Analytic Strategy Partners

Improve your analytic operations and refine your analytic strategy

AI

Developing an AI Strategy: Four Points of View

by

An AI strategy is component strategy supporting an overall corporate or organizational strategy. Source of image: Open Data Press.

Although the name of the blog is Analytic Strategy, we haven’t discussed analytic strategy per se in a while, and it is time to return to the topic. An analytic strategy is a functional component of a corporate or organizational strategy. Other functional component strategies include: a product strategy, a marketing strategy and an IT strategy. In this post, we discuss four points of view for developing a corporate or business strategy in general that might be helpful as you develop an analytic or AI strategy component strategy.

Strategy through systematic planning

What is sometimes called the classical approach to business strategy is focused on profitability and using systematic planning to achieve it.  An exemplar of this approach was the strategy pursued by General Motors under the leadership of Alfred Sloan as President of the company from 1923-1943 and as Chairman of the Board from 1937-1956.  Under his leadership, GM sales grew over 10 percent per year for thirty-three years, and by 1956 GM was double the size of the next largest company [1].   Profits in 1955 were 50% higher than those at each of the next three largest companies [1].  The importance of focusing on profitability achieved by systematic planning was codified during the 1960’s with the writings of Alfred Chandler [2], Alfred Sloan [3] and Igor Ansoff [4]. 

Strategy through competitive positioning

One of the most influential contemporary thinkers about business strategy is Michael Porter, who is the Bishop William Lawrence University Professor at Harvard Business School.  His 1980 book Competitive Strategy [5] book was selected as one of the most influential management books of the 20th century by the Academy of Management [6].

Porter views strategy as a competition between firms in a market and recommends positioning a firm within a sector using a framework called the five forces so that there are structural barriers allowing the firm to sustain above average profits over a long a time period [5].  The five forces are competitive rivalry, the bargaining power of suppliers, the bargaining power of buyers, the threat of substitution, and the threat of new competitors. 

In 1983, Michael Porter co-founded a consulting company called the Monitor Group to help companies implement his view of competitive strategy [6].  It’s important to keep in mind that external forces can overwhelm even the best strategy. The Monitor group was quite successful for over 25 years, but struggled during the 2008 financial crisis, declared bankruptcy in 2012, and was acquired by Deloitte Consulting in 2013 [7].

Strategy through CEO leadership and management excellence

Around the same time, Tom Peters and Robert H. Waterman, Jr. wrote In Search of Excellence [8], which was published in 1982 and sold over 3 million copies in its first four years.  In this book, they told stories about how companies achieved excellence and organized the stories around what they called their 7S Framework, which consisted of strategy, structure, systems, staff, style, skills, and shared values. The focus was less on the economics of competitive positioning and more on the people, processes, culture, and leadership, with the emphasis on the leadership, that could lead to excellence. 

Most people views of complex subjects like strategy are shaped through stories, and good stories about CEOs are quite sticky [9].

Strategy through sustainable technological innovation

Beginning in the late 1990’s and early 2000’s, a different view of strategy began to emerge that used technology, innovation, and large amounts of digital data to steadily improve a product or service.  A particularly powerful approach was to create digital platforms that linked producers and consumers [10].  This data driven and analytics-based approach led to the success of companies like Amazon (founded 1994), Google (founded 1998), and Facebook (founded 2004).  The technology was enabled by software, and in the words that captured this insight, “software began eating the world [11].”

You can think of the recent advances in deep learning and AI as a natural trajectory that began in the 1990’s and has continued as Moore’s law led to more computational power; the growth of the internet, mobile devices and the internet of things (IoT) led to more data; and powerful open source software frameworks began transforming processes [12]. 

The Practice of Management by Peter Drucker

This post started with Alfred Sloan and GM and it’s a good way to end it. Peter Drucker was invited by General Motors to spend the two year period 1943-1945 studying the company, which led to his 1946 book The Concept of the Corporation [13]. This book was one of the early books to focus on the corporation as an entity worth studying and helped popularize viewing strategy through the lens of systematic planning, the first tradition described above. Alfred Sloan’s book My Years at General Motors can be viewed in part as a counterpoint to Drucker’s book [14]. For those interested in the history of strategy, both books are still worth reading today, as well as Drucker’s 1954 book The Practice of Management [15].     

On the other hand, in the current era, digital technology has fundamentally changed how we work, manage, plan, live, and play, and has created new capabilities that in turn has created new businesses and new business models.  For this reason, with the commoditization of data, computing power, network bandwidth, and software [12], it’s a good time to rethink and reinterpret your business strategy.  

Developing an AI or analytic strategy

I wrote a short primer about Analytic Strategy that discusses how to develop an AI or analytic strategy from several different points of view.  It is designed to be self-contained and provides short introductions to both analytics and strategy

The primer covers seven standard strategy tools and frameworks that can be adapted to analytics and AI, including: analytic SWOT, Analytic Ansoff Matrix, Porter’s Five Forces, Blue Ocean Analysis, the Analytic Experience Curve, and PESTEL analysis.

The book also covers specialized tools and frameworks that I have developed to support analytic strategy, including the Analytic Diamond and the Analytic Value Chain.

You can buy Developing an AI Strategy: A Primer online.

References

[1] Hoover, Gary, The Greatest Businessman in American History: Alfred P. Sloan, Jr., American Business History Center, November 4, 2021. Retrieved from https://americanbusinesshistory.org/the-greatest-businessman-in-american-history-alfred-p-sloan-jr.

[2] Chandler, Alfred D. Strategy and structure, MIT Press, 1962

[3] Sloan, Alfred My Years with General Motors, Doubleday & Company, 1963

[4] Ansoff, Igor H, Corporate Strategy, 1965

[5] Porter, Michael, Competitive Strategy, Free Press, New York, 1980.

[6] Bedeian, Arthur G., and Daniel A. Wren. “Most influential management books of the 20th century.” Organizational Dynamics 3, no. 29 (2001): 221-225.

[7] Denning, Steve, What Killed Michael Porter’s Monitor Group? The One Force That Really Matters, Forbes, November 20, 2012.

[8] Peters, Thomas J. and Robert H. Waterman, In Search of Excellence: Lessons from America’s Best-Run Companies, Harper & Row, 1982

[9] Heath, Chip and Dan Heath, Made to Stick: Why Some Ideas Survive and Others Die, Random House, 2007.

[10] Parker, Geoffrey G., Marshall W. Van Alstyne, and Sangeet Paul Choudary. Platform Revolution: How Networked Markets Are Transforming the Economy? and How to Make Them Work for You. WW Norton & Company, 2016.

[11] Marc Andreessen, Why Software Is Eating the World, Wall Street Journal, August 20, 2011.

[12] Grossman, Robert, The structure of digital computing: from mainframes to big data. Open Data Press, 2012.

[13] Drucker, Peter F., Concept of the Corporation, John Day, 1946

[14] Kay, John, The concept of the corporation, 2017, retrieved from https://www.johnkay.com/2017/03/16/the-concept-of-the-corporation/

[15] Drucker, Peter, The Practice of Management, Harper, 1954.

Hidden Dangers from Dark Patterns of Data Collection

by

Hidden dangers.

The term “dark patterns” refer to user interfaces that are deliberately designed to mislead users.

A good definition of the term dark patterns is contained in the article “Shining a light on dark patterns [1]:”

“Dark patterns are user interfaces whose designers knowingly confuse users, make it difficult for users to express their actual preferences, or manipulate users into taking certain actions [1].”

Getting the data you need for modeling is often the hardest part, and some organizations are tempted by using dark patterns to collect. In Chapter 6, of my Primer on developing an AI strategy, I cover several ways that data can be legitimately collected. In this post, I’m going to look at three dark patterns of data collection, in which the user is deliberately mislead. There are quite a few dark patterns of data collection, but here we will focus on just three of them.

Dark Pattern 1: Promiscuous Data Collection

This is one of the most common dark patterns used for collecting data. A good example of this approach is a weather application on your phone that provides the local weather but also collects your location data each day, extracts features from it to characterize your behavior, and then sells your location and behavior data to third parties. There is no standard name for this dark pattern, but I call it promiscuous data collection. Another good example is a game on your phone that collects and sells your location data. The terms of service that allow this are usually, but not always hidden in the click through agreement when you install the app. One of the definitions in the Oxford English Dictionary for promiscuous is: “Of an agent or agency: making no distinctions; undiscriminating,” and this certainly describe mobile applications that collect and sell data in this way.

Dark Pattern 2. Collecting Data Through Browser Fingerprinting

Since cookies can be deleting, advertisers and advertising networks have designed other ways to track your behavior. There are about 300,000,000 million people in the US, which is about 2^28, so about 28 bits of information are needed to identify someone in the US. Browser fingerprinting, also known as online fingerprinting or device fingerprinting, is the technique in which standard online scripts is used to collect information about the system you are using to browse the web, such as the extensions in your browser, the screen resolution and color depth of the system you are using, your time zone, the language you are using, etc. With answers to enough questions like these, a “fingerprint” can be formed that uniquely identifies a device used by an individual. For example, the number of bits of information in a user agent string that a browser provides varies, but 10 bits is a good average [2].

A good place to learn about browser and device fingerprinting is the the privacy education website Am I Unique. When I visited Am I Unique, my operating system, browser (Firefox, Chrome, Safari, etc.), browser version, time zone, and preferred language (all available from the browser when I visited the website) uniquely identified me via a browser fingerprint from 4.2M fingerprints in their database. Am I Unique collects 23 characteristics of your browser and device, although only five of these were needed to uniquely identify me.

I view browser fingerprinting as a dark pattern, because unlike with cookies, there is less you can do to block browser fingerprinting, although this has started to change. You can find a list of tools on the Am I Unique website that can provide some protection from browser fingerprinting.

Dark Pattern 3. Dark Digital Twins

Digital twins are “digital replications of living as well as nonliving entities that enable data to be seamlessly transmitted between the physical and virtual worlds [3]”. AI and deep learning are enabling the development of more functional and more powerful digital twins.

By a dark digital twin, I mean an AI application that either asks a series of questions or observes your behavior in one context with your consent, and then through any of several techniques, builds a profile of you and uses this profile in another context without your consent. As a simple hypothetical example, assume you are interacting with an AI application that is giving you wine recommendations, but the information used is then used in a dark digital twin to target you with vacation packages. Although this is pretty innocent example, as digital twin profiling technology improves, the use of dark digital twins is likely to become more and more disconcerting.

These days, it is more common for a machine learning or AI model built with user consent for one purpose is reused for another purpose without adequate user consent, but it is only a matter of time before the models became sophisticated enough to start acting as digital twins.

References

[1] Jamie Luguri and Lior Jacob Strahilevitz. “Shining a light on dark patterns.” Journal of Legal Analysis 13, no. 1 (2021) pages 43-109.

[2] Peter Eckersley, A Primer on Information Theory and Privacy, 2010, Electronic Freedom Foundation, retrieved from https://www.eff.org/deeplinks/2010/01/primer-information-theory-and-privacy on July 7, 2021.

[3] Abdulmotaleb El Saddik, Digital twins: The convergence of multimedia technologies, IEEE multimedia 25, no. 2 (2018) pages 87-92.

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

by

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

Machine Learning vs AI Business Models – What’s New with the Economics of AI?

by

The Economics of AI

Ajay Agrawal, Joshua Gans, Avi Goldfarb and Catherine Tucker have organized a series of important and influential conferences on the economics of AI. The proceedings of the 2019 conference are open access and full of interesting persepctives.

Three of the conference organizers (Ajay Agrawal, Joshua Gans, and Avi Goldfarb) are all from the University of Toronto Rotman School of Management and published a 2018 book for general readers called Prediction Machines [1]. In this book, they view AI systems as prediction machines that dramatically lower the cost of predictions. In principle, as the cost of prediction falls, organizations can make more and better predictions, and hopefully better decisions. They are fundamentally focussed on the lower cost of predictions and how that is changing business.

There is absolutely no question that the price of predictions has been falling dramatically. I tend to look at this through a longer and broader perspective of commoditization. 1) The commoditization of compute has been driven by Moore’s Law over the past several decades; the commoditization of software has been driven by open source software, and, more recently, by cloud computing and Software as a Service (SaaS); and, 3) the commoditization of data has been driven by the exponential growth of new sources of data from the internet, from smart phones, and from IOT/OT devices. For over forty years, analytics has been at the intersection of these three trends and this confluence has been changing business over the same period. We just keeping calling it something different: data intensive statistics in the 1980s, data mining in the 1990s, predictive analytics in the 2000s, and AI in this decade. See [2].

From an economics perspective, the cost of predictions drops and continues to drop.

The Economic Challenges of AI

On the other hand, if you are launching an AI start-up or starting an AI initiative, it is important to look at some of the barriers in building a successful AI business. Martin Casado, a partner from Andreesen Horowtiz, and his colleagues have written a series of articles that are well worth reading on this topic, and the broader topic of the economics of AI, including:

  • Martin Casado and Matt Bornstein, The New Business of AI (and How It’s Different From Traditional Software), February 16, 2020.
  • Martin Casado and Matt Bornstein, Taming the Tail: Adventures in Improving AI Economics, August 12, 2020.
  • Martin Casado and Peter Lauten, The Empty Promise of Data Moats, May 9, 2019

In these articles, there are insightful comparisons of AI start-ups compared to software as a service (SaaS) start-ups. Perhaps the most useful take home message for those not working in the industry is the following formula from [3]

Equation 1. An important Equation from the article by Martin Casado about the new business of AI [1].

The importance of this formula from Martin Casado and Matt Bornstein’s article “The New Business of AI” cannot be over emphasized. Although many AI start-ups, data science start-ups, and analytic start-ups may initially view themselves as a software start-up, they generally also end up involved with curating data and building models over the data; that is, they find themselves in a services business also.

The Four Elements of a Successful AI Business

Whether in the era of statistical modeling (80’s), data mining (90’s), predictive modeling (2000’s), or AI (2010’s), there have always been four critical elements.

Element 1. You need the data and the IT infrastructure to manage it.

Although data is being commoditized, getting the data you need to solve a problem that can be monetized is not always easy. Specialized IT infrastructure, may, or may not, be needed, depending upon the volume and velocity of the data.

Element 2. You need the expertise to clean the data and build the models.

This is often labor intensive and often involves exploratory data analysis, careful cleaning of the data, and experimentation to improve the model.

In addition, some models may take substantial amounts of data and substantial amounts of computing power, raising the cost of the model and its maintenance.

Element 3. You need software to build models.

Depending upon your solution, you may, or you may not, need to develop your own software.

We can summarize these three critical elements with a slight addition to Equation 1 to get Equation 2.

Equation 2. Although data is commoditized, getting the data you need to solve the business problem of interest is often still a problem.

In practice, both Equations 1 and 2 miss a critical element that is at the core of most successful analytic companies.

Element 4. You need a business model that generates enough business value to justify the costs required to collect the data and build the model.

The point to keep in mind here is that services required to curate data and build models is often labor intensive and therefore the analytic model must generate enough business value to justify the costs to collect the data, curate the data, understand the data, build the model, improve the model, and manage the edge cases. This is not easy.

This brings us to Equation 3:

Equation 3. What’s needed for an AI business.

Although the economics of AI is dramatically lowering the cost of predictions, finding a business model to provide the foundation for a competitive and sustainable AI business still requires some effort. In addition, as pointed out in the articles by Martin Casado, the resulting AI business generally does not have the margins and scalability of a software company. These two basic facts have been the case for the past forty years.

In my book Developing an Analytic Strategy: a Primer [4], I take a slightly simpler perspective. These days with cloud computing and Software as a Service, the software is usually not the critical path. If you have the data, if you have the expertise, and if you have the business model, you can generally succeed. I call this the DEB Framework.

  • Data. Is the data (“D”) required for your analytic strategy available? If not, do you have a realistic plan for getting it?
  • Expertise. Is the expertise (“E”) required for processing and transforming your data available? Does this expertise include people who have developed, deployed, operated, and maintained similar models?
  • Business model. Have you identified a business model (“B”) for monetizing the data or extracting the required value that is sustainable, provides compelling competitive advantages, and can be protected from current competitors and future new entrants into the market?

References

[1] Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. Prediction machines: the simple economics of artificial intelligence. Harvard Business Press, 2018.

[2] Robert L. Grossman, The Structure of Digital Computing, Open Data Press, 2012.

[3] Martin Casado and Matt Bornstein, The New Business of AI (and How It’s Different From Traditional Software), February 16, 2020.

[4] Robert L. Grossman, Developing an Analytic Strategy: A Primer, 2020.

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 my books.