Digital Economy Dispatch #039

6th June 2021

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Revitalizing Your Data-Driven Innovation Strategy

Over the last decade, companies such as Amazon, Microsoft, IBM, Google, and Facebook have created vast, interconnected computing utilities across the globe. These have rapidly become the information hubs of the digital age. The data centres at the heart of these networks are fed by Exabytes of data generated by billions of consumers who have benefited from ubiquitous access to services through internet-connected mobile devices including tablets, smart phones, IoT sensors, and all kinds of wearable technologies.

To take advantage of this, individuals, organizations, and governments are looking for new ways to make use of this infrastructure to manage the vast amount data being captured and shared. Across a range of scenarios, the data brings insight for improving existing operating practices, learning more about current stakeholder behaviours, and driving new business growth.

Yet, despite the massive technological advances in interconnectivity, computer processing, and storage, the data-driven innovation required to improve the quality of decision making remains largely elusive. Largescale studies have concluded that “investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision making”. They highlight that despite massive amounts of data bringing unlimited insights and opportunities, we remain far away from being able to harness that potential into consistent meaningful actions.

To create economic and social value from the huge collections of information now being stored in global information hubs, we as individuals, communities, and businesses need to convert this powerful and ever-expanding resource into meaningful input that can help us with everyday decisions rather than confuse and overwhelm our lives. Some of these insights may allow us to address narrow operational concerns. However, across a range of domains the data being generated can help to support us in addressing some of the biggest questions of our age, for example:

  • Does greater insight into energy consumption via smart metering decrease waste and change consumers’ behaviour to drive a more sustainable approach to energy management?
  • Will the connected car enable us to reduce congestion in cities and avoid accidents?
  • Can banks’ knowledge of financial markets and individual spending patterns be used effectively to create a fairer society?
  • Does the adoption of wearable health monitors lead to earlier interventions to increase wellness and ensure a longer, more active old age at a price we can all afford?

While such questions appear to be deceptively straightforward, we still have a long way to go to make meaningful progress. Smarter approaches to data-driven decision-making require organizations to build the capabilities needed to bring together multiple data sources, filter out errors in the data, extract meaningful insights from repeated patterns, and so on. This broad approach to data-driven insight is often referred to as Machine Intelligence (MI).

MI could well be the integrative mechanism that transforms so much data into genuine sources of new value. It can be seen as a ‘killer app’ for the digital economy. MI holds out the promise of being able to make sense of such large volumes of data by exploiting a combination of machine learning and AI to yield entirely new sources of value. It encompasses natural language processing, image recognition, algorithms, and other techniques to extract patterns, learn from these by assessing what they mean, and act upon them by connecting information together.

MI is inevitably disruptive by nature. Hence, it is essential to recognize that MI and its associated digital business models may pose significant challenges, which can be addressed in the following ways:

  • Changing the way data is collected and processed. It is important to move away from localized databases associated with specific applications, and form larger data lakes that can be exploited by new layers of intelligence essential to MI success.
  • Ensuring you offer a flexible, scalable technology infrastructure across your organization. Business success requires integrating the many applications that constitute a complex set of workflows by using open, component-based techniques as well as connected platforms such as those provided by Google, Microsoft, IBM, and others.
  • Tackling the many cultural barriers that persist in your organization. Previous technology investments often constrain thinking and encourage business leaders to cling on to ageing business models and supporting processes. New thinking is required.

MI-based innovations will inevitably put stress on existing organizational structures. Leadership is always a critical element of any major organizational change, and until the key business leaders are convinced of the need for radical change, little progress will be made. Companies as diverse as major technology providers, largescale business-to-consumer services providers, and industrial business-to-business solutions providers are already seeing the impact of such changes, illustrating that effective progress can be made when the corporate culture is receptive to new ideas.

So, where should organization’s aiming to drive their data-driven innovation place their focus? The convergence of MI, big data, and 5G-powered interconnectivity in a rapidly accelerating fashion signifies the need for firms to identify their priorities and start allocating their resources to achieving competitive advantage. However, there will be many challenges to overcome before the full potential of such technologies can be realized. Furthermore, different companies across a range of industries are at different phases of their journey to understand and adopt MI.

Progress requires a clear plan. Business leaders and management should consider exploiting current technology-driven developments through three categories of activities: research, experimentation, and execution. Here are a few of the high-level elements that are core to a successful data-driven strategy.

Research

  • Familiarize your organization with potential applications of MI-based digital technologies and consider where high pay-off areas might be within the organization.
  • Create a clear map of the MI landscape as it affects your organization’s view of the industries in which it competes, and examine new startups in your sector as early signals of market change.
  • Examine new MI-based business models that could challenge the existing status quo or represent green-field opportunities.

Experimentation

  • Engage in open, honest discussions with your teams about the extent of data-driven decision-making within the organization, and experiment with new ways that data could be obtained, curated, and used.
  • Conduct experiments or innovation sprints with appropriate partners to evaluate possibilities prior to scaling to identify minimum viable solutions.
  • Engage in small-scale pilot deployments of MI that focuses on learning about the processes, skills, and impact on the organization.

Execution

  • Ensure that key roles and functional areas in your business are set up to act as appropriate entry points for MI-based innovations by engaging with start-ups and technology leaders (e.g., CTO and CIO).
  • Create time in projects to build stories around success and failures that inspire and motivate teams to gain a shared understanding and vocabulary about MI and its supporting technologies.
  • Promote internal successes across the organization to highlight behaviours and approaches to MI that the organization wants to encourage.

Digital Economy Tidbits

The Cost of Cloud: A trillion dollar paradox. Link.

A quite fascinating article about the long term costs of enterprise cloud approaches. No-one is arguing the value of migrating to the cloud for many of its benefits…including initial cost savings. But as this article points out, longer term it is not so simple to calculate.

Yet most companies find it hard to justify moving workloads off the cloud given the sheer magnitude of such efforts, and quite frankly the dominant, somewhat singular, industry narrative that “cloud is great”. (It is, but we need to consider the broader impact, too.) Because when evaluated relative to the scale of potentially lost market capitalization — which we present in this post — the calculus changes. As growth (often) slows with scale, near term efficiency becomes an increasingly key determinant of value in public markets. The excess cost of cloud weighs heavily on market cap by driving lower profit margins.

The example they use of Dropbox is quite compelling:

When the company embarked on its infrastructure optimization initiative in 2016, they saved nearly $75M over two years by shifting the majority of their workloads from public cloud to “lower cost, custom-built infrastructure in co-location facilities” directly leased and operated by Dropbox. Dropbox gross margins increased from 33% to 67% from 2015 to 2017, which they noted was “primarily due to our Infrastructure Optimization and an… increase in our revenue during the period.”

 

NHS GP appointments app announces £3bn US stock market listing. Link.

An NHS appointments app….floated via a SPAC….and being valued at £3B. There are so many things about this story that I don’t understand…and don’t want to think about too deeply.

A virtual GP appointments app used by the NHS has announced a £3bn US stock market listing after agreeing to a blank-cheque company merger that will net its British-Iranian founder almost £1bn.

Babylon’s reverse merger with Alkuri Global, a New York-listed special-purpose acquisition company (Spac), makes it thelatest firm to take advantage of a growing Spac trend that makes it cheaper for private companies to go public.

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