The real value of data requires a holistic view of the end-to-end data pipeline

The O’Reilly Data Show Podcast: Ashok Srivastava on the emergence of machine learning and AI for enterprise applications.

By Ben Lorica
June 7, 2018
Moebius loop Moebius loop (source:

The real value of data requires a holistic view of the end-to-end data pipeline
Data Show Podcast

00:00 / 00:31:05

In this episode of the Data Show, I spoke with Ashok Srivastava, senior vice president and chief data officer at Intuit. He has a strong science and engineering background, combined with years of applying machine learning and data science in industry. Prior to joining Intuit, he led the teams responsible for data and artificial intelligence products at Verizon. I wanted his perspective on a range of issues, including the role of the chief data officer, ethics in machine learning, and the emergence of AI technologies for enterprise products and applications.

Here are some highlights from our conversation:

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Chief data officer

A chief data officer, in my opinion, is a person who thinks about the end-to-end process of obtaining data, data governance, and transforming that data for a useful purpose. His or her purview is relatively large. I view my purview at Intuit to be exactly that, thinking about the entire data pipeline, proper stewardship, proper governance principles, and proper application of data. I think that as the public learns more about the opportunities that can come from data, there’s a lot of excitement about the potential value that can be unlocked from it from the consumer standpoint, and also many businesses and scientific organizations are excited about the same thing. I think the CDO plays a role as a catalyst in making those things happen with the right principles applied.

I would say if you look back into history a little bit, you’ll find the need for the chief data officer started to come into play when people saw a huge amount of data coming in at high speeds with high variety and variability—but then also the opportunity to marry that data with real algorithms that can have a transformational property to them. While it’s true that CIOs, CTOs, and people who are in lines of business can and should think about this, it’s a complex enough process that I think it merits having a person and an organization think about that end-to-end pipeline.


We’re actually right now in the process of launching a unified training program in data science that includes ethics as well as many other technical topics. I should say that I joined Intuit only about six months ago. They already had training programs happening worldwide in the area of data science and acquainting people with the principles necessary to use data properly as well as the technical aspects of doing it.

I really feel ethics is a critical area for those of us who work in the field to think about and to be advocates of proper use of data, proper use of privacy information and security, in order to make sure the data that we’re stewards of is used in the best possible way for the end consumer.

Describing AI

You can think about two overlapping circles. One circle is really an AI circle. The other is a machine learning circle. Many people think that that intersection is the totality of it, but in fact, it isn’t.

… I’m finding that AI needs to be bounded a little bit. I often say that it’s a reasonable technology with unreasonable expectations associated with it. I really feel this way, that people for whatever reason have decided that deep learning is going to solve many problems. And there’s a lot of evidence to support that, but frankly, there’s a lot of evidence also to support the fact that much more work has to be done before these things become “general purpose AI solutions.” That’s where a lot of exciting innovation is going to happen in the coming years.

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Post topics: AI & ML, Data, O'Reilly Data Show Podcast
Post tags: Podcast

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