An abridged version of this post can be found here.

The value of information – reliable and timely information – has been highlighted during the coronavirus pandemic. In the UK we have seen debates over what constitutes a “test” and how we count the number of people sadly dying from the virus. At a national level in the UK, there are nearly 40,000 Government databases with nearly 600 covering areas of health and social care, and around 1000 for the environment (British Geological Survey, 379; Department for Environment, Food and Rural Affairs, 331; and, Centre for Ecology and Hydrology, 307)[1]. Running parallel to this huge volume of Government data, there are a range of private companies who draw upon these free, public datasets, repackage them and make them available in an easy to use way. In some cases, private companies have become default providers of national data.

Private information provider businesses are investing heavily to protect not only their IP but also how it is managed and shared e.g. Experian hold 246 patents which has been growing at 30+ per year for the last 4-5 years[2] and we see this trend in other major IoT businesses e.g. Amazon with 2035 patents (2010-2018)[3] and Google with a massive 51,000 patents[4]. While companies like Landmark provide packages of information for multiple aspects of most property transactions using data largely drawn from public sources[5].

More recently we have seen a major growth in the ability to collect and share information arising from the widespread application of the internet and the use of multiple satellite systems. The overall satellite data market is one where we have an oversupply of high-quality data and an its general underutilisation[6]. This situation has drawn in multiple new start-ups seeking to become a google-like in their dominance of specific activities along the data value chain.

Figure 1: The Data Value Chain structure and activities

GENERATION COLLECTION ANALYTICS EXCHANGE
Acquisition Transmission Processing Packaging
Consent Validation Analysis Selling
Capturers Collectors Developers/Enrichers Providers
Human Inputs*
Mobile
Computer
Wearables
Communication Services
CDN services
VPN providers
Software
Analytics software providers
Trading in
Information services
Market intelligence/ data providers
Transport apps
Price comparison sites
Data brokers
Devices
Smart meters
Connected vehicles
Industrial plant
Weather stations
Satellites
Transport
LPWAN
IoT networks
Network service providers
Analytics Services
Data processing service providers
‘XaaS’ services
Outsourced data analysis
Trading on
Media agencies
Website publishers
Ad exchange
System generators Enablers (often provided as a joint service) Non-Traded
Airline and hotel pricing
Insurance quotes
Financial trading
Network charging system
Data Storage
Storage
Data centres and management
Processing Infrastructure
Software and cloud-based platforms
Internal product and process optimisation**

Notes: * i.e. a person directly generates data and inputs via mobile, wearables, etc.; ** i.e. the organisation collects and analyses data for its own (value creating) activity only

Source: The Data Value Chain. Report by AT Kearney for the GSMA. June 2018.

Across the data value chain businesses seek to develop a strong and competitive position but in building a position they can often find it hard to gain traction by restricting their options, and hence their growth. Progress of these new start-ups has been patchy due to several reasons:

Underestimating the technological readiness (digital maturity) of users: Every business is developing their own digital strategy, and in some sectors, this is relatively well advanced (e.g. logistics, advanced manufacturing, financial services)[7] while in others the process is only starting to begin (e.g. forestry)[8]. This means both markets and their constituent businesses exhibit different levels of capability to recognise the potential of new digital data streams and products. At the same time there are different levels of ability to integrate packets of new data into an incumbent legacy IT system. Profiles of technological readiness have been well developed for many years and in many ways determine a businesses’ ability to rapidly absorb and use a new set of digital products which must be fitted into a series of part-digital processes[9]. Indicators exist which reveal a businesses’ readiness to make full use of new digital offerings, and some of these are readily available. For example, one indicator is the presence of an in-house platform which seeks to consolidate and make full use of internal and external data[10].  Investment in such platforms is readily shown in the staffing of key data management and analytical roles. Hunting job postings and job titles provides an indication of capability and these data are now becoming part of the HR digital infrastructure thanks to Burning Glass, EMSI, and LinkedIn Talent[11].

Focusing on products rather than a balance of products and services: There seems to be aversion to selling both products and services as a package in recognition of the capability of potential customers. Just look at what we see happening in manufacturing with the suppliers of automation systems as they seek to grow their businesses by providing extensive pre-sales input for their products and their integration but also in the provision of extensive post-sales support e.g. Rockwell Automation deploys a small army of staff to help their customers make use of their automation systems[12]. Why should this not be the case with data products? Most major consultancies have developed internal data analytical capabilities to complement their whole organisation strategic and operational solution assignments. Likewise, in the software industry, we have seen a growth in the size of ‘application’ staff required to implement a classic supply chain project. Though even this growth has greatly declined as alternative migration and transformation options have become available. We also see the trend in manufacturing where the growth of services is significant around core product sales and is dependent on digital technology capabilities e.g. aero-engines, wind turbines, etc[13].

Developing the potential of data and information provision: When does data become the product and the supplier having mastery of its capture and packaging more important across the value chain than providing a pre-determined product? As with all waves of technology and their diffusion across potential users, there is a secondary wave of capability development. Users at first are near dependent of their suppliers but as capability across a technology becomes core to their competitive success, they acquire and develop new full-time, internal capabilities. It is these staff who become the analysers and integrators of their employers’ needs with the data which are available[14].

Recognising the emerging plug and play options with machine learning (ML) and artificial intelligence (AI): Software solutions rapidly develop to meet new and emerging needs, and these are often openly shared. In the field of ML and AI it is becoming the case where tools are available on a plug ‘n’ play basis requiring a detailed knowledge of the underlying statistics and the data being analysed. We have seen time and again in the software industry the movement of skills between software developers, the software they create and the software users, and the growth of the pool of capability spread across all industries[15].

Fit with the customer digital strategy and plan: Many business into which data and information companies are selling have their own digital strategies and plans, and will probably have gone through a form of digital diagnostic process identifying the links between their key business drivers and the appropriate digital intervention and option[16]. Understanding the target customers thinking and their digital intentions, and where addition, new data and information might add significant value and benefit.

Conclusion: Exploring and realising the potential value to be derived from selling information and data into a rapidly changing customer base requires truly understanding individual customer capabilities, their real needs, and how parallel developments in analytics and data management is shifting the power imbalance between users and suppliers of data and information.

Notes

[1] Deloitte (2013) Market Assessment of Public Sector Information. Department for Business, Innovation and Skills. 235 pages.

[2] Company website – www.experian.co.uk

[3] Company website – www.amazon.com

[4] Company website – www.google.com

[5] Company website – www.landmark.co.uk

[6] Business Wire (2020) Global Satellite Data Services Market (2020 to 2026); In the Netherlands, TNO also has probed the utilisation levels of satellite data (www.tno.nl ) and it is worth look at Morgan Stanley’s reviews too (https://www.morganstanley.com/Themes/global-space-economy )

[7] McKinsey Global Institute (2017) Artificial Intelligence: The Next Digital Frontier? 80 pages; McKinsey Global Institute (2016) The Age of Analytics: Competing in a Data-Driven World. 136 pages

[8] Holmstrom, J. (2019) Digital Transformation of the Swedish Forestry Value Chain: Key Bottlenecks and Pathways Forward. Mistra Digital Forestry. 24 pages.

[9] Executive Office of the President of the USA, National Science and Technology Council, Advanced Manufacturing National Program Office (2013) National Network for Manufacturing Innovation: A Preliminary Design. Washington DC. GAO-19-409

[10] For example, ESRI, the GIS mapping systems business, has around 350,000 users worldwide (www.esri.com )

[11] Business-Higher Education Forum (2018) Foundational Skills of the Digital Economy. Developing the Professional of the Future. Report by Burning Glass. 62 pages; Manpower Group and DMDII (2018) The Digital Workforce Succession in Manufacturing. Digital Manufacturing and Design Job Roles Taxonomy. 69 pages; e-Skills UK (2013) Big Data Analytics. An Assessment of Demand for Labour and Skills, 2012-2017. Report for SAS. 36 pages; SQW and UKCES (2016) State of Digitisation in UK Business. Strategic Labour Market Intelligence Report. UKCES. 92 pages; Ala Mutka, K. (2011) Mapping Digital Competence: Towards a Conceptual Understanding. EU JRC 67075. 62 pages; Janssen, J. et al (2012) Experts’ views on digital competence: commonalities and differences. EU Joint Research Centre. Project No. IPTS-2011-JO4-46-NC. 9 pages.

[12] Rockwell Automation, for example, operates 12 remote support centres with 500+ engineers; 100+ consultants; 700+ field service professionals. Source: Rockwell Automation (2019) Workforce Support and Training. 8 pages. The total workforce of Rockwell Automation is 23,000, and so they have at least 6% of their staff devoted to user support.

[13] In aeroengines there has been a major shift of emphasis with major players shift their revenues (e.g. 52% of Rolls Royce revenues from civil aircraft engines comes from after sales services and margins on components average around 19%  – source: “Lucrative aircraft maintenance market scrutinised”, P. Hollinger and T. Powley, Financial Times, October 14th 2015). More widely there has been a major growth in the digital businesses being set-up by the major aeroengine, aircraft manufacturers. MRO engineering companies, and airlines.

Company Digital Development
AAR Digital Solutions, 2017
Air France/KLM Prognos, 2016
Airbus Skywise, 2017
Boeing AnalytX, 2017
GE Digital Collaboration Centre, 2015; FlightPulse, 2017
Honeywell Avasio, 2015
Lufthansa Technik Aviatar, 2017
Pratt & Whitney United Technologies Digital Division, 2017; EngineWise, 2017; FlightSense, 2018
Rolls-Royce R2 Data Labs, 2017; Availability Centre, 2017
SAFRAN Analytics Division, 2015; Predictive Maintenance Partnership, 2018

Source: “Big data: the race is on, but what is the end goal?” Paper presented by Joost Groenenboom of ICF International at the 14th Maintenance Cost Conference, Atlanta, September 19-21, 2018.

In wind energy, see Nielson, V.V. (2017) The Danish Wind Cluster: The Microeconomics of Competitiveness. Working Paper, Harvard Business School. Margins are higher in the servicing of wind turbines (17%) than in their manufacture (7%).

[14] see notes [11] and [12] and PWC (2019) Creating value from data. Why you need to take a strategic approach to maximise the value of your data. 16 pages.

[15] Accenture Operations (2018) See more, do more, be more. The Future Belongs to Intelligent Operations. 36 pages; Accenture (2020) Ready. Set. Scale. A Practical Primer on Scaling AI for Business Value. 48 pages; Dittrich, K.R. et al (no date) Databases in Software Engineering: A Roadmap. University of Zurich. 10 pages; Aery, M.K. and Ram. C. (2017) “A Review of Machine Learning: Trends and Future Prospects”, International Journal of Engineering Sciences, 25, 89-96; Greenwald, H.S. and Oerstel, C.K. (2017) “Future Directions in Machine Learning”, Frontiers in Robotics and AI, 3, 1-7; Cioffi, R. et al (2020) “Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends and Directions”, Sustainability, 12, 492 (26 pages); Business-Higher Education Forum (2018) op. cit.

[16] Baur, C. and Wee, D. (2015) Manufacturing’s next act. McKinsey and Co. The digital compass seeks to help companies find tools to match their needs relating a set of value drivers with Industry 4.0 levers. As a part of the Digital Entrepreneurship Monitor Project in Europe a similar tool was developed (Strategic Policy Forum on Digital Entrepreneurship, 2016, A digital compass for decision makers: toolkit on disruptive technologies, impact and areas for action. DG Internal Market, Industry, Entrepreneurship and SMEs.) In the UK, the Warwick Manufacturing Group (2017) developed An Industry 4.0 Readiness Assessment Tool and in Germany, Acatech (the Academy of Science and Engineering) developed the Industrie 4.0 Maturity Index (RWTH Aachen University) with its six-stage model.