Certainly we can add data associated with location and time to the list and we commonly refer to these as being geospatial and event data. But what about other types, such as personal, pricing, indexing data, and so on? Or perhaps more excitingly, the more exotic types of data, like metadata, analytical, aggregated and inferred data? The answers are nowhere near being clear yet, but certainly playing with data in this way is leading us to understand that our journey is only just beginning. For all we think we know about data, the real lessons are still to come. But wouldn’t it be fascinating if we had something like a periodic table of data, a touchstone we could turn to to help us experiment with data in safe, but as yet unimagined, ways…!
Co-authored with PhilTetlow: @DocPhilT

Are we nearing the end of human ingenuity?

After reading David Gann and Mark Dodson’s excellent article titled “There will be much less work in the future. We need to rethink our societies”, I feel myself pausing. Are we too entwined with the what we know and using this data to define the future? There is no doubt that there will be less of the current work in the future. However, I never cease to be amazed by the human ingenuity to create new meaningful employment.

Who would have guessed that digital games industry would exist in the form it does today?

The scale of wastage and inefficiency in all the systems that support our daily lives will new solutions to be created and will create new employment in ways we cannot imagine today. So I think we need to explore the opportunity for new employment created by the digital economy and help guide the evolution of these system to create new meaningful employment.

Soapbox mode off. Now time to think about how to create a better future.