47 min

026 - Why Tom Davenport Gives a 2 out of 10 Score To the Data Science and Analytics Industry for Value Creation Experiencing Data with Brian T. O'Neill

    • Business

Tom Davenport has literally written the book on analytics. Actually, several of them, to be precise. Over the course of his career, Tom has established himself as the authority on analytics and how their role in the modern organization has evolved in recent years. Tom is a distinguished professor at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior advisor at Deloitte Analytics. The discussion was timely as Tom had just written an article about a financial services company that had trained its employees on human-centered design so that they could ensure any use of AI would be customer-driven and valuable. We discussed their journey and:

Why on a scale of 1-10, the field of analytics has only gone from a one to about a two in ten years time
Why so few analytics projects actually make it into production
Examples of companies who are using design to turn data into useful applications of AI, decision support and product improvements for customers
Why shadow IT shouldn’t be a bad word
AI moonshot projects vs. MVPs and how they relate
Why journey mapping is incredibly useful and important in analytics and data science work
How human-centered design and ethnography is the tough work that’s required to turn data into decision support
Tom’s new book and his thoughts on the future of data science and analytics

Resources and Links:

Website:  Tomdavenport.com
LinkedIn:  Tom Davenport
Twitter: @tdav
Designingforanalytics.com/seminar
Designingforanalytics.com

Quotes from Today’s Episode
“If you survey organizations and ask them, ‘Does your company have a data-driven culture?’ they almost always say no. Surveys even show a kind of negative movement over recent years in that regard. And it's because nobody really addresses that issue. They only address the technology side.” — Tom

Eventually, I think some fraction of [AI and analytics solutions] get used and are moderately effective, but there is not nearly enough focus on this. A lot of analytics people think their job is to create models, and whether anybody uses it or not is not their responsibility...We don't have enough people who make it their jobs to do that sort of thing. —Tom

I think we need this new specialist, like a data ethnographer, who could sort of understand much more how people interact with data and applications, and how many ways they get screwed up.—Tom

I don't know how you inculcate it or teach it in schools, but I think we all need curiosity about how technology can make us work more effectively. It clearly takes some investment, and time, and effort to do it.— Tom

TD Wealth’s goal was to get [its employees] to experientially understand what data, analytics, technology, and AI are all about, and then to think a lot about how it related to their customers. So they had a lot of time spent with customers, understanding what their needs were to make that match with AI. [...] Most organizations only address the technology and the data sides, so I thought this was very refreshing.—Tom

“So we all want to do stuff with data. But as you know, there are a lot of poor solutions that get provided from technical people back to business stakeholders. Sometimes they fall on deaf ears. They don't get used.” — Brian

“I actually had a consultant

Tom Davenport has literally written the book on analytics. Actually, several of them, to be precise. Over the course of his career, Tom has established himself as the authority on analytics and how their role in the modern organization has evolved in recent years. Tom is a distinguished professor at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior advisor at Deloitte Analytics. The discussion was timely as Tom had just written an article about a financial services company that had trained its employees on human-centered design so that they could ensure any use of AI would be customer-driven and valuable. We discussed their journey and:

Why on a scale of 1-10, the field of analytics has only gone from a one to about a two in ten years time
Why so few analytics projects actually make it into production
Examples of companies who are using design to turn data into useful applications of AI, decision support and product improvements for customers
Why shadow IT shouldn’t be a bad word
AI moonshot projects vs. MVPs and how they relate
Why journey mapping is incredibly useful and important in analytics and data science work
How human-centered design and ethnography is the tough work that’s required to turn data into decision support
Tom’s new book and his thoughts on the future of data science and analytics

Resources and Links:

Website:  Tomdavenport.com
LinkedIn:  Tom Davenport
Twitter: @tdav
Designingforanalytics.com/seminar
Designingforanalytics.com

Quotes from Today’s Episode
“If you survey organizations and ask them, ‘Does your company have a data-driven culture?’ they almost always say no. Surveys even show a kind of negative movement over recent years in that regard. And it's because nobody really addresses that issue. They only address the technology side.” — Tom

Eventually, I think some fraction of [AI and analytics solutions] get used and are moderately effective, but there is not nearly enough focus on this. A lot of analytics people think their job is to create models, and whether anybody uses it or not is not their responsibility...We don't have enough people who make it their jobs to do that sort of thing. —Tom

I think we need this new specialist, like a data ethnographer, who could sort of understand much more how people interact with data and applications, and how many ways they get screwed up.—Tom

I don't know how you inculcate it or teach it in schools, but I think we all need curiosity about how technology can make us work more effectively. It clearly takes some investment, and time, and effort to do it.— Tom

TD Wealth’s goal was to get [its employees] to experientially understand what data, analytics, technology, and AI are all about, and then to think a lot about how it related to their customers. So they had a lot of time spent with customers, understanding what their needs were to make that match with AI. [...] Most organizations only address the technology and the data sides, so I thought this was very refreshing.—Tom

“So we all want to do stuff with data. But as you know, there are a lot of poor solutions that get provided from technical people back to business stakeholders. Sometimes they fall on deaf ears. They don't get used.” — Brian

“I actually had a consultant

47 min

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