EP 277: What Is Your Data Really Worth Data Innovators Know. Splunk

Hi, everyone. This is Mitch Ashley. com You're listening to another DevOps chat cast.

Today, I'm joined by Andy Mann, chief technology advocate with Splunk. And our topic is a report that just recently released called What Is Your Data Really Worth? The ultimate question, you know, it's kind of from what's 42?

Is that the answer? OK. Anyway, another another topic.

So welcome, Andy. Great to have you on DevOps chat. Hey, Mitch, it's so good to be with you again.

Absolutely. Andy and I are friends from way back, so we know each her quite some time now. So this could get a bit squirrely.

Let's not let's let's see how we go do. Okay. Make sure I didn't have too much caffeine so before recorded.

So hopefully that will kind of keep me in check anyway. And we'll see. So for folks who don't know, you, tell us a little bit about what you do and what you do.

It's like being a little bit of your background. Sure. So, Mitch, I'm chief technology advocate at Splunk for our Eye Team Markets Group.

What that means as an advocate. I spend my time explaining stuff to other people and advocating for technology mostly. So I talk a lot.

A lot of people will see me doing podcasts like these. They'll see me at conferences and keynotes, writing and other things. A lot of outbound talking about Splunk and our data, everything platform and all the great solutions.

We've got to bring data to everything. But a lot of people don't see the other side of it, which is me advocating for my customers. It's one response, core values that we have two years and one mouth and we should try and use them, at least in that proportion.

So I try to listen a lot to experts like yourself, like other analysts and pundits. Customers, market makers. And and try and help Splunk create the best possible products and solutions to make our customers successful.

Sounds like when I was a kid, the seeing was God gave you two years for a reason. Mitch Ashley you weren't the only one who got told that as a kid, huh? No, no.

I'm guilty as charged. So let's just jump to the report. Tell us a little bit about if obviously you're in the data aggregation analysis collection, all kinds of interesting part of the data world, lots of different sources.

How did you decide to try to answer the question of what your data really were? What was the genesis of this report? Well, the first thing we did was we decided to fund an independent expert to help us.

So we went to Enterprise Strategy Group. Now, you know, then there an analyst firm out of folks, good, really good folks. They're key areas where they focus on data and security.

And so for the data part of this survey, they were a very obvious choice. So they helped us figure out a bunch of questions to ask to see if companies were using their data in advanced ways or not. So we were looking at things like how much data they use, where do they get their data sources from which business departments use data in their decision making and a whole bunch of other outcomes.

And we were able to figure out from that year with working with ESG, with Enterprise Strategy Group. We were able to figure out a certain percentage, around about 10 percent of the survey respondents, over 30, 100 respondents were what we call data innovators. So they were taking more data.

They were using it in more deliberate ways. They were using it more business departments and more business decisions. And so we were able to figure out, well, if they're using data in better ways, what are the good things that happen when you do that?

And comparatively, for the companies that are using data as sophisticated ways, what are the downsides for them? So it's a really interesting set of questions and answers that we managed to find some really interesting data on. Great question, Darce.

So tell us, what was the number one thing that jumped out was the learning you didn't expect to get from the study? So I think one of one of the learnings I think we did expect to get was that using data better helps you in your business in all sorts of ways. So the one big area I think I was a little bit surprised at was that using data better is not just going to help you save money, it's going to help you make money.

So for these data innovators, on average, they had a profitability of around 22 per percent growth across different sizes of businesses as well. That means about 38 million dollars average, total gross profit for these innovative organizations who are collecting, managing and analyzing data to improve their business. I think the second thing that surprised me was that this wasn't even split between top line and bottom line.

T. especially, we often look at ourselves as a cost center. We often told to do more with less.

T. is how important it is to making money, to adding revenue. But that's what we've found from this research data innovators added on average over five percent to their annual revenue because they were using data better.

And that added to reduction of cost of around five percent as well. So that's how we get up to that 10 to 12 percent on the bottom line. It's a combo of making more money and saving money, which is, you know, there's not a lot of technologies, but you can point to for that.

And definitely both sides of the line. That was five point three, two percent over 12 months as a result of their data use. Talk about how do you define a data innovator.

So it's all the things we looked at for the data innovators. Are they more or less sophisticated in this strategy? So do they have a data strategy?

They have a chief data officer. Do they have specific plans over a 12 to 30 more performance period of how they're going to get more data in and use that data? Do they have analytics programs in place?

Do they have a data science program in place? Yeah, these are some of the signals that we looked at to see what they were able to do with data. And then in terms of the outcomes, we looked at things like your revenue growth.

We looked at operational cost reduction. The ability to innovate. How long does it take to get new products or new ideas to market?

We looked at outcomes as well, like customer satisfaction, customer retention, ability to make faster decisions. So all of this sort of gave us this picture of what does a data innovator look like? Just to give folks an idea here.

This isn't like, you know, baseball. Everybody gets an award and gets to be called a dead innovator. This is 11 percent of global organizations.

Was your measurement for you. And so so it is kind of top, top 11 percent kind of cream crop of the crop of folks. I thought another interesting stat that I read in the report, it said, you see one in five data innovators generated more than 20 percent of their annual revenue from products and services developed in the past 24 months, compared to just two percent.

Did innovators. So so if you're if you are have really invested in, analyze, assessing, using and applying data, it that number is backing up what you said about it. Yes, it does add top line growth.

Yeah. Because I mean, when you've got data to go and you can make these rapid decisions, we talk about innovation. I've always I've written about innovation quite a lot and we've talked about it directly a couple of times.

And, you know, to innovate, you've got to be able to make fast decisions, you know, try things out. You know, typically, 95 percent of innovation will fail. That's okay.

As long as you fail fast, fail small, fail cheap and fail forward. But if you don't have the right data to make decisions, then all of a sudden you're in a state of paralysis or indecision, paralysis. You can't make those rapid decisions.

You end up going to get more information. You end up going with the loudest voice in the room. Or you end up going with the loudest person in the room, which, by the way, tends to work against some of the greatness of diversity and inclusion, about getting different opinions, about getting different and diverse opinions of viewpoints.

So if you if you have data, the data speaks for itself and you can set gates for innovation. This is classic innovation theory. You try stuff out, you do it small, you set gates, you pass the gate.

One example, for example, directly for this audience as well, is thinking about what is a high quality release. Is it a release that has no box? Well, that might be too high to get over.

But to understand what a level of testing is it gone through? What is the pass fail? Right.

What is the code quality? What is the compliance quality in your code? These sorts of things, the data points that you can then make decisions on even more so you can automate decisions based on data points.

If I've passed ninety nine point nine two percent of my tests and I've run 100 percent of the tests like specked, that's a really good mark and I'm probably gonna go straight into production with it so I can iterate faster. I can do new things in new ways because I have Shortie that the decisions I'm making are real and based on substantive information that will matter when I get to prod. So innovation is absolutely a strong outcome that we see in this research as well coming from these data innovators.

Now, I kind of threw the trick question. You first. What didn't you expect to learn from this?

I mean, no, Tuscola, when you read some things you did expect to learn, were there any, you know, small, medium, large surprises that you walked away from some of the answers that came out of the research? Yeah, look, I think some of the. And stuff was just around the vertical side in the industries that were goodlooking, industries that were bad.

I would have expected some other some industries who are lower on the ability to get data insights. I would have expected it to be higher. Surprisingly, higher ed and public sector.

They have a lot of access to data. They don't necessarily have the same issues with data analytics and aggregation that private sector do. Yeah, they've got things in place or a privacy and protection of data.

So there's some really positive things. Higher education especially, I would have thought with I have investigative units. Research is something they do.

I was a little surprised by that. What we saw was that technology organizations do really well by using data better. I sort of got that.

That makes sense. They're involved in machine learning programs and I programs and things like that. They're on the cutting edge.

Somewhere around two thirds of financial organizations had really good results in terms of higher revenue through better utilization of data assets. And again, financials are financial organizations often on the cutting edge of technology and so forth. So that made sense.

But for me, higher education, public sector, we're only down around 50 percent at this ability to use data in operational ways. And honestly, that surprised me. I think they could do a lot better.

I think they've got the fundamentals, the people, the technology, the inquisitiveness and the opportunity certainly to be able to use data in better ways. So, yeah, I'd love to see those numbers come out better. I wonder if you think there's a correlation or connection to this idea that those folks who were data innovators, their culture is what you termed in this report, quote unquote, data obsessed.

It was just data driven company. Everything is drilling decisions is driven by data collecting, using and Sanches loudest voices in your room. Seems like that's a pretty high correlation there to the folks that are really in a place that are leveraging data in a very successful way.

Yeah, the cultural aspect is really interesting. You know, we've talked about culture in the DevOps community for a decade or more and how important the cultural changes. And, you know, we know from DevOps you can throw all the tools at the problem.

If you don't have a culture of collaboration and sharing, then you won't have a collaborative environment to work in, regardless of what tools you throw out. Data is the same. The data's there.

The big difference between the data innovators and they are companies that aren't necessarily as as innovative with data. You know, the companies we call the data attractors and so will be different. They're inquisitive about the data that exists and go looking for it and look for ways to use it.

It's not necessarily they have more data. It's not necessarily they have different data. It's not even necessarily that they have data that people don't understand or understand.

Better or worse, it's that they have a culture that values data driven decisions. And so when the decision comes to the you know, the meeting comes to a decision factum, they have people in that room who deliberately put their hand up and go, what is the data saying? Rather than having the people in that room go, like, I think we've got everything we need.

What do we all think? Right. And that's a cultural change, Mitch.

That's a cultural difference. Having that data obsession, as we've turned it in the report, means that your culture is looking for actively looking to make those data driven decisions, actively looking to get data from everywhere and bring it to every decision, not just the important or less important or whatever, every decision. And that absolutely is a cultural difference.

So I don't think this is not a trick question at all. I'm interested or curious your thoughts on day to day to data. Extremely important.

The report showed the value and the impact it can have configured in an analysis standpoint. Sometimes you can get into analysis paralysis or maybe the insights aren't always just in the data, but from other factors and things. How do you how do you blend that?

Both tools, the experience, the knowledge, the capabilities of the organization and infuse that with data in a really healthy way? Or are your thoughts about that? Yeah, look, that's a super question, Mitch, because you know, the lies, damned lies and statistics right now create a cycle just where you can make it, say whenever you want to.

So you've got to be careful about stuff. You've got to be careful about bias. You know, I talked about diversity, inclusion a little bit.

If you all your algorithms are written by people that look and sound exactly like you in their study, you to reflect who you are or what you will. So having a variety of data and having a variety of algorithms created by a variety of people for different background. To have diversity in your teens, again, it's a cultural thing, right?

Your data will tell you what you want it to if you ask it. So you've got to have the ability to get more data. You've got to have the ability to ask data, ask continual questions of your data.

So it's not just the first answer. Typically, when you're doing data analytics, an inquisition, the first answer just pops up more questions for you. So you've got to be able to go through that iterative cycle of asking more questions.

That's a very fundamental and practical thing to do with how do you structure your data? What tools do you use to inquire after your data? You've also got to have the understanding that some things are not necessarily a data decision.

Some things actually don't have data and you do need to have personal experience. I'm a big believer, Mitch, and letting the machines make the right call on stuff they're good at complex data time series data. Hi.

Cardinality data, long aperiodic data. Humans are awful at things like pattern matching. Were awful at looking at long term data.

What patterns? Machines are really good at that stuff. So I'll say really at large volumes of data where we as humans love a data point and one you don't make headway millennials.

I have two data points at home, Simões, right? Oh, yeah. And Alnwick data.

Yeah. It's it's the bane of our existence, I think. But it's important to understand that machines can't make intuitive leaps either.

Machines have no imagination whatsoever. Have you ever seen some of the Harry Potter scripts that have been written by machine learning engines? Oh, no.

So awful. It's so bad because machines have no imagination. So there's a there is a cutoff point and it's a fair question to ask.

And I don't think there's any definitive answer of where that cut point years. But at some point, you need to have a human to interpret and bring imagination, bring intuition, bring experience to that data driven decision. But I would certainly posit that you bring that human experience to the data.

You don't just go with a human gut feeling. What to do? I don't recall this is looked at or at least talked about in the report.

I would imagine those data innovators have figured out where that balances. It's not just about having the most data of the data. The answer is always in the data.

Right? It's that balance of, you know, it's the right sources. It's the valot.

It's the negative validation, as well as the positive validation, the correlation, the analytics, the you know, how valid is this statistically valid as the information is? All kinds of things that data scientist know how to do that. Hope you be really good about how to use that data.

And of course, there's probably a maturity curve that you work up to. Yeah, exactly right. I mean, yeah, we see.

And it's not necessarily specifically in the data, but we absolutely looked at that maturity curve and what it means to be a data innovator versus a data adopt versus a data deliberator. Somebody in the early phase, for example, we deliberately looked at what it was like as a company, what patterns. And again, in the DevOps community, we're very familiar with this concept of patents and anti patterns.

And the patterns that the data innovators talk gave us a sense for what is a mature business. You know, we're not necessarily about to tell you exactly what those gates are that define good innovation or define a good test outcome or define a good marketing campaign or whatever. It is a product that will be successful.

But what we can do is help you understand what the data told us about data maturity. And so that's why we're actually working on we've actually loaded up onto these Splunk dot com website. A data maturity calculator says a free it's a Web based assessment tool.

It's free, obviously. So you can actually compare yourself against some of these data innovators. Yeah.

Really easy way to assess what's your data use, what tools you need to get the most out of your data, what data you're missing. And how do you compare on this data maturity curve to be able to make those decisions between smart, experienced individuals and definitive or maybe not so definitive data and data driven decisions? Interesting.

Great. Well, we're we've used our time pretty well here. Atlas certainly find out how to folks get this information.

Talked about this assessment tool where you and the data innovator curve are getting the report information about it. Yeah, absolutely. So it's it's all available up on Splunk dot com.

In fact, if you go there on the home page right now, you will see the data or everything platform and you'll see links there to be able to get that video. You'll be able to get also stories from some of our customers who have used data and turn data into you and your household names like Dominos and others really using data to create an impact. To be a data driven organization.

So, yeah, jump on to Splunk dot com. You'll be able to see the report. There you'll be able to jump on, read the report.

You can also take that assessment for yourself. It's going to be really fascinating to understand how you line up with those data innovators and where you can go to try and get a slice of that extra money that those innovators are getting. Great Splunk dot com.

Great place to go for lots of other information as well as this report. Say, by the way, before we end things. I hope you come back.

I'd love to have a conversation with you about data in the DevOps worlds. One, the challenge data has such a different nature, right, than here. What we can do more flexible is software and configuration, automation and things around software.

Oftentimes, developers sort of struggle with this more amorphous large piece of data or collections of data and how that's evolving along with things like DUBBA. So I'd love to have a conversation, right? Yeah, I would love that Mitch Ashley call.

Awesome. Well, hey, thanks a lot, Andy, for joining us today. Thank you, Mitch.

It's a pleasure. It's always great to talk to you. Always great to talk with you.

And I won't talk about where Australia fell on the list of data in rage, but that's another hard place. All right. Well, you've listened to another DevOps chat podcast.

I'd like to thank my my friend and colleague, someone I've known for quite a while. Andy Mann chief technology advocate. So thank you.

Of course you are. com and you just listened to another DevOps chat podcast. Be careful out there.