Category Archives: Strategy

How We Teach The Leaders of Tomorrow To Be Curious, Ask Questions and Not Be Afraid To Fail Fast To Learn Fast

I recently enjoyed recording a podcast with Joe DosSantos (Chief Data Officer at Qlik). This was one in a series of #DataBrilliant podcasts by Qlik, which you can also access here (Apple Podcasts) and here (Spotify). I summarize below some of the topics that Joe and I discussed in the podcast. Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more.

The Age of Hype Cycles

The data age has been marked by numerous “hype cycles.” First, we heard how Big Data, Data Science, Machine Learning (ML) and Advanced Analytics would have the honor to be the technologies that would cure cancer, end world hunger and solve the world’s biggest challenges. Then came third-generation Artificial Intelligence (AI), Blockchain and soon Quantum Computing, with each one seeking that honor.

From all this hope and hype, one constant has always been there: a focus on value creation from data. As a scientist, I have always recommended a scientific approach: State your problem first, be curious (ask questions), collect facts to address those questions (acquire data), investigate, analyze, ask more questions, include a sensible serving of skepticism, and (above all else) aim to fail fast in order to learn fast. As I discussed with Joe DosSantos when I spoke with him for the latest episode of Data Brilliant, you don’t need to be a data scientist to follow these principles. These apply to everyone, in all organizations and walks of life, in every sector.

One characteristic of science that is especially true in data science and implicit in ML is the concept of continuous learning and refining our understanding. We build models to test our understanding, but these models are not “one and done.” They are part of a cycle of learning. In ML, the learning cycle is sometimes called backpropagation, where the errors (inaccurate predictions) of our models are fed back into adjusting the model’s input parameters in a way that aims to improve the output accuracy. A more colloquial expression for this is: good judgment comes from experience, and experience comes from bad judgment.

Data Literacy For All

I know that for some, the term data and some of the other terminology I’ve mentioned already can be scary. But they shouldn’t be. We are all surrounded by – and creating – masses of data every single day. As Joe and I talked about, one of the first hurdles in data literacy is getting people to recognize that everything is data. What you see with your eyes? That’s data. What you hear with your ears? Data. The words that come out of your mouth that other people hear? That’s all data. Images, text, documents, audio, video and all the apps on your phone, all the things you search for on the internet? Yet again, that’s data.

Every single day, everyone around the world is using data and the principles I mention above, many without realizing it. So, now we need to bring this value to our businesses.

How To Build A Successful Enterprise Data Strategy

In my chat with Joe, we talked about many data concepts in the context of enterprise digital transformation. As always, but especially during the race toward digital transformation that has been accelerated by the 2020 pandemic, a successful enterprise data strategy that leads to business value creation can benefit from first addressing these six key questions:

(1) What mission objective and outcomes are you aiming to achieve?

(2) What is the business problem, expressed in data terminology? Specifically, is it a detection problem (fraud or emergent behavior), a discovery problem (new customers or new opportunities), a prediction problem (what will happen) or an optimization problem (how to improve outcomes)?

(3) Do you have the talent (key people representing diverse perspectives), tools (data technologies) and techniques (AI and ML knowledge) to make it happen?

(4) What data do you have to fuel the algorithms, the training and the modeling processes?

(5) Is your organizational culture ready for this (for data-informed decisions; an experimentation mindset; continuous learning; fail fast to learn fast; with principled AI and data governance)?

(6) What operational technology environment do you have to deploy the implementation (cloud or on-premise platform)?

Data Leadership

As Joe and I discussed, your ultimate business goal is to build a data-fueled enterprise that delivers business value from data. Therefore, ask questions, be purposeful (goal-oriented and mission-focused), be reasonable in your expectations and remain reasonably skeptical – because as famous statistician, George Box, once said “all models are wrong, but some are useful.”

Now, listen to the full podcast here.

Key Strategies and Senior Executives’ Perspectives on AI Adoption in 2020

Artificial intelligence (AI) has become one of the most significant emerging technologies of the past few years. Some market estimates anticipate that AI will contribute 16 trillion dollars to the global GDP (gross domestic product) by 2030. While there has been accelerating interest in implementing AI as a technology, there has been concurrent growth in interest in implementing successful AI strategies. Some key elements of such strategies that have emerged include explainable AI, trusted AI, AI ethics, operationalizing AI, scaling sustainable AI operations, workforce development (training), and how to speed up all of this development.

The 2020 year of the pandemic has forced organizations to speed up their digital transformation and advanced technology adoption plans, essentially compressing several years of anticipated developments into several months. These accelerated developments cover a wide scope, including: technology-enabled remote work solutions, technology-enhanced health and safety programs, AI-powered implementations of “all of the above”, and sharpened focus and attention on their workforce: future of work in the age of AI, AI-assisted human work and process enhancements, and training initiatives (including data literacy and AI).

In the recent 2020 RELX Emerging Tech Study, results were presented from a survey of over 1000 U.S. senior executives across eight industries: agriculture, banking, exhibitions, government, healthcare, insurance, legal, and science/medical. The survey, carried out by RELX (a global provider of information-based analytics and decision tools for professional and business customers), focused on the state of interest, investment, and implementations in AI tech during the pandemic period. But it was not just a snapshot on the state of AI in 2020. The survey also had forward-looking questions, as well as historical comparisons and trends from results of similar “state of AI in the enterprise” surveys in 2018 and 2019.

Some of the most remarkable findings in the 3-year trending data include these changes from 2018 to 2020:

1) The percentage of senior executives who stated that AI technologies are being utilized in their business dramatically increased from 48% to 81%.

2) The percentage who are concerned about other countries being more advanced than the U.S. in AI technology and implementation increased from 70% to 82%.

3) The percentage who believe that government programs should assist in AI workforce development increased from 45% to 59%.

4) There was a slight increase (though still a minority) in those who believe that the government should leave the promotion of AI technologies to the private sector, growing from 30% to 36%.

5) There is a continued (and growing) strong belief that U.S. companies should invest in the future AI workforce through educational initiatives such as university partnerships, increasing from 92% to 95%.

6) The percentage who are offering training opportunities in AI technologies to their workforce increased significantly from 46% to 75%.

Some of the interesting findings specifically in the 2020 survey results include:

1) Across all sectors, a strong majority (86%) of survey respondents believe that ethical considerations are a strategic priority in the design and implementation of their AI systems, ranging from 77% in government and 80% in the legal sectors, up to 93% in banking and 94% in the insurance sectors, with healthcare and science/medical near the middle of that range.

2) The majority (68%) of respondents stated that they increased their investment in AI technologies, 48% of which invested in new AI technologies. The sectors with the greatest increases in investment were insurance, banking, and agriculture, followed closely by healthcare and science/medical.

3) 82% stated that AI technologies are most likely to be used to increase efficiencies and worker productivities.

4) Only 26% of respondents reported that AI technologies are being used to replace human labor.

Regarding the last point, this minority view regarding AI’s potential negative impact on employment is consistent with a 2019 worldwide survey of 19,000 employers, which found that 87% plan to increase or maintain the size of their staff as a result of automation and AI, and that just 9% of companies across the globe and 4% in the U.S. anticipate cutting jobs. Another report stated, “Rather, than being replaced, humans will be redeployed into higher-order jobs requiring more cognitive skills.”

The broader takeaways and insights drawn from the 2020 RELX Emerging Tech survey are these:

(a) COVID-19 drove increased AI tech investment and adoption.

(b) The use of AI has increased across all sectors that were polled.

(c) Ethical AI is viewed as both a priority and a competitive advantage.

(d) International competition remains a concern for U.S. organizations.

(e) AI workforce training and development is a major component of AI strategy, though AI implementations consistently outpace training initiatives.

Vijay Raghavan, Executive Vice President and Chief Technology Officer for the Risk & Business Analytics division of RELX, has summarized the survey results very well in the following statements:

  • “Businesses’ response to COVID-19 has confirmed the view of US business leaders that artificial intelligence has the power to create smarter, more agile and profitable businesses.”
  • “Businesses face more complex challenges every day and AI technologies have become a mission-critical resource in adapting to, if not overcoming, these types of unforeseen obstacles and staying resilient.”
  • “Companies that do not dedicate the necessary resources to training existing employees on new AI technologies risk leaving growth opportunities on the table and using biased or otherwise flawed systems to make and enforce major decisions.”

Therefore, when we consider AI strategy, a global perspective is no less important than local organization-specific mission objectives. Understanding your competition, the marketplace, and both the current expectations and future needs of your stakeholders (customers, employees, citizens, and shareholders) is vital. Furthermore, non-technology considerations should be incorporated alongside technology implementation and operationalization requirements. Performance metrics and goals associated with AI governance, ethics, talent, and training must be on the same balance sheet as AI tools, techniques, and technologies. How to bring all of these pieces together in a successful AI strategy has become clearer with the results and insights revealed in the 2020 RELX Emerging Tech survey.

You can find and read the full RELX survey report here:

Key Finding in Emerging Tech Survey
Key Findings in RELX 2020 Emerging Tech Survey