Top Data & AI Industry Trends in 2023 with Tom Davenport, Tony Baer, and Sonny Rivera

Key Moments: 

• The biggest surprise in the survey findings around the role of the CDO (03:13)
• Data governance or data enablement? (05:14)
• CDO’s making a play for CEO in 2023 and beyond (07:27)
• Legacy companies and AI adoption (08:30)
• 2023 prediction from Tom (13:50)
• Could we have predicted that data mesh would be such a hot topic in 2022?  (23:45)
• Tony’s prediction for data mesh, data lakes, data warehouse, data fabric? (30:28)
• Tony’s prediction and hopes for CDOs in 2023 (35:00)
• ThoughtSpot's new evangelist, Sonny Rivera (35:55)
• Sonny’s work in the DBT community (41:30)
• Sonny’s prediction for the analytics engineer in 2023 (44:10)
• The need for more modeling education in analytics boot camps (45:02)
• What should your 2023 resolution be as an analytics engineer? (48:06)

Key Takeaways: 

How do you make AI an asset for business? (13:14)
Get on board adopting AI as your ally in business. This trend is ever-increasing, with no signs of slowing down, especially as the technology develops and becomes more useful! As Tom Davenport predicts, in 2023 and beyond only those who stay rooted in the past have anything to fear. 

"Well, I think the prediction for 2023 is that increasing numbers of us will have coworkers who are AI-oriented. And I really think the only people who have anything to worry about from AI are the people who refuse to work with it…And I think it could be said about accountants and marketers with all these generative AI tools, content creators, still require humans in the loop to create a great prompt and to edit this stuff when it comes out to make sure it's okay. But certainly collaborative relationship with a machine in more and more cases." - Tom Davenport

What is the best data architecture? (11:24)
The most important thing to discuss when contemplating data achitectures is having a common language. Tom explains more about his theory for how best to move and store data. 

“ I don't think it's an either-or question, frankly, because data mesh is really about ownership of the data. And Zhamak would basically say that where it's physically housed is kind of incidental to all this. What I do think is really essential to all this is that basically, any organization that is practicing data mesh needs to speak from a common playbook or at least a common language. And so at the base of all that is metadata. And I think we need to have a common language for expressing what this data is and for describing it and describing also practices on how we work with it and govern and secure it. Otherwise, I think we're in a situation of being in the union un general assembly without interpreters. And so therefore, and this is what I basically wrote a year ago, and I still fervently believe this, is that you can implement a data fabric, whatever that is. …But don't even think about implementing data mesh unless you have some sort of fabric at least starting to go in place.” Tony Baer

What are the most important skills for analytics engineers to have this year? (15:41)
As the role of the analytics engineer demands evolves, these individuals will need to have excellent data modeling skills. Sonny Rivera unpacks his thought process behind this prediction  and touches on the need for better analytics education. 

“Cindi, I think that in 2023, analytics engineers that have data modeling skills are gonna command higher salaries than those without. These are really important skills to have and there are a lot of demands being put on this, this analytics engineer's role. If you think about what's happening with analytics engineers, they're a hybrid role. We have engineers that need to build data models for the business to do self-service analytics, maybe leverage semantic layers, add to that python skills. And I think you're going to see more and more importance on data modeling to enable that key feature that we hear everyone talking about, which is self-service analytics.” - Sonny Rivera

Key Quotes: 

You're right, governance is a kind of protective function. [In] some of the interviews people said they were trying to figure out alternatives to data governance, if you will, ways to make it easy to do the right thing without com constantly telling people what to do and what not to do. I think governance is kind of a defensive response, and yet most of the people in this survey and other ones that I've done have said, ‘Hey, we're shifting our focus to offense.’ I don't even like the term. I tell people, ‘Use enablement, might nurse something rather than governance. - Tom Davenport 

And I guess the biggest surprise was the juxtaposition of two things. One, in all of my interviews and in a lot of the survey responses, people said, ‘Well, the big thing for CDOs is to create visible value typically using things like analytics and AI because they're much easier to show visible returns than data management activities.’ But then the thing that didn't really go together with that is when I asked them what activities do you pursue? The number one  activity or job responsibility they thought was data governance, which I find, really difficult to show value. And I'm sort of surprised that it ended up as that high a priority. - Tom Davenport

I don't think it's an either or question, frankly, because data mesh is really about ownership of the data. And Zhamak would basically say that where it's physically housed is kind of incidental to all this. What I do think is really essential to all this is that basically any organization that is practicing data mesh needs to speak from a common playbook or at least a common language. And so at the base of all that is metadata. And I think we need to have a common language for expressing what this data is and for describing it and describing also practices on how we work with it and govern and secure it. Otherwise, I think we're in a situation of being in the union un general assembly without interpreters. And so therefore, and this is what I basically wrote a year ago, and I still fervently believe this, is that you can implement a data fabric, whatever that is. …But don't even think about implementing data mesh unless you have some sort of fabric at least starting to go in place. - Tony Baer 

DBT really is a data transformation tool. It enables data architects, analytics engineers to model their data, create their pipelines, test, and then deploy their product as if it were software like the rest of everyone else is doing in their agile software development. So I think that's the huge breakthrough of DBT. They've taken modeling the data warehouse building and turned that into a software development process that can be done agilely. - Sonny Rivera 

Mentions:

• CDO Agenda 2023: Prioritizing business value creation
• Tom Davenport Books
• Zhamak Deghani on The Data Chief 
• Tony’s reaction to an article article about the best Data Mesh
• 9 data and analytics trends for 2023 - eBook

Bio:

Tom

Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. He earned his Ph.D from Harvard University and has taught at the Harvard Business School, the University of Chicago, the Tuck School of Business, Boston University, and the University of Texas at Austin.

One of HBR’s most frequently published authors, Tom has been at the forefront of the Process Innovation, Knowledge Management, and Analytics and Big Data movements. He pioneered the concept of “competing on analytics” with his 2006 Harvard Business Review article and his 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence. Tom’s book, co-authored with Julia Kirby, Only Humans Need Apply: Winners and Losers in the Age of Smart Machines offers tangible tools for individuals who need to work with cognitive technologies and in his latest book, The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, he provides a guide to using artificial technologies in business. You can read a review of The AI Advantage on MIT Press.

Harvard Business Review editors highlighted his latest ideas in the 10 Must Reads 2017: The Definitive Management Ideas of the Year and again in the 2019 issue. One of his articles is also in the new 10 Must Reads on AI, Analytics, and the New Machine Age. Tom was also named one of ten “Top Voices” by LinkedIn–in 2016 for Education, and in 2018 for Technology. He has also been named one of the top three business/technology analysts in the world, one of the 100 most influential people in the IT industry, and one of the world’s top fifty business school professors by Fortune magazine.

Tony

Tony Baer is the founding principal of dbInsight LLC, which you can find at https://dbinsight.io. The goal of dbInsight is to provide independent counsel to data and analytics technology providers navigating a changing world where cloud deployment, artificial intelligence, and data are upending their client's expectations. Baer is an authority on how cloud-native architecture can transform traditional on-premise data platforms, and how it can be used to break down data and application silos. dbInsight delivers counsel to vendors and publishes independent research that is available through partners.

Baer is capitalizing on a decade of thought leadership at Ovum where he created two new research practices , and was one of Ovum's most visible analysts. He reaches tens of thousands of followers as part of the Big on Data blogging team at ZDNet, along with his Twitter and LinkedIn blogging feeds.

Among his accomplishments, Baer created and defined the emerging "Fast Data" market segment, focusing on real-time analytics on data at scale. He pioneered research calling for emergent approaches to data governance and data quality that are now being embraced by data integration tool vendors.

Sonny

Sonny Rivera is a Senior Analytics Evangelist at ThoughtSpot. Sonny is a modern data stack thought leader and expert. He brings with him over 25 years of experience in delivering data solutions that drive business value and increase the speed to insights. He also has a long history of bridging the gap between business needs and technical capabilities.