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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

Why Great Machine Learning Models are Never Enough: Three Lessons About Data Science from Dr. Foege’s Letter

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Figure 1. William H. Foege, MD, MPH standing next to the bust of Hygeia, the Greek goddess of health on the grounds of a CDC facility in Atlanta. Dr. Foege was the Director of the CDC from 1977 until 1983.
Source: https://phil.cdc.gov/Details.aspx?pid=8149.

Foege’s Letter

In September, William H. Foege, MD, MPH sent a private letter to Robert Redfield, the Director of the CDC reminding him that the “best decisions come from the best science” and the “best results come from the best management.” The letter became public on October 6, 2020 in a USA today article written by Brett Murphy and Letitia Stein and it is well worth reading.

In this post, we look at how these insights apply to building analytic and AI models and applying them to challenging real world problems.

Dr. Foege trained in the Epidemic Intelligence Service (EIS) of the Centers for Disease Control and Prevention (CDC) between 1962 and 1964. The EIS is a fellowship program run by the CDC that trains epidemiologists and is famous the quality of the epidemiologists it trains and for the effectiveness of its investigative and emergency response efforts. In the 1970s, Dr. Foege made critical contributions to the global strategy that led to the eradication of smallpox, culminating in the May 8, 1980 declaration at the 33rd World Health Assembly (WHA) that the world was free of this disease. Smallpox is one of only two diseases that the WHA has designated as eradicated. He served as the Director of the CDC from 1977 to 1983.

Great Science Supports Great Management

To say the least, Dr. Foege is well qualified to understand the role of data and science, management and coalitions, and the leadership necessary to tackle challenging problems, such as the COVID-19 pandemic and how to organize and lead the response to it. In the letter he states:

The first thing … [is] to face the truth. We have learned that the best decisions are based on the best science and the best results are based on the best management. William Foege, MD, MPH, in a letter dated Sept 23, 2020.

From an analytics perspective, I would add two more layers

  • the best results are based on the best management
  • the best decisions are based on the best science
  • the best models are based on the best data
  • the best data are based on the best data sharing (or data collection efforts)

The first two are the domain of management; the second two are the domain of data science. The role of analytic governance is to knit these together through an analytic strategy and to develop a strategic implementation plan to produce the best results. See Figure 2. For background information about analytic strategy and analytic governance, my Primer may be helpful.

Lessons for Tackling Challenging Data Science and Analytic Problems

From the perspective of this blog, I would highlight three lessons that Dr. Foege’s letter suggests:

  1. When you have a challenging problem, face the truth and speak the truth.
  2. Clearly separate the data science / analytics from the management, and make sure you have the best of both. It is critical that there is sufficient analytic governance and strong enough leadership to guarantee that the best science supports the best management.
  3. Good models require good data, and one of the best ways to get good data is by through data sharing collaborations. This is especially important in times of national emergencies.
Figure 2. In analytics and AI, the best data sharing and data collection leads to the best data; the best data leads to the best models; the best models lead to the best decisions; the best decisions lead to the best results. The best results require both the best science and the best management. Analytic governance is the governance structure that knits this all together.

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.

Improving the Analytic Maturity Level of Your Company

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The figure shows the five levels of analytic maturity – Analytic Maturity Level (AML) 1 -5.

Over the past 10 years or so, I developed and used an analytic maturity model that was broadly motivated by the software Capability Maturity Model (CMM) that is used in software development.

You can learn more about the model in this article: A Framework for Evaluating the Analytic Maturity of an Organization, International Journal of Information Management, 2018, which is open access and available here: doi.org/10.1016/j.ijinfomgt.2017.08.005. This article introduces five Analytic Maturity Levels (AML) and discusses the analytic processes required to achieve each level.

Over the past few years I have been working on a book called the Strategy and Practice of Analytics and have been thinking about analytic maturity levels again. Here are the five levels as defined in the book:

AML 1: Build reports. An AML 1 organization can analyze data, build reports summarizing the data, and make use of the reports to further the goals of the organization.

AML 2: Build models. An AML 2 organization can analyze data, build and validate analytic models from the data, and deploy a model.

AML 3: Repeatable analytics. An AML 3 organization follows a repeatable process for building, deploying and updating analytic models. In my experience, a repeatable process usually requires a functioning analytic governance process.

AML 4: Strategy driven repeatable analytics.  An AML 4 has an analytic strategy that aligns with the corporate strategy, uses the analytic strategy for selecting which analytic opportunities to pursue, and follows a repeatable process for building, deploying and updating analytic models.

AML 5: Strategy driven enterprise analytics. An AML 5 organization uses analytics throughout the organization and analytic models in the organization are built with a common infrastructure and process whenever possible, deployed with a common infrastructure and process whenever possible, and the outputs of the analytic models integrated together as required to optimize the goals of the organization as a whole. Analytics across the enterprise are coordinated by an analytic strategy and analytic governance process.

Five ways to improve the analytic maturity level of your organization. Here are five suggestions for improving the analytic maturity level of your organization.

  1. Set up a committee to quantify the analytic maturity of your company. If you cannot measure it, you cannot improve it.
  2. Set up (or improve) the environment for deploying analytic models, using a model interchange format, analytic engines, or similar technology. (Helpful for AM Level 2).
  3. Set up your first analytic governance committee or improve the operational efficiency of your current analytic governance. (Helpful for AM Level 2).
  4. Set up SOPs for building and/or deploying analytic models so that the process is faster, repeatable & replicable. (AM Level 3 Requirement)
  5. Volunteer to lead a process to integrate two different models from two different parts of your company to improve the relevant actions. (Helpful for AM Level 5)

What’s different between AMM-12 and AMM-17? Although the paper describing the analytic maturity model was published in 2017, the work dates back to period 2010-2012. I first gave a talk about the AMM in 2012 at a Predictive Analytic World (PAW) conference that took place on June 25, 2012 in Chicago. Since the AMM didn’t change between 2012 and when it was published in 2017, let’s call this the AMM-2012 model.

In the AMM-2012, AML 1-3 are the same, but AML 4 is Enterprise Analytics and AML 5 is Strategy Driven Analytics. The problem with this approach is that strategy driven analytics can occur at any level, so it is not particularly helpful to reserve it for level 5. From one perspective whether, strategy can be integrated into processes for building reports (AML 1), for building models (AML 2), for building models in a repeatable fashion (AML 4), or for building models across the enterprise in a repeatable fashion (AML 5). With this approach, reports -> models -> repeatable models -> enterprise models would be one axis and the degree of strategy integration would be another axis. This seemed too complicated though for most applications, so for the AML-19 versions of the AMM, AML 4 is simply strategy driven repeatable analytics and AML-5 is strategy driven enterprise analytics.

I speak about analytic maturity models from time to time, most recently at the Predictive Analytics World (PAW) Conference that took place in Chicago on June 29, 2017 and The Data Science Conference Chicago (TDSC) that took place in Chicago on, April 20, 2017. I also occasionally give in house training courses that include material about helping a company increase its analytic maturity level.

You can find more information about Analytic Maturity Models in Chapter 14 of my book The Strategy and Practice of Analytics.

Copyright 2019 Robert L. Grossman