alphaspirit - Fotolia

Developing a data-driven culture begins with enablement

In an interview, Jennifer Redmon, chief data and analytics evangelist at Cisco, talks about how she helps organizations enable their employees to work with data.

It's imperative for organizations to develop a data-driven culture.

Data is what has enabled many enterprises to pivot their businesses and survive the economic crisis created by COVID-19. Data is what has helped health care organizations make decisions throughout the pandemic. Data is what empowers employees at all levels of organizations to make the decisions that spur growth. Data is even what helps sports teams at economic disadvantages to their competitors to remain competitive and even thrive.

Meanwhile, failing to develop a data-driven culture can have serious negative consequences.

Jennifer Redmon is the chief data and analytics evangelist at Cisco, and on Oct. 13 she helped lead a breakout session at the BI vendor Looker's virtual user conference, JOIN@Home: "Easy to Say. Hard to Do: Building Data-Driven Culture at Scale."

One of the many challenges Redmon faces in her role is the rate at which Cisco acquires other companies. At the end of July, the tech giant acquired Modcam, and since then it has said it intends to acquire Portshift and BabbleLabs Inc. Over its 36-year history, Cisco has acquired more than 200 companies.

Those acquisitions become business units of Cisco, and Redmon and her staff have to make sure those business units have the resources -- technological and educational -- they need to be data-driven.

Before speaking at the conference, Redmon took time to discuss the importance of developing a data-driven culture and how she helps develop that culture not only at Cisco but also at the nonprofit organizations at which she works through Cisco's partnerships.

What does it mean to be a chief data and analytics evangelist?

Jennifer Redmon, chief data and analytics evangelist at CiscoJennifer Redmon

Jennifer Redmon: When I first met a data evangelist, I thought it was a silly job title because of the way it was explained to me. I asked a woman what she does as a data evangelist and she said to me she encourages people to use data. I asked, 'Which data models do you use?' and she said, 'I don't.' I walked away and I thought that seemed ridiculous, so it seems like a little karma that I'm now the chief data evangelist. But the way I earned this title and this position is by creating services, products and platforms that support a data-driven culture.

I report to the chief data officer, and what I started looking at was how to create a data-driven culture, so not just looking at the architecture, not just looking at things like threading data and a lot of the traditional IT elements, but how to create the cultural elements that are needed.

Strategically, how do you create the elements that are needed to develop a data-driven culture?

Redmon: I've put together a framework and a model around thinking about workforce strategy planning and how to support people as they try to make better decisions through higher quality and larger amounts of data, and the idea is to look at this job by job. What I started looking at in support of our overall data and analytics organizations, which was put together about two years ago, was how to support not only the traditional IT capabilities but what it really takes to become a data-driven culture. In many ways, Cisco is already a data-driven culture, but we're also a company of acquisitions. We just announced a new one, and we have over 200, and so we need to look at how we create a framework that we can use and reuse and scale, and not only that but a framework that I can take to the nonprofit work I do.

I work mostly in the suicide prevention space, but I also work with various nonprofits including those in education and I'm very passionate about helping them create data-driven cultures. It's really about how to look at it as a model instead of asking what it means to be data-driven and then having a long list of things.

What are some of the details of that model?

Redmon: The idea is there are two axes. The first is data IQ, and that is defined as the knowledge you need based on your role -- the knowledge that I need for my role and the knowledge that someone who's an executive assistant or salesperson needs is completely different. And then data enablement is the other axis. That's the community practice, the platforms, the services, everything else you need to be successful.

And what groups are formed by plotting someone's data IQ and their data enablement along those axes?

Redmon: I'll start with the data illiterate, and I will say I don't think I've spoken a to a data illiterate person in years. Those are people who don't have the data education for their role and they don't have access to the tools or services or platforms they need. Siloed high performers are people who have a really high knowledge about what needs to be done and can do much more in their role but they need a cloud platform, or a data dictionary, or to more easily be able to share their models with people. Enthusiasts are people who are high in data enablement and use different products and services, and they're very excited about the future of AI and data science and all they need is education. And then you have data-driven, so people in a given role who have the education they need, the ongoing education they need, and they have all the different support platforms to actually achieve what they need.

The view that most people have about evangelism is that it's about me telling people why they should use data or how to use data, but it’s really more about listening to what the obstacles are to using data and making it part of their workflows and work streams.

What are some of the more common obstacles organizations face to developing a data-driven culture?

What I started looking at was how to create a data-driven culture, so not just looking at the architecture, not just looking at things like threading data and a lot of the traditional IT elements, but how to create the cultural elements that are needed.
Jennifer RedmonChief data and analytics evangelist, Cisco

Redmon: Back when I graduated from college, data science wasn't a major, so what you have now is this transition where you have people coming out of school with a degree in data science, and then you have these people with an immense amount of experience in data science, 20 or 30 years. You have this gap between people who have a fantastic understanding of the capabilities, and then you have a lot of people who know the domain but they haven't learned the different tools, so one of the most important parts I've found is the need to upskill people who have that deep domain knowledge. I think the Houston Astros case study is a great one. What they did was they quantified the staff's intuition so they didn't just throw away the knowledge of these people who had this incredible experience. They said, 'Let's just quantify that; let's use that as a data point.' I think we're in this transition where people are trying to figure out how to merge [people with different approaches].

That's a really common problem across a number of companies I've spoken with, and then across the nonprofit and for-profit space. I think that it's really important to not throw away all of the historical knowledge that people have developed over their careers and find a way to integrate it with the new ways of working with data.

What are the benefits of a data-driven culture?

Redmon: The benefits are that people are making better decisions. At the end of the day, it's not about the data itself. It's about what the data is enabling an organization to do, and in my experience that's really about improving decision-making across the enterprise and the incorporation of decision science. At the end of the day, what we really care about is whether we're making the best decisions, whether we're most informed, whether we're seeing the situation as it truly is, whether we're using the data to de-cloud potential biases.

We're talking because you're speaking at Looker's conference, so what's the role Looker has played in your data evangelism at Cisco?

Redmon: Looker is a fantastic product and one of the numerous BI tools employed within Cisco right now. One of the things that I'll do is work with different groups to understand within that data enablement stream what are the best tools for them, how do we best meet their needs, and Looker has met a lot of the needs of the company. It's not by any means the only tool Cisco is using. We have so many different lines of business that there's just a lot of varying needs. But Looker is really great in terms of the ease of which you can develop queries, you can customize them, you can run them on regular intervals. The ease of use for different people around different levels.

What are some of the other BI products Cisco employs?

Redmon: We use Tableau, we use Power BI. And we probably use some that I'm not aware of because we're a company of acquisitions. We just brought one on, and we announced our intention to buy another. But Cisco is very open to exploring new technologies and new spaces, and Looker and these other tools are in a very important space.

What will your process be as you assess whether the new acquisition has a data-driven culture and work to bring them into the Cisco ecosystem?

Redmon: My process is really to listen and understand, and to make a lot of connections. What I look for from a cultural perspective is what they're looking for, what can they benefit from, and what do they have to offer. It's really interesting because there's so much innovation happening all the time that the value an evangelist can create is to see that one company is innovating here, another is innovating in that area, and how we can connect some of the data sets in one way or take the insights another is getting in real time and push them that way.

In a lot of ways I liken it to some of the research that's come out about the business translator role, and the importance of that is being able to move from the business outcomes to the technologies needed to the different solutions and go through that process over and over again.

With all these different acquisitions, does Cisco want the data culture essentially the same across what become different business units?

Redmon: What I try to do is listen and see what the business units need. I'm in a role where I get to constantly learn from brilliant people, and the leaders both technical and managerial across Cisco typically know what they need and know what they're looking for and know what they can offer, and so I don't try to impose anything with them as I work with them but rather to really understand and then help them fulfill their needs. And then I can also tell them, 'So and so is also working on this and it sounds like this would be a great collaboration.'

Do you ever run into a situation in which there's an acquisition and there's no data-driven culture, or perhaps a situation in which there's an IT department and everything has been kept locked down?

Redmon: I haven't, but I'd also say that I frame data culture differently. I frame it in terms of the information we use to make decisions, and every organization has some type of data culture. It's just that maybe that data culture surrounds more structured data, semi-structured data or unstructured data, and it may be more based on knowledge and experience and intuition. I'm working with a retired Army general right now, and one thing he's mentioned is he wants to become more data savvy. I'm like, 'That's fantastic, I can teach you a lot of the modern methodologies and concepts, but don't throw away all your years of experience. This is really valuable, high quality data that you use to make decisions and we're not going to throw that away and not call it data anymore.' It's just data of a different type.

Editor's note: This Q&A has been edited for clarity and conciseness.

Next Steps

Good data-driven decision-making avoids common pitfalls

Dig Deeper on Data science and analytics

Data Management
Content Management