Does your business need to become data-driven?

Posted in: Business Insights, DevOps

Find out how the experts answered at Pythian’s Velocity of Innovation event in NYC

This summer I had the pleasure of moderating panel discussion that brought some of IT’s most innovative thought leaders together with some of North America’s top CIOs. This is the second in our series of posts that outline the discussions that took place at our Velocity of Innovation event this summer in New York. Our panel of experts consisted of: Paul Vallé, Pythian’s founder and CEO; Gene Leganza, vice-president, principal analyst serving enterprise architecture professionals at Forrester Research; and Otto Toth, CTO at Huffington Post. Event attendees included IT leaders from across a range of industries who supplied the questions that formed the discussion.

This series of blog posts focuses on topics covered in the New York Velocity of Innovation discussion this past summer. This post concentrates on a discussion between Gene and Paul about the importance of data as a driver of success. The question was: Do we think that every organization must become data-driven or is this just for customer facing marketing organizations?”

Here’s just a sample of the discussion:
Paul: Gene is one of the world’s leading experts on that exact subject. I’m just dying to hear what he’s going to say about this.

Gene: I’m a believer. It’s a really interesting space. But the question is how to get there, and the culture that needs to exist. I’ve had a lot of discussions with vendors doing interesting things with data. It’s funny, if I ask what their software technology does — how it’s being implemented, and the success their customer are having — somehow they always end up saying, “Well, but that’s a technology, the culture is a lot slower to change.”
Somehow the culture needs to change in order to implement a lot of these technologies and make organizations really data-driven. It is not something that’s going to change overnight. It’s largely why, I think, organizations are not yet embracing the notion of a chief data officer role. When I first did surveys on this maybe two years ago, four or five percent of organizations said they had or were planning on hiring a chief data officer in the next year. Now, it’s up to 50 to 60 percent depending on the industry and region. People don’t always do what they say they’re going to do in surveys. But I think what’s behind these responses is an understanding that someone has to lead the charge, not only on the technology and analytic strategy side, but on the cultural side — making the move to change the organization.

There’s so much data out there, and increasingly so, that there’s probably data to support any decision you want to make relating to your business. If you’re not using data to make the decision, you’re just going to be behind the curve. For a while, you can say, “Oh, we have really smart people who have been in the industry forever. They go by their gut and I really trust that, and that’s really cool.” That will be good a little while longer, but sooner or later as your competition starts getting better at using data to understand what to do and get predictive about what to do, you’re just going to be behind the curve. It takes a while to get there so I think people need to get started now.

When you’re writing business decisions, having business conversations, wondering which way the market was going, what people are buying, what the competition is buying, what your customer friends’ friends are buying, are all things you can now find out. Making assumptions about that is really unfounded. I mean, there’s just too much data. It’s really a race to see who can be better at finding the relevant information and getting the answers out of data than the next guy. It’s what it’s coming down to, making it easier and easier for the business people to do that without having to ask IT.

The old school here is if you need to analyze something, you need to ask IT to provision something in the data warehouse so we can do analytics. Now, you need to play with the data for a while before you know what you really want to look at. The ability to monkey with data from multiple sources to explore the answers to questions has become easy to do. If you don’t do it, your competition will, and you’ll be behind the curve. I think that’s really just the bottom line.
But also, you have to be an evangelist and a cheerleader. Being opportunistic and having a cheerleader explicitly lead the charge to get the people to think differently about it so that you can eventually change processes. It eventually gets done.

I recently I talked with someone who felt extremely behind the curve and they only recently did things like looking at data design as integral to the design phase of their system development. They had always, of course, reviewed design of the applications before implementing them, but data was always an afterthought. Applications create data, so what?

Now that they’re trying to get better at data, they had to actually explicitly make sure you got a data person in there designing the data, looking at the impact of what this application’s doing with the landscape or the information architecture they have and reviewing that before going off and actually writing a code.
That was new for them. It’s important for them that they instantiate that in the process. That starts to win over the hearts and minds of the developers who are doing this sort of stuff. In terms of getting all the various players to get it and do their part, using that data instead of flying off the seat of their pants, now we have a lot of socializing and conversation, but baking data-driven things into processes.

It’s not something that you can do upfront and necessarily just expect a process to take over because then it looks like bureaucracy and people hate you for slowing down their life. You have to be very careful about introducing ideas into the organization and then try to bake them into the fabric of the organization before people see the actual value and they’re actually bought into the concept.

Paul: I have something to chime in, it’s actually in contradiction to Gene, but it will prompt, I think, an interesting discussion right along the lines of your question.

I find that one of the nicest ways to look at a business at a holistic level is to put a lens in front of it. I’m just going to use Fresh Direct as an example because I’m in New York City and I’m seeing the trucks all the time. You all live here, you might order their groceries. It’s useful to think of FreshDirect and look at the business through the lens of a grocer. You’re going to talk about the thickness of the steaks, you’re going to talk about the freshness of the vegetables and that’s great. It’s a great way to look at that business. You can also look at that business and think of it as a technology business. To what degree is FreshDirect a technology business that happens to deliver groceries the way is a technology business that happens to deliver books?

That’s a useful way to look at the business. To me, this whole data idea and the chief data scientist idea is the new lens and that’s what’s powerful about it. It lets you look at FreshDirect and say, “Hey, to what degree is Fresh Direct a data business?”

As for Huffington Post, it’s a media business. You need to be able to look through the media lens to you look at the quality of the content. You need to look at your reach and your audience, but to what degree is the Huffington Post a technology business? That’s a new lens. To what degree is the Huffington Post a data business?

That’s where sometimes, not always and not for every business, but sometimes you realize, “Holy cow! I am a data business.” Sometimes you realize that the most game-changing investments that you can make in your business involve adopting insights that emerge from the data. If you look at Fresh Direct and you say, “Well, Fresh Direct is a logistics business.” It is. Maybe when you look at Fresh Direct through that lens, you have a ‘holy cow’ moment and you realize that the investment in your supply chain management or your logistics allows you to compete better. That’s where your most efficient investments are. That’s okay.
What’s powerful about data is that we never used to talk about the power of our data as a lens through which to look at our businesses holistically and then to quantify our investments. To rephrase your question, I think it’s a really good question, but I wanted to just disagree with Gene just a little bit. I don’t know whether the crowd will lie, but in general, you should listen to Gene.

I think one of the interesting twist on your question is which of us, which of you are looking at your businesses through the data lens and seeing opportunities for investments that you feel that pressure in your belly, in your gut that you need to claim that opportunity?

Attendee # 1: Data can make an enormous difference to your business. Deciding which data to pay attention to, which to collect, where to source data, and what insights you’re looking for is almost more of an art than a science at this point.
That’s why the whole data scientist’s movement, it’s like the most sought-after position, it’s a huge growth space and it’s because there isn’t any prescriptive method. You have to bring judgement to the table. Choosing the data that’s important is probably the harder part right now. If you choose the wrong data, then it doesn’t make sense.

Attendee # 2: I think you need to invest not just in good talent, but also in good knowledge and optimization especially for a legacy business model.

Gene: That’s a really big challenge. We just got some results from a survey that asked what biggest obstacle was to getting more data-driven? There were a bazillion answers that all have some weight. At the top of the list is hiring the right data talent. Obviously, that’s a big deal for everybody. It’s not just data scientists. It’s always interesting to look at how you define what a data scientist is. The best definition of data scientist is somebody that would totally impossible to find because they would bring together PhD level education, a hard science level of math and algorithm knowledge, combined with the skills of a software engineer. All this would be combined with someone who has a domain knowledge we were just talking about it, it’s not just numbers.

This is just a sample of the our discussion on data at the New York event this summer. More of these sessions are planned for the coming weeks. To request an invitation to a Velocity of Innovation event in a city near you, visit

Velocity of Innovation is a series of thought-leadership events for senior IT management hosted by Pythian.The format is a panel discussion in which Pythian leads conversations around today’s disruptive technologies. Topics range from big data and cloud to advanced analytics and DevOps. These events are by invitation only. If you’re interested in attending one of our upcoming Velocity of Innovation events, contact [email protected]



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About the Author

Lynda Partner is a self-professed data addict who understands how transformational data can be for organizations. In her role as EVP of Data and Analytics, Lynda focuses on Pythian’s services that help customers harness the power of data and analytics and holistically manage their data estate.

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