• Fraser Harper

The business of information: looking for the golden threads?

A more detailed version of this post is available here

The coronavirus pandemic has shown just how unreliable and untimely data can be.  In the UK, experts can’t even agree on what constitutes a ‘test’ and how we count the number of people dying from the virus.

The UK Government has nearly 40,000 Government databases.  Almost 600 of these cover areas of health and social care, with around 1,000 covering the environment[1]. Running parallel, a wide range of private companies repackage these free, public datasets and make them available under commercial terms.  Landmark, for instance, provides packages of information for multiple aspects of most property transactions using data largely drawn from public sources[2].

Private information provider businesses invest heavily to protect their IP and how it is managed and shared.  Experian hold 246 patents, a number that has been growing at 30+ per year for the last 4-5 years[3].  Amazon gained 2,035 patents between 2010 and 2018[4].  Google currently holds a massive 51,000 patents[5].

More recently, we’ve seen a major growth in new satellite systems. But the satellite data market currently has an oversupply – and under-utilisation – of high-quality data[6].  In response, many startups are trying to become Google-like in their dominance of activities along the Earth Observation (EO) data value chain.

Looking for the golden threads along the Data Value Chain

Adapted from The Data Value Chain. Report by AT Kearney for the GSMA. June 2018.

Across the data value chain, businesses seek to develop strong and competitive positions. But they can often find their options are restricted.  Progress of these new start-ups has been patchy due to several reasons:

Overestimating market readiness

Businesses and sectors gain digital maturity at different rates.  Sectors like logistics are generally well advanced, others – like forestry – much less so.  Too often, startups find themselves at the bleeding edge of the slower-to-mature markets and end up dead in the water before they’ve even had a chance to swim.

We discuss this problem further in our follow-up post here.

Focusing on products only

Just as with physical and software products, data products often need extensive pre- and post-sales service wrappers to support implementations and feed the ongoing product marketing and development processes.  An over-eagerness to get data products to market – often forced on startups by hungry seed investors – often leads to early project failures and loss of market confidence.

See our follow-up post here.

Failing to ride the wave

At the leading edge, early adopters are heavily dependent on their suppliers. But as capability builds, the early adopters can become a secondary wave that is often unrecognised by the supplier, resulting in lost opportunities for partnerships, OEMs and other developing market channels.

Failing to recognise emerging options

Software solutions rapidly develop to meet new and emerging needs, and these are often openly shared. In the field of AI, plug-and-play tools like Tensorflow can greatly accelerate the ML algorithm-building process but often go untapped by startups, who stick with their original homegrown algorithms without taking advantage of these productivity improvements these tools and libraries can offer.

Not fitting with clients’ digital strategy and plan

Many startups – particularly those coming from academia – run the risk of building solutions looking for problems unless they recognise that their target clients have often developed their own digital strategies and plans.  Recognising that leads to a process of understanding where new data and information could add significant value and benefit.

Our Comment

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

[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 )

#DataMethodology #DataStrategy #DigitalMaturity

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