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Developing an AI Strategy: Four Points of View

May 12, 2022 by Robert Grossman

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.

Filed Under: Uncategorized Tagged With: AI, AI strategy, Alfred Sloan, analytic strategy, competitive positioning, concept of the corporation, management excellence, Peter Drucker, Practice of Management, sustainable technology innovation, technology innovation

Ten Books to Motivate and Jump-Start Your AI Strategy

April 11, 2022 by Robert Grossman

Sun Tzu, a general and strategist, who was the author of the Art of War over 2500 years ago. Source Wikipedia

This post contains a list of ten books that I found helpful as I wrote my book: Developing an Analytic Strategy: A Primer.

I thought about different ways to order the list, but, in the end, I simply ordered the books alphabetically by the last name of the author. I don’t find this ordering very satisfactory. I thought seriously of ordering the books by the number of pages, from the shortest (The Art of War) to the longest (Exploring corporate strategy: text & cases), since I have a particular fondness for short books that can concisely summarize complex subjects.

Focus Your Business: Strategic Planning in Emerging Companies

Steven C. Brandt, Focus Your Business: Strategic Planning in Emerging Companies, 1997, Archipelago Publishing, Friday Harbor, WA.

Steven Brandt is an entrepreneur who also taught at Stanford’s business school in the 1970’s, 1980’s and 1990’s.  The book is concise (169 pages) and practical.  Its focus is on smaller and emerging companies.  The book is full of diagrams and tables, which I particularly liked.  There is an emphasis on practical planning to support the identification and implementation of a strategy.

Competing on Analytics

Thomas H. Davenport and Jeanne G. Harris, Competing on Analytics: The New Science of Winning, Harvard Business School Press, 2007.

In 2006, Thomas Davenport, a professor at Babson College and a Fellow at Accenture, wrote an article for the Harvard Business Review called Competing on Analytics that brought the importance of analytics to the attention of many corporate level executives for the first time. The article was the most downloaded article that year. The following year he wrote Competing on Analytic with Jeanne Harris.  The book has sold more than 100,000 copies and has been translated into 13 languages.

25 Need to Know Strategy Tools

Vaughan Evans, 25 Need to Know Strategy Tools, Pearson, 2014

One of my favorites for getting a good practical introduction for writing a strategic plan or for refreshing your memory about some strategy tools and frameworks that you have learned in the past.  It is probably more useful if you have had a course in strategy or have a bit of exposure to strategic planning, but even if this is not the case, you will probably get something out of the book. 

The book is relatively concise (about 200 pages).  Knowing 5-8 of the tools in detail and knowing the names of the others would put you in good place.

Evans has also written a related book with even more tools and frameworks that I also recommend:

Evans, Vaughn, Key Strategy Tools: The 80+ tools for every manager to build a winning strategy, Pearson, UK, 2013

Strategy: A History

Lawrence Freedman, Strategy: A history, Oxford University Press, 2015.

Freedman has written a comprehensive book (over 600 pages) about thinkers who have written about strategy, their impact, and Freedman’s assessment of their work.  Unlike most of the other books in this list, Freedman looks at strategy quite broadly and discusses military strategy (Sun Tzu, Clausewitz, Jomini and others), business strategy, strategies of non-violence (Martin Luther King and Mahatma Ghandi) and strategies of class warfare (Karl Marx).  The book is very readable and does not need to be read from end to end, but it is still quite comprehensible if individual chapters or even sections are read. 

Competitive Strategy: Techniques for Analyzing Industries and Competitors

Michael E. Porter, Competitive Strategy: Techniques for Analyzing Industries and Competitors, The Free Press, 1980.

Michael Porter is the Bishop William Lawrence University Professor at Harvard Business School.  His book Competitive Strategy was selected as one of the most influential management books of the 20th century by the Academy of Management.

Porter views strategy as a competition between firms and advocates understanding competition through what he calls the five forces: competitive rivalry, the bargaining power of suppliers, the bargaining power of buyers, the threat of substitution, and the threat of new competitors, and using this understanding to position a firm within a sector to provide structural barriers so that it could sustain above average profits over a long term.

Good Strategy, Bad Strategy

Richard P. Rumelt, Good Strategy, Bad Strategy: The Difference and Why it Matters, 2011, Crown Business, New York.

Richard Rumelt is a business school professor and consultant who has written a thoughtful and practical book about strategy.  The McKinsey Quarterly called him as a “strategist’s strategist.”  He emphasizes the importunate of any strategy having a kernel, which contains the following  three elements:

  • A diagnosis that defines or explains the nature of the challenge. A good diagnosis simplifies the often overwhelming complexity of reality by identifying certain aspects of the situation as critical.
  • A guiding policy for dealing with the challenge. This is an overall approach chosen to cope with or overcome the obstacles identified in the diagnosis.
  • A set of coherent actions that are designed to carry out the guiding policy. These are steps that are coordinated with one another to work together in accomplishing the guiding policy.

The Boston Consulting Group on Strategy

Carl W. Stern and Michael S. Deimler, editors, The Boston Consulting Group on Strategy: Classic concepts and new perspectives, John Wiley & Sons, 2006.

This book contains reprints of several dozen articles written by consultants and principals from the Boston Consulting Group covering the classic tools they introduced and / or popularized, including the cash cows, experience curves, time-based competition, and many others.  Still worth reading.

The Art of War

Sun Tzu and Lionel Giles (translator), The Art of War, 1910, retrieved from http://classics.mit.edu/Tzu/artwar.html.

This is the classic test on military strategy written about 2500 years that has spawned many books on business strategy.  It’s always helpful to think about some of the similarities and differences between military and business strategy.  The book exploded in popularity in 2001 due to an episode on the HBO series The Sopranos. Tony Soprano’s therapist recommended the book to him, and Tony Soprano in a clip that you can see on YouTube told the therapist that he found the book much more helpful than the Prince by Machiavelli, which some of his colleagues and adversaries had read.  Your mileage may vary.

Exploring corporate strategy: text & cases.

Gerry Johnson, Richard Whittington, Kevan Scholes, Duncan Angwin and Patrick Regner, Exploring corporate strategy: text & cases. Pearson education. 11th edition, 2017.

One of the standard and most widely used textbooks on strategy.  It’s been used by over 750,000 students.  It’s through – the main text is over 500 pages and the case studies cover more than 200 additional pages.  An excellent text and an excellent reference.  You can get most of the benefit from one of the older editions, which can be much less expensive than the current edition.

What is strategy – and does it matter?

Richard Whittington. What is strategy – and does it matter?. Cengage Learning EMEA, 2001.

A thoughtful book about strategy, providing an analysis of the various academic traditions in strategy.   For those wanting to master strategy, the insights and historical analysis are well worth it.  The book is concise at 165 pages.

About the image. The image of Sun Tzu is in the public domain and is available from Wikipedia.

Filed Under: Uncategorized Tagged With: AI strategy, analytic strategy, Boston Consulting Group, Business strategy, competitive strategy, Corporate Strategy, strategy frameworks, strategy tools, Sun Tzu, The Art of War

A Rubric for Evaluating New Projects that Produce Data

March 15, 2022 by Robert Grossman

You can see the red rubrics in the page above from a manuscript that dates from the 1500’s. Source: Wikipedia, https://en.wikipedia.org/wiki/File:Gradual_of_King_John_Albert.jpg.

I find it helpful to have simple rubrics when evaluating analytic projects as an advisor. I have written previously about George Heilmeier and the rubric he used when he evaluated projects as Director of DARPA from 1975-1977. The seven questions he asked when evaluating projects are now back on the DARPA website [1].

Eight Questions

Here are 8 questions that I find helpful to ask when evaluating a project that works with data and produces machine learning or analytic models:

Question 1. What datasets (or data products) will this project produce? How will they be made available so that they are findable, accessible, interoperable and reusable (FAIR)?

The most basic question to ask is what datasets will be produced and how they will be made available so that others can find them, access them and use them [2].


Question 2. How is the use and value created from the datasets being measured and tracked?

The second most basic question is how is the use and the value produced by the use of the data being measured and tracked? This is a surprisingly difficult question and often one simply counts the times the data is used by others. Even better is to track what is produced by others using the data, such as any reports, publications, products, or services produced using it.


Question 3. What is the expected impact? If you are successful, what difference will it make?

This question is one of the questions that George Heilmeier always asked. It is related to Question 2, but goes deeper. It’s not, for example, the data is used by two other processes in our organization, but, rather, it is only using this data that our organization can do X, and X has impact Y for our organization.


Question 4. Will the datasets be in a format and available via an API that supports range queries (“sliceable data”) or must the entire dataset be transferred?

Question 4 is more technical, but important. Just because data is available does not mean it is easy to use by other applications. Question 4 is about whether the data produced by your project will be available via an API that allows other applications to get the data they need without accessing, hosting and parsing the entire dataset.


Question 5. What machine learning (ML) or AI-ready datasets is the project producing and making available?

Data is often produced in transactions, events or encounters, (we’ll use the term “events” for simplicity), but usually machine learning or AI models are based upon the entities that produce the events. For each entity, there is a feature vector and the input to a machine learning model is often a matrix, data frame or similar structure with rows corresponding to the entities and columns corresponding to the features. These days, data structured this way is beginning to be called AI-ready. To clean and curate the data and produce features from the events can be quite labor intensive. It usually also requires quite a bit of domain knowledge.


Question 6. How is the curation, provenance, and processing of the data being managed and made available? What steps are being taken to make sure that the curation and analysis of the data is reproducible?

If the data and data products are being used by others, then others can usually benefit by reusing the processes to curate and process the data, since sometimes they need to make minor (or major) changes to the processes.


Question 7. What is the sustainability plan for maintaining the datasets and continuing to make them available?

For business, this is a function of the return generated by the data produced by the project, including downstream projects that reuse and repurpose the data. For not-for-profits, this is a more complex function of the value generated by the data, how the value generated by the data impacts the mission, and a comparison involving how much mission value and impact could be generated by alternate uses of the funds required for the project.


Question 8. What steps are you taking, what standards are you following, and what software services are you using so that the data you are producing and the software applications and services that you are developing can be used by other projects?

This question is related to Question 2, 4, 5 and 6, but is framed a bit differently. Almost always extra work is required to take data from one project and make it more easily used by other projects. This question begins to address the trade-off about whether additional work should by undertaken by the project so that other projects can more readily leverage the data it produces. This is a trade-off at the organizational level or at the mission level.

Two Contexts

There are different contexts in which to ask and to evaluate these questions. The first is within the context of your organization in order to generate a competitive advantage for your organization as part of an analytic strategy.

Another context is is as part of a not for profit mission in which you are using data sharing to advance your mission. For example, to accelerate research by sharing data. From this perspective, these questions can help answer how the project can advance what is being called open data, and, more broadly, open science [3].

On the Origin of Rubric

Today, in education, a rubric is a scoring guide used to evaluate student performance or a student project, assignment, or exam. The term rubric has becoming widely used and many of us use, for example, rubrics for evaluating job candidates in interviews.

Rubric derives from the Latin ruber or rubeus. The Latin word rubrica was the name for red earth, red ochre or red chalk. As another example, the English word ruby, a red gemstone, derives from the Latin rubinus lapis for red stone.

In medieval manuscripts, the first letter of paragraphs and section headings was sometimes in red ink for emphasis [4]. Adding the red section headings and related markings was called rubrication. Later, with the advent of printing, in printed liturgical texts, black ink was used for what should be recited and red ink was used for what should be done. The instructions in red ink were called rubrics.

Rubric has also been used in a number of different figurative senses over time. The Oxford English Dictionary dates its use to the 1400’s and included as one of its definitions: an established custom; a set of rules, an injunction; a general prescription [5]. For example, “an important rubric of open data is to make the data FAIR.”

References

[1] DARPA, The Heilmeier Catechism, retrieved from https://www.darpa.mil/work-with-us/heilmeier-catechism on March 1, 2022.

[2] Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg et al. “The FAIR Guiding Principles for scientific data management and stewardship.” Scientific Data 3, no. 1 (2016): 1-9.

[3] Fecher, Benedikt, and Sascha Friesike. “Open science: one term, five schools of thought.” In Opening science, pp. 17-47. Springer, 2014.

[4] Wikipedia, rubric, retrieved from https://en.wikipedia.org/wiki/Rubric on March 1, 2022.

[5] Oxford English Dictionary, rubric, retrieved from https://www.oed.com/view/Entry/168394 on March 1, 2022.

Filed Under: Uncategorized Tagged With: AI ready, analytic strategy, analytic value, FAIR, open data, rubric, sustainability

How Does No-Code Impact Your Analytic Strategy?

February 14, 2022 by Robert Grossman

Figure 1. Developing an AI/ML application requires

Introduction

An important long-term trend of machine learning and AI is the growing sophistication and power of software frameworks for creating machine learning and AI models and applications. They are a variety of software frameworks that have been developed and refined over the years that have been developed for different types of users and for different purposes. This trend applies to all areas of software development, from web applications to mobile applications to data center applications.

Of course, there is still a need for software developers, data scientists and data engineers, but the appropriate software framework opens up the development of models and applications to a broader community. Below we will look at several different types of software frameworks, including visual programming frameworks, low code data science applications, and no code data machine learning applications.

Low Code Data Science

Low code data science has been around for over 20 years and enables a wider variety of data scientists to build and deploy data science applications. Low code data science uses visual programming so that you can copy, paste and drag icons for extracting data from data sources, cleaning data, building features, training models, validating models, and deploying models into certain applications.

Low code data science still requires understanding the basics of data science, including the roles of cleaning, extracting, transforming data, building features, training models, validating models, etc. Low code data science opens up these techniques to data scientists who understand these tasks but may not have the programming expertise to use programming languages, such as Python or R effectively.

Low code data science views data science as a workflow or pipeline and allows you to drag, drop and connect visual elements, each representing a particular task. You can also introduce your own custom tasks by writing your code, which can then be used like the predefined tasks.

Visual programming itself, an important element of low code data science, has been around in its current incarnation since the early 1990’s. The Wikipedia period article on visual programming has links to dozens of visual programming tools, including a category of tools for data warehousing / business intelligence. It is important to note that many data science visual programming tools are listed in the that the list is not complete and important tools such KNIME are listed in the systems/simulation list, not the data warehousing / business intelligence list.

No Code Data Science

There is a spectrum, and, as just mentioned, no-code data science is still designed for data scientists. No code data science (or no code machine learning / AI) is designed for a broader range of users. With no code machine learning, you just need some data. In general, no code AI/ML is designed so that the user just needs to select the appropriate data and, perhaps, the targeted application where the model should be deployed [1].

No code AI/ML makes use of default parameters, while low code AI/ML allow the data scientist to tune the default parameters of task (represented by a visual icon) if desired.

Using No Code AI/ML to Develop a “First Look”

An important role of no code AI/ML applications is to try out new ideas quickly with very little labor. You might call this a “first look” at a new concept or idea.  You can also think of this as the stage before a proof of concept (POC) or a pre-proof of concept (Pre-POC). If the idea demonstrated with no code AI/ML looks interesting, with additional work it can be turned into proof of concept, follow by followed by a prototype, pilot and MVP.  

Crossing the Data and Engineering Chasms

To move from a first look or Pre-POC to a MVP, two barriers must be crossed. See Figure 1.

The first barrier is the data chasm (see Chapter 6 of my Primer [2]). This is the challenge of getting all the data that you need and labeling it appropriately so that you can build not just a ML/AI model, but an AI/ML model that generates the required business or operational value. This is usually a significant effort, especially given the fact that datasets large enough to be used for deep learning and AI usually have significant biases, gaps, and other defects that must be removed before a viable MVP can be developed.

The second barrier is the engineering chasm, which is described in my February 2021 post [3]. This is the challenge of moving from a ML/AI model (that has crossed the data chasm) to a ML/AI system that satisfies user requirements and generate sufficient revenue or value to the company or organization to operate and sustain the system.

The challenge is developing effective methods for crossing the data and engineering chasms and moving from ideas to POCs, prototypes, pilots, and MVPs. If you have an effective framework for moving from ML/AI POCs to ML/AI MVPs, adding no-code to support Pre-POCs is easy.

What about your Competitors?

From an analytic strategy point of view and a competitive landscape perspective, it is important to understand what your competitors are doing.  In most cases, you should assume that your adversaries and competitors are using no-code and low-code approaches to explore new applications.  By including no-code ML/AI as one of the technologies you use, you provide the ability to take a quick look at new ideas and concepts to a wider fraction of your organization.  This can provide some important advantages.

Conclusion

Here is quick summary:

  1. There is an important role for low code data science and no code machine learning and AI applications.
  2. They can be used to try out new ideas quickly and go from a concept to a simple model derived from data. Think of it as a “first look” or “Pre-POC” of a new idea.
  3. You should assume that your competitors and adversaries may be using no-code and low-code methods, which has consequences for your analytic strategy.
  4. It is a still a long journey to a full model and even a longer journey to a complete application that provides value and has an acceptable number of edge cases and failure modes. To obtain a competitive advantage, you need to: 1) develop a culture where those with ideas have access to no-code environments; and 2) develop efficient methods to move from no code machine learning / AI to POCs, prototypes, pilots and MVP. The first is relatively easy; the second is relatively hard.

References

[1] Marcus Woo. “The Rise of No/Low Code Software Development—No Experience Needed?.” Engineering (Beijing, China) 6, no. 9 (2020): 960. Available from PubMed Central PMC7361109.

[2] Robert L. Grossman. Developing an Analytic Strategy: A Primer, Open Data Press, 2020.

[3] Robert L. Grossman. How to Navigate the Challenging Journey from an AI Algorithm to an AI Product, February 15, 2021, Analytic Strategy blog.

Filed Under: Uncategorized Tagged With: analytic strategy, Data Chasm, engineering chasm, low code, low-code AI, no code, no-code AI, Pre-POC

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  • Developing an AI Strategy: Four Points of View
  • Ten Books to Motivate and Jump-Start Your AI Strategy
  • A Rubric for Evaluating New Projects that Produce Data
  • How Does No-Code Impact Your Analytic Strategy?
  • The Different Varieties of Advisors & the Difference it Makes

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