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Three Reasons All Corporate Boards Need Someone Who Understands Both Analytic Innovation and Analytic Strategy

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According to a 2019 report from CB Insights [1], between 2010 and 2019 there were 635 AI acquisitions. The acquisitions break into three groups, as can be seen in the visualization below (Figure 1) from CB Insights. Facebook, Apple, Google, Microsoft, Amazon (FAGMA) and Intel accounted for 67 acquisitions, each making 7 or more acquisitions during the period 2010 to August 2019 (Group 1). Fifty two companies made between 2 and 6 acquisitions during this period (Group 2), and 431 companies made a single acquisition (Group 3).

Figure 1 This histogram from CBInsights show the number of AI acquisitions that a company made during the period 2010 – August 2019. Facebook, Apple, Google, Microsoft, Amazon and Intel accounted for 67 acquisitions, each making 7 or more acquisitions during this period. Fifty two companies made between 2 and 6 acquisitions during this period, and 431 companies made a single acquisition. Source: CBInsights, retrieved from: https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/ [1]

Reason 1. Analytic and AI strategy is too important for a company not to have someone with board level experience in this area.

Whether it is to ask critical questions about the single AI acquisition that 431 companies did between 2010 and 2019 or to ask critical questions about a company’s own analytic and AI efforts, a board member who has experience overseeing deployed analytic and AI applications is important.

It is important to note here the difference between someone who has experience in the entire life cycle of analytics from project start to deployment versus someone who only has experience developing analytic models. This is because most analytic projects don’t get deployed and don’t bring the expected value when deployed.

I taught a course at the University of Chicago’s Booth School of Business for three years called the Strategy and Practice of Analytics. One of my favorite case studies was HP’s acquisition of the AI company Autonomy in 2011 for $11.1 billion. As the New York Times reported a year later:

“Last week, H.P. stunned investors still reeling from more than a year of management upheavals, corporate blunders and disappointing earnings when it said it was writing down $8.8 billion of its acquisition of Autonomy, in effect admitting that the company was worth an astonishing 79 percent less than H.P. had paid for it [2].”

Reason 2. Board members with experience developing, deploying and operating complex analytic projects have critical experience using technology to innovate, not just operate.

All boards understand the importance of having board members that understand how to manage operations and risk to align with corporate strategy and drive financial performance. On the other hand, these days using technological innovation to align with corporate strategy and drive financial performance is also important. Successful senior leaders in analytics and AI generally have a deep understanding of technology innovation and how to use it to drive financial performance. See Figure 2.

In contrast, it is common for many CIOs to spend their time as CIOs managing IT operations, reducing IT costs, and using IT to quantify and control risks, rather than using IT to drive technology innovation and drive financial performance.

When successful, good analytic leaders find ways to use data, analytics and AI to change a company, not just run it. Having this perspective on a board is very valuable, as is experience with analytic projects that leverage continuous improvement and analytic innovation.

Figure 2. Traditional CIOs who serve on boards can help boards understand how to use IT to improve the efficiency of operations and reduce risk. Senior technical leaders who understand data and analytics can help boards understand how technical innovation can align with corporate strategy and improve financial performance.

Reason 3. An analytic perspective for a board member helps with evaluating cybersecurity and digital transformation, both critical topics for many boards.

A 2017 Deloitte study found that:

“high-performing S&P 500 companies were more likely (31 percent) to have a tech-savvy board director than other companies (17 percent). The study also found that less than 10 percent of S&P 500 companies had a technology subcommittee and less than 5 percent had appointed a technologist to newly opened board seats. … Historically, board interactions with technology topics often focused on operational performance or cyber risk. The Deloitte study found that 48 percent of board technology conversations centered on cyber risk and privacy topics, while less than a third (32 percent) were concerned with technology-enabled digital transformation.” Source: Khalid Kark et al, Technology and the boardroom: A CIO’s guide to engaging the board (emphasis added) [3].

A senior analytics executive with experience supporting cybersecurity is a double win for a board. Even without this experience, behavioral analytics plays an important role in the cybersecurity for a large enterprise, and senior analytics executives almost always have experience in behavioral analytics.

The remote work caused by the COVID-19 pandemic has accelerated the importance of board level understanding of digital transformation. As a Wall Street Journal article from October 2020 states it:

“If you didn’t have a digital strategy, you do now or you don’t survive,” said Guillermo Diaz Jr. , chief executive officer at software firm Kloudspot, and a former chief information officer at Cisco Systems Inc. “You have to have a digital strategy and digital culture, and a board that thinks that way,” he said. Source: Angus Loten, Many Corporate Boards Still Face Shortage of Tech Expertise, Wall Street Journal [4].

It is hard to image a digital strategy without an analytic strategy. Chapter 8 of my Developing an Analytic Strategy: A Primer [5] describes seven common strategy tools that can be easily adapted to develop an analytic or AI strategy, including SWOT, the Ansoff Matrix, the experience curve and blue ocean strategies.

References

[1] CBInsights, The Race For AI: Here Are The Tech Giants Rushing To Snap Up Artificial Intelligence Startups, CB Insights, September 17, 2019. Retrieved from: https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/.

[2] James B. Stewart, From H.P., a Blunder That Seems to Beat All, New York Times, Nov. 30, 2012. Retrieved from: https://www.nytimes.com/2012/12/01/business/hps-autonomy-blunder-might-be-one-for-the-record-books.html

[3] Khalid Kark, Minu Puranik, Tonie Leatherberry, and Debbie McCormack, CIO Insider: Technology and the boardroom: A CIO’s guide to engaging the board, Deloitte Insights, February 2019. Retrieved from: https://www2.deloitte.com/us/en/insights/focus/cio-insider-business-insights/boards-technology-fluency-cio-guide.html

[4] Angus Loten, Many Corporate Boards Still Face Shortage of Tech Expertise: But more CIOs are expected to earn a seat as the pandemic forces companies to lean on digital, Wall Street Journal, Oct. 12, 2020. Retrieved from: https://www.wsj.com/articles/many-corporate-boards-still-face-shortage-of-tech-expertise-11602537966

Do You Need a Grand Strategy in Analytics?

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Figure 1: From lean to grand analytic strategies.

In foreign affairs and national defense, especially among academics, it has becoming more common to talk about grand strategies. There is a very popular course at Yale University by John Lewis Gaddis called On Grand Strategy, and in 2018 he published a book worth reading with the same name.

An emerging definition for grand strategy for state is “something that has the characteristics of being long-term in scope, related to the state’s highest priorities, and concerned with all spheres of statecraft (military, diplomatic, and economic) [Silove 2018].” Of course, the problem is that a strategy in general is concerned with the long term decisions that an organization makes to further its priorities, so this definition doesn’t help as much as you might hope. See the Appendix for a definition of analytic strategy.

There is though some common themes that emerge if you review some of the recent articles on grand strategies [Biddle 2015, Silove 2018; Gaddis 2018]:

  • Grand strategies are longer term than typical organizational strategies, for example, 20 or more years. Fifty and hundred strategies are not unusual in China.
  • Grand strategies cover more domains than typical organizational strategies. For example, grand strategies for states typically cover military, diplomatic and economic strategies, and not just one of these.

At the Other Extreme – A Lean Analytic Strategy

Before discussing grand analytic strategies, it is probably helpful to start at the other extreme (see Figure 1) and briefly mention lean analytic strategies. In the post, I discuss developing a lean analytic strategy and introduced a lean analytic canvas modeled after the business model canvas for lean start-ups. For start-ups, smaller companies, and smaller units in larger organizations, the focus should be on developing an end to end system with analytics that some value as soon as possible, and iteratively improving it to increase the business value that it delivers. A lean analytic strategy is a good way to do this. You can find more information in Chapter 10 of my book: Developing an AI Strategy: A Primer and a definition of a lean analytic strategy in the Appendix of this post.

Five Questions for a Grand Analytic Strategy

At the other extreme, for larger organizations with multiple divisions and planning that extends out five years or more, it may be appropriate to consider developing a grand strategy for analytics that includes answering questions like the following?

  1. In the long run, how much of the IT, data and analytic ecosystem do we buy vs build? What new technologies should we develop to advance our strategy in analytics?
  2. What are our long terms alliances and partnerships in analytics?
  3. How can we develop, promote and influence standards in analytics to support our strategy in analytics?
  4. How can we best leverage lobbying and influence legislation to support our long term strategy in analytics?
  5. How can we educate our users in particular and the public more generally so that they understand and support how we use data and analytics in our products and services, while balancing privacy with improved functionality?

An Example – Google’s Grand Strategy in Analytics

Alphabet’s revenues for 2019 were over $161 billion and leveraged their analytics and AI to drive revenue across the various subsidiaries of Alphabet and and divisions of Google and leverages advances resulting from investments in fundamental computing and analytics over years. The second paragraph of Alphabet’s fourth quarter 2019 earnings release [Alphabet 2020] reads:

Our investments in deep computer science, including artificial intelligence, ambient computing and cloud computing, provide a strong base for continued growth and new opportunities across Alphabet.

Source: Alphabet 2020.

A recent report from CBInsights writes:

[Google] is also seeking out new streams of revenue in sectors with large addressable markets, namely on the enterprise side with cloud computing and services. Furthermore, it’s looking at industries ripe for disruption, such as transportation, logistics, and healthcare. Unifying Alphabet’s approach across initiatives is its expertise in AI and machine learning, which the company believes will help it become an all-encompassing service for both consumers and enterprises.

Source: CBInsights, Google Strategy Teardown, 2020.

From Lean to Grand Analytic Strategies

To summarize, as Figure 1 shows, there is a spectrum of analytic strategies, as the complexity of the organization grows and as the time frame of interest lengthens. As you move from left to right in the figure, the scope of the strategy becomes broader and broader.

A lean analytic strategy is a shorter term strategy for an analytic start-up or a smaller unit within a large organization, and is concerned with the core of any analytic strategy: how data is collected or generated; how data is transformed using analytics to produce scores or other outputs; how the outputs are used to create something that can be monetized or something that otherwise brings value to the business; and how this whole chain can be protected from a competitive standpoint.

An analytic strategy specifies the long-term decisions an organization makes about how it uses its data to take actions that satisfy its organizational vision and mission; specifically, the selection of analytic opportunities by an organization and the integration of its analytic operations, analytic infrastructure, and analytic models to achieve its mission and vision.

A corporate analytic strategy is an analytic strategy for two or more strategic business units, and it includes a plan for allocating resources across the business units.

A grand analytic strategy is longer term in scope than a typical analytic strategy and is designed for large complex organizations with various subsidiaries, divisions, or strategic business units. A grand analytic strategy is concerned with all spheres and interactions of the organization with analytics, both internal and external, including the broader technological landscape, regulatory and legal landscape, public perceptions, societal trends, etc. around analytics and its applications.

For more information, see: Developing an AI Strategy: A Primer.

References

[Alphabet 2020] Alphabet Announces Fourth Quarter and Fiscal Year 2019 Results, retrieved from https://abc.xyz/investor/static/pdf/2019Q4_alphabet_earnings_release.pdf, on November 1, 2020.

[Biddle 2015] Tami Davis Biddle, Strategy and grand strategy: What students and practitioners need to know. Army War College-Strategic Studies Institute, Carlisle, United States; 2015 Dec 1.

[CBInsights 2020] CBInsights, Google Strategy Teardown, 2020.

[Gaddis 2019] John Lewis Gaddis. On grand strategy. Penguin Books, 2019.

[Silove 2018] Nina Silove, Beyond the Buzzword: The Three Meanings of “Grand Strategy”, Security Studies, 27:1, 27-57, 2018, DOI: 10.1080/09636412.2017.1360073

Notes About Links

There are no affiliate links in this post and I get no revenue from the Amazon links. I do get a royalty from the sale of the book Developing an AI Strategy: A Primer.

Continuous Improvement, Innovation and Disruption in AI

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Figure 1. Some of the differences between continuous improvement and innovation in analytics and AI.

It’s important for managers and leaders in analytics and AI to know the difference between continuous improvement, innovation and disruption in their field and to know how it applies to their projects and to their organization.

Continuous improvement is about encouraging, capturing and using individual knowledge about current processes and how to improve them from the people actively involved. Good examples of continuous improvement applied to complex engineering problems includes: W. Edwards Deming improving the quality of automobile manufacturing in Japan (the Kaizen Process); Bill Smith at Motorola trying to reduce the defects in the manufacturing of computer chips (leading to Six Sigma); and Admiral Hyman G. Rickover improving the safety of nuclear reactors on nuclear submarines [1].

Innovation is about developing new processes, products, methodologies, and technologies. It is usually done by those not directly involved in the day to day to work. Often there is a challenge transitioning innovations from the lab to a product or into a deployed process in production. These days, it’s claimed more than it’s produced. True innovations are usually recognized by experts relatively quickly, but by others over a longer period of time due to clutter in the markets [2, Chapter 3]. Also innovative technology can take a while for companies to deploy for a variety of reasons, including the agility of the company, the lock-in of current vendors, and the sometimes complex motivations and incentives of decision makers [2, Chapter 4].

Disruption occurs when a new technology fundamentally alters the price-benefit structure in an industry or market segment [3]. An example from AI is the use of deep learning software frameworks, such as TensorFlow and PyTorch, along with transfer learning from large pre-trained models, such as ImageNet, Inception, and ResNet, which allows individual scientists using modest computational resources to build deep learning models, without the large computational infrastructure and very large datasets that would be required otherwise.

Some differences

Continuous improvement is about improving something that exists. Innovation is about creating something that doesn’t exist. Innovation can take months or years, while continuous improvement can often be done in days or weeks. See Figure 1 for some more differences.

Best practices in analytics

Best practices for continuous improvements in analytics include:

  • A champion-challenger methodology, where you use a formal methodology to frequently new models and compare them using agreed upon metrics to the current model in production.
  • Weekly model reviews, where all the stakeholders meet each week to review the model’s performance, what additional data can be added to the model, the actions associated with the model, and business value generated from the actions and discuss how these can be improved. Weekly model reviews are part of the Model Deployment Review Framework that I cover in my upcoming book, the Strategy and Practice of Analytics.
  • Model deployment frameworks. A third best practice is to use a model deployment framework so that models can be deployed quickly into production. This might involve PMML or PFA , a DevOps approach to model deployment, or one of the providers of specialized software in this area.

Best practices for supporting the development of innovation in analytics include:

  • Setting up a structure to develop innovative projects. This can be a separate group (a R&D lab, a Future Groups, or an Innovation Center) or supporting regular time (such as Google’s 20% time) devoted to innovation. For example, in our Center we set aside 1-3 days per month for the entire team to work on selected projects that have been proposed.
  • Setting a process to select and support meritorious projects. Innovation takes times and requires support. It cannot be done in a simple brain-storming session.
  • Setting up and fine tuning a process to move useful innovations from the lab into practice. Finally, it is all too common for innovation in large organization never to leave the lab. A number of large organizations have over time developed good processes for transitioning innovation to create new products, services and processes. IBM is quite good at this [3]; a recent example is the investment they put into bringing holomorphic encryption into practice, which took sustained investment over more than a decade. Overtime, this will have an important impact on analytics and AI.

The Power of Simple Process Improvements

It is worth emphasizing the tremendous power of simple process improvements, such as transitioning from letting the data scientists that build models decide when and how to deploy them to weekly model reviews involving all stakeholders, including the business owners. In these weekly meetings, the model is reviewed end-to-end, including the data available, the performance of new models (the challenger in the champion-challenger methodology), and discussing developing potentially new actions associated with the model (see the post on Scores, Actions and Measures (SAM)).

Here is another simple example of the power of continuous improvement that is not related to analytics. For many years, I took notes using emacs in outline mode. Recently, after reading about the Zettelkasten method, I switched to using emacs in org mode and adopted a few of the ideas used in digital Zettelkasten. This small change has made it much easier for me to find technical information that I need. You can find a nice introduction to Zettelkasten in lesswrong.

References

[1] Dave Oliver, Against the Tide: Rickover’s Leadership Principles and the Rise of the Nuclear Navy. Naval Institute Press, 2014.

[2] Robert L. Grossman, The structure of digital computing: from mainframes to big data, Open Data Press, 2012. See Chapter 3, Technical Innovation vs. Market Clutter and Chapter 4, Technology Adoption Cycles. Also available from Amazon.

[3] Clayton M. Christensen, The innovator’s dilemma: when new technologies cause great firms to fail, Harvard Business Review Press, 2013.

[4] National Research Council. 1995. Research Restructuring and Assessment: Can We Apply the Corporate Experience to Government Agencies?. Washington, DC: The National Academies Press. https://doi.org/10.17226/9205. See https://www.nap.edu/read/9205/chapter/6.