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volume and velocity of data

The Heart of Every Business: Data Analytics – Past, Present, and Future

John Lewis
John Lewis, Executive Partner at Madison Dearborn Partners and HPA member

John Lewis is an information services and data analytics leader who has served as Global President of Nielsen and CEO of Knowledge Networks. Now, as an Executive Partner at Madison Dearborn Partners, John works on finding the best investments and opportunities in the space, leveraging his experience to understand the current landscape. At Hyde Park Angels, John lends his expertise to evaluating potential companies and providing operational guidance to portfolio companies. We spoke with him about what makes the data analytics space a unique area for innovation and growth.

How were you first exposed to the data analytics space?

John: I was running the consumer products division at Nutrasweet in the mid-90s, and I was a big user of Nielsen data. That notion of using data to make better decisions was appealing to me; I was a fact-based leader. I had the opportunity to go and become the CMO at Nielsen North America. I thought that all businesses were going to be information business and fundamentally drive all business decisions at some point, so I thought it would be very good for me to learn the information side of it. I got that right: information is at the heart of every business model in any industry that we can talk about.

How would you define data analytics?

I would say the foundation of data analytics is being able to possess, own, use, and organize data in a way that can drive decision-making. There are two branches of data analytics. One branch is made of companies that use data and analytics to drive their business model, and the other is made up of businesses like Nielsen and Uptake which are purely about using data analytics to help other companies. The best analytics companies in the world are really Google, Facebook and Amazon. They don’t call themselves data analytics companies but that’s what they are. They are by far the best at using large datasets to drive their business models.

In fact, if you asked who is making the biggest investment in AI in the world, it’s those three companies. Amazon Web Services (AWS) is being driven by AI and Amazon’s retail business is also being driven by AI. If you have an AI expert in the valley, it’s really hard to keep them because Google and Facebook have an insatiable need for those kinds of people. They are investing at a level that is unrecognized anywhere else in the world.

How has the industry been changing from when you first started at Nielsen to now? Have you noticed differences in how the platforms are built or applied?

John: It’s changed a great deal. If I tried to pick a few things that have changed, I would say that the ubiquity of data and analytics has caused the number of industries that are really depending on data to change completely. It used to just be a few industries cared in the early 2000s, but now it’s every industry.  The second big shift is the change in the volume and velocity of the data, which really brought forward the term Big Data. What we thought of big data ten years ago is laughable compared to today.

How is this evolution and growth of data analytics tied to AI and machine learning?

John: Really AI and machine learning are an answer to the volume and velocity of data. There is no human who can process that data any longer, so now people are trying to create the technology and the rules that help machine learning improve over time. Those technologies are a direct result of the explosion of the volume and velocity of data. IOT is basically creating new data from every individual thing and that is really adding to the volume and velocity of data. Mobile, too, is creating a lot of data. Most of these new technologies naturally create more and more data, and the technology that handles that is machine learning.

Would you say the main challenge right now in data analytics is figuring out how to manage these enormous volumes of data sets?

John: It’s two-fold. First, it’s how to manage, ingest and integrate data sets, but in the end that is only the first step. The real step is how to use that integrated data to drive a different product, offering, or relationship. It’s all about using it to do something new or better.

Silicon Valley-based companies have really embraced Big Data and has spawned countless companies and jobs in this space. Are there other geographic regions where you think the next explosion in data analytics will take place?

John: It will be ubiquitous in some respects, but there will also be dominant hubs for talent and companies. I would say Chicago is already a hub, but the question is: Can Chicago be recognized as a leading hub? Can we position the city as an advantageplace to start or build a big data company?? We have the universities, we have the large foundational companies, and we have the startup ecosystem to become that hub. But right now, I think it’s a competition to become one of those leaders.

To win that competition, what do you see as the defining advantage or asset needed?

John: I think it’s the talent and the technology, but you still need the business ecosystem to use it. In other words, you also have to have the universities, companies, and startup ecosystem to make use of the technology and the talent. But, in the end, if you don’t have the tech or the talent, you’re not going to be a hub. If they’re not around, you won’t see the formation of new companies and the hub won’t grow.

What are some of the companies doing data analytics you think will help establish Chicago as a hub?

John: There is a really nice ecosystem today. In addition to Uptake, which I previously mentioned, Civis Analytics, Enova, Avant, and Grey Matter Analytics, are all data analytics startups that are all very interesting. There are of course lots of things going on at Northwestern and the University of Chicago, which is a benefit if we can harness it. There are also information driven companies that have a big presence like Nielsen, IRI, Transunion or Morngingstar to name a few.

One other anchors in place to help Chicago reach a leadership role is Hyde Park Angels.The group has been building a strong group of investors with data analytics expertise and has been focused on investing in companies both in Chicago and the Midwest with either direct analytics missions or applications. A couple Chicag examples are, RepIQ, which is building a research database of sales professionals and then using artificial intelligence to power recommendations for its users. Another is Catalytic which is using artificial intelligence (AI) to automate business process, with the AI ultimately learning how the businesses work and providing data insights into roadblocks.

What do these companies need to do to bring on customers? And what are the big obstacles for corporations to use these technologies?

John: One thing that is happening is that at the C-Suite level in most companies, regardless of the industry, people recognize that they better get on this train. There isn’t a company in the world where you go into C-Suite and they don’t say “We have more data than we know what to do with on our products and customers.” They know that their opportunity to use information to change their businesses for the better is significant. The cultural awareness is high, and the investment is starting to become high. Companies are starting to hire data scientists, but we’re still in the early stages of it. Most companies are experimenting and searching for the scaleable ideas and processes.

One of the things that is holding everyone back right now is that many data analytics companies have just one piece of the solution. I think that we need more holistic tools, services and approaches because point solutions or improvement in pockets will not drive major gains. The fact that it’s a booming market has people taking on bits and pieces of it and just those pieces alone aren’t really helping the companies understand what they need to do better.

Earlier, I think you nailed it on the head when you said that adoption is a cultural decision. What needs to happen for more data-based decisions to be made, and how do people need to think about it for it to happen?

John: I don’t know if there is one clear answer, but it definitely has to be valued culturally in a company. You need to have the attitude that we as a company are a fact-based, data-oriented culture. If you went to Amazon, that’s what they are. I once had a presentation from Alibaba in China and they said “Listen, we’re a data company, pure and simple.” These companies have a lot of data on their customers, they value it, and they hire people who value it. So, for companies that didn’t grow up in an existing data-oriented industry and who don’t have that culture, they are going to have to adapt and change.

If you were to make a prediction right now about where the industry is going to go, what would it be? Do you have a sense of what change is going to look like?

John: The thing that’s the most interesting to me is the number of companies that don’t exist today and the number of business models that don’t exist today that will be huge successes 10 years from now. They will be born in somebody’s ability to use information differently from somebody else. In some respects, companies that have been around for 20 years today were born by taking advantage of their new technologies of the time. I anticipate that we’re going to have a whole new set of companies because of the new capabilities of our time.