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Five Steps for a Data-Driven Future – Futurum Tech Podcast Interview Series

On this special episode of the Futurum Tech Podcast Interview Series, host Daniel Newman is joined by Doug Merritt, CEO of Splunk, to discuss the five steps for a data-driven future including how to establish data principles that make for a clear future. Without data we are flying blind — something companies can’t afford to do in our current competitive environment.

In our conversation, Doug and I explored the questions companies need to ask to define their data principles. There’s a billion different ways data can be sliced, diced, collected, analyzed, and utilized, but you have to know what you’re trying to do from the start. Questions like what data needs to be accessed, what data needs to be treated differently, and what will you do with the data need to be defined from the start.

Principles aren’t black and white. Doug shared thoughts on what often gets companies sidetracked when it comes to data principles. Too often, companies try to get too specific and black and white on exactly what is allowable and what is not. We are constantly moving and changing. Data principles are meant to serve as a framework. They are not meant to answer every question, but instead to give guidance.

Data principle framework. Doug and I discussed the core framework that should be in place when dealing with data. First, and most importantly, companies need diversity. The more angles and diverse data sets you can get, the better. Second, companies need collaboration. It’s important to bring as many different viewpoints, skills, backgrounds, and experiences of the people that are attacking the data together. Third, companies need to rely on curiosity and an open mindset when working with data. Finally, if you can bring diverse data sets, different collaborative sources and viewpoints, curiosity and an open mindset, you need to be able to admit that your viewpoint was wrong or that it changed. You need courage.

The human element of data. We also discussed how to bring as many people as possible to the data. To master this proposition, companies are going to have to focus on training, education, exercises and even “lunch and learns” around data. If you want to solve a problem, you need employees from all aspects of the business, with the right expertise.

Making data actionable. Lastly, Doug shared that employees need to be empowered to make decisions with the data. The people closest to the data, closest to the insight need to be able to make rapid, real-time decisions. At the same time, there should be checks and balances put in place to ensure that the right decisions are made. There should also be feedback for the employees and the management on whether the decision was effective or not.

If you’d like to read more about these five steps be sure to check out Doug’s thought-provoking article. Make sure to listen to the full episode of the podcast and while you’re at it, hit subscribe so you never miss an episode.

Disclaimer: The Futurum Tech Podcast is for information and entertainment purposes only. Over the course of this podcast, we may talk about companies that are publicly traded and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors and we do not ask that you treat us as such.

Related insights from the Futurum Team:

Data To Everything Makes Sense 

Splunk Posts Solid Results As Recurring Revenue Grows

Splunk’s Transformation To Cloud Continues With Strong Q4


Daniel Newman: Welcome to the Futurum Tech Podcast. I’m your host today, Daniel Newman, principal analyst, and founding partner at Futurum Research. Joining me today is Doug Merritt, CEO of Splunk. I’m super excited to have him on the show. Doug, how are you doing today?

Doug Merritt: Good. Thank you very much for having me on the show, Daniel. Always a pleasure to spend some time together.

Daniel Newman: Yeah, it was fun. We got to kick off this relationship of doing shows together, I think it was late March, early April. Right as the world was entering its first phase of chaos earlier on about all the things going on with COVID, which you guys were up to at Splunk, how the company was approaching this new work from home situation. It just seemed like a great time to get you back on to talk a little bit about where things are at. We’re slowly but surely turning the corner. First and foremost, how are you feeling? How are you holding up, given all this turmoil, and basically running a company in a way that you really probably never expected when you took the CEO job at Splunk?

Doug Merritt: Yeah. I’m sure a lot like you and all of our listeners, it’s taxing, it is. I feel more tired than I felt in a long time. I think it’s a combination of usually 6:30 or 7:00 AM starts with an early morning Zoom, or WebEx or Team, or depending on what the customer wants. That then ends at 6:00 or 7:00 or 8:00 o’clock night. So it’s fatiguing, I think, for all of us to go back to back and stare at these screens all day. And then you add in all the normal concerns that we all have, and we’re all part of the human race, so it’s hard not to turn off your mind to the plight of what’s happening around the world and how it’s affecting people. And certainly the civil unrest and the horrible set of events that we saw with George Floyd. And it just adds to the difficulty that we’re all dealing with every single day.

Daniel Newman: Yeah. Thanks for drawing attention to that. These are really important, and a lot of conversations that need to be had. And as part of the tech community, we certainly can play a part. Right now I’m listening, I’m learning and I’m really trying to spend a lot of time reflecting on how to do better. I’m really glad to hear from you. Had a lot of conversations with a number of clients. So we’ll have to come back to that at some point, talk more about that. Because I think, as companies get to their senses of what we can do, we’ll certainly want to keep bringing this up to make sure it doesn’t just become a blip and then go away.

Doug, to what you said about tired, who would have thunk that not having to go to work would lead to working so much more? Being 30 feet away from my office at all times has led me to working some of the longest hours ever. I used to schedule time in to go to the gym. Well, I can’t go to the gym anymore. So now my gym is upstairs or in my basement, I have a few free weights and I’m down there. I’m like, “Oh, I got to go do something.” And instead of where we’d be gone for an hour, an hour and a half and you’d make a note, you’re like, “Oh, I’m just going to run back up to my office and do a little work.” Or you’re sitting at the dinner table, eating dinner, and all of a sudden you get a message. And you’re like, “Oh, I need to respond to that.” And then you climb back into your office at 7:30 at night.

I think I’m working like 16 hours a day now. It’s pretty much work, eat and sleep. So anyone out there that said the work from home thing, Doug, was going to make lives better and more balanced, this has got to be the proof point. No wonder some companies are like, “You can work from home forever.” Because employees are working like crazy.

Doug Merritt: Yeah. I definitely think there are new barriers, new patterns, new disciplines that we need. I’ve felt like over the past five, 10 years, I’ve gotten better, because of the forced cutoffs and starts, to try and cordon off work and personal life a little bit better, so that I wouldn’t be at the dinner table and all of a sudden take my attention away from the kids, especially if I had been locked up in the office the entire day. And that has all blown apart for sure. I think a piece of it is, we’re all dealing with the adrenaline of, how do you quickly pivot and keep our companies, our organizations, our families, our society moving? But to make this sustainable, there does have to be a better balance of how quickly we respond and how we’re spending time on ourselves and our families and people that we care about.

Daniel Newman: Yeah. I think we have a lot to learn. I think in the beginning it came down to balance. It came down to keeping ourselves, keeping our companies in good shape, our responsibilities to our organizations as leaders, the pivots we had to make, to make sure we stayed relevant to our customers. I think we’re starting to find that balance in terms of our companies. We’ve started to know what our plight in the future might be like. But at the same time now we’ve got to rebalance ourselves, recalibrate ourselves a little bit.

I’ve got half a dozen topics I want to talk to you about. Because I read something really interesting that you wrote about driving a data-driven future. But before I do that, I did want to just ask one question kind of as a recap, when we talked last time, you talked a lot about the new approach that Splunk had to take. Now that you’re about two months into it, in terms of work from home, in terms of new means of getting to customers, in terms of new ways to lead from afar and leadership, managing teams, I’m just curious, how has it all gone? Was it sort of as expected? Can you take a minute and just share a couple of experiences or teachings or observations?

Doug Merritt: I think it’s gone better than I could have ever imagined. And it’s something I hear a lot with the CEO forums and the one on ones I have around the world. If we weren’t in 2015, 2020, with the investments we’ve made, I can’t even imagine how the world and businesses would have responded to suddenly be enforced in mass around the world to shelter in place. So it’s it’s awesome, it’s uplifting to see customers, and partners, and agencies, and local entities making themselves available, being excited about, what can they do to lead through this charge?

So it’s not easy. As we talked about, we’ve got to figure out the new normal, we’ve got to figure out new boundaries. And we definitely are rotating harder on current customers than prospecting for new customers, and that’s not a longterm sustainable trend either. But people have really risen to the occasion internally and externally. It’s positively inspiring when there’s so much other chaos going on around that isn’t as positive. We need some beacons of light.

Daniel Newman: Yeah, small moments, and beacons is a great way of saying it. I did cover the earnings for Splunk, and I won’t have you touch on that, because that’s something I think once a quarter is enough for most of us, but I would say it was encouraging. There was some very encouraging data. The company’s pivot to cloud and to recurring revenues and ARR has been… My take on it, Doug, was that you guys are well positioned, and for a company like yours, you’ve got, as we start talking about data-driven future here, data is going to continue to grow exponentially.

So as you’ve set up something that’s going to be dependent on volumes of data that drives additional revenue, it’s a really nice position to be in, especially when you’re really the overarching data platform that sits on top of everything, that says, yes, all your data can be utilized, mined, enriched, enhanced, visualized to run your business. I’m not an equities analyst. I don’t do price targeting. I’m an industry analyst.

But I do watch the two, the strategy and the earnings very closely because I think they’re very important in alignment. And I’m very bullish on what I saw. So I think as you guys can get back out there and start driving net new, and even just getting closer to customers again, I have to imagine you guys have to feel good.

Doug Merritt: Yeah. I really do believe, and hopefully all of you listening believe that data makes a massive, massive difference. Without data we’re flying blind on so many different decisions. You can see it in how we’re trying to manage the both progression of COVID as well as potential therapies and vaccines. You can see it with the lack of data around the plight of black Americans. Looking out across the landscape.

There are so many holes in data. You can certainly see it with, what does it take to get all your employees virtual? You need data from every piece of infrastructure. You can see it with, how do I get my players back to work? From simple data polls like, do you want to come back? But even more importantly, how do we create an effective environment for you?

Data is at the heart of every answer out there, I think. Every crisp well-informed balanced answer. And it helps to be an Uber data platform. Somebody that also I’m really excited about is, I’m so thankful that we’ve shifted our pricing model last year to focus on infrastructure as a charge rather than data volumes. Data volumes continue to explode, and every single customer that I’ve talked to that’s shifted to a core or a CPU metric has had a significantly different experience than the data volume. I think it’s just perception. Now that I’m not focused on data volume, I just bring data to every question, every decision and every action, and then worry about infrastructure instead. I encourage all of you that are listening that are customers or might choose to be customers, go with the infrastructure charge. It will make your lives much better.

Daniel Newman: Oh, absolutely. And again, it’s the model, it seems to be working. But either case, whether it’s more data or more compute, you need more compute to support more data. So there’s intrinsic relationship between the two.

So I read the interesting piece that you’ve been touting and talking about, one of your more recent tracks has been on, the five steps for a data-driven future. So I’d love to walk through these with you just for a few minutes here. You start off talking about defining data principles and trade-offs. Talk a little bit about your approach there. When you say that, what do you mean by defining a company’s data principles?

Doug Merritt: So I think to effectively solve any problem, you have to understand, where do you currently sit? And be crisp on, where do you want to go to? Data has capabilities. It has being. It has presence, like everything else. We know all the energy that’s happening around the world right now on data privacy and data use and anonymization. There is no clear black and white answer, just like there isn’t in anything else in life. So I think getting very clear upfront on, how do you feel about data? What data do you feel is acceptable to access?

Which data do you feel may need to be treated differently? And what data do you need to completely black or mask or ignore for whatever your ethics, your principles, your business needs are? Because it’s hard to go back. Once you start accumulating certain data sets and weaving it into everything that you are driving with that data, it helps if you’re very prescriptive and thoughtful upfront on those basic data principles and beliefs and practices.

Daniel Newman: Yeah. I think that makes a lot of sense. Because I think this is one of those starting points. And I’m sure you maybe feel like I feel, but I’ve been talking about this for years, getting the foundation of your data strategy and data management, and what your outcomes are going to try to attempt to accomplish with all this data. I still feel like a lot of people are eating the elephant and they’re not doing it a bite at a time.

So what I love about your idea here really about defining your principles here is, what you’re really saying to people is, there’s a billion cuts, a billion different ways this data can be sliced, diced, collected, analyzed, utilized, realized. But at some level you have to understand what you’re trying to do. You have to start. There’s no question companies are using data, but there’s such a long way to go. So you talked a little bit about the approach, but after you come up with that, then I think companies need to create definitions for their data principles. Right?

I’m sure Splunk has some thoughts on how companies should go about doing that. How do they go about defining their data principles? What would you tell them?

Doug Merritt: So I think the important part about principles, where I think legislation gets sidetracked is, it tries to get so specific and so black and white on exactly what is allowable and what’s not, the definition of everything, and the world is a constantly evolving place, it’s very gray and muddy. It’s not binary and black and white. Even though our compute landscape and often data is a binary format. So I think with principles, they’re beliefs, they’re frameworks that are meant to not answer every question, but to give guidance.

A simple example, do you have a principle that says you should not touch any personal data, data that distinctly describes anything about an individual? I think it’s very difficult to answer lots of questions without that data. But when you look at the different standards that are trying to evolve around the world, the detail that they’re trying to go into, and things like GDPR and the California Privacy Legislation, others, actually creates more confusion than answers.

So I think a lot like any great piece of writing and work, like the Constitution, there are a handful of points, right? It’s a lot of work to feather it down to three, five, seven core guiding principles around data. It’s going to be different for every organization. And as you can see with the competing legislations around the world, there is not going to be agreement, because different cultures, different societies, different groups are going to have different perspectives on what principles are appropriate and which ones aren’t.

Daniel Newman: You could almost argue the need for a data-driven mission statement for each company. Because remember, you’ve probably been through this with Splunk, but for a company, the hardest thing in the world to do in your company that does as many diverse things as Splunk does, is to say, how do we encapsulate this into a sentence or two or less where the whole world can understand our purpose? And I think with data, it’s a little bit of that same, like our data itself almost needs its own mission inside of an organization. And that becomes the foundation of every decision you make from there on forward.

Basically, by the way, that just came to me as I was listening to you talk. Because if you look at the way content is consumed, right? If I write an article or publish a thought leadership piece that says, Five Steps For a Data-Driven Future, people can read that, they can consume it, they can digest it, they can understand it. If you just write a very in depth manifesto about how to use data, people will walk away and they will be like, “Wow, that’s great. I don’t know what to do.” And so it’s almost like you’ve got to boil it down narrow, and then you need to come up with your five points, and then everything you do lives from those five points.

Part of this whole process, Doug, is about, data has been proven, we saw digital transformation get accelerated from a 10 year schedule to a two month schedule. Now, that’s somewhat in jest. But the reality is, the companies that were in the most agile transformative states, your Netflixes, your Amazons, your even Chipotles. So it wasn’t just in tech. It was companies that had tech-driven, agile business models that were data-driven, that had great eCommerce, used AI. All those things were in place. They basically came out of this whole disaster unscathed in a lot of ways. In some cases, they came out better. But for a lot of businesses, their transformation, this was the forced transformation, and they’re going through that process. But we want to get back to some semblance of normal, and data is going to be a key to that.

Talk a little bit in your mind about how businesses can follow these principles and can coordinate to get back to a state of normalcy. It may not be the same normal, but a state of normalcy where they can operate towards the future.

Doug Merritt: So let me blend in a separate framework that we have around data-driven leadership, what is necessary to be very effective with data. We try to boil that down to four simple principles as well. So we’re going to mix these two up. I think it helps.

Daniel Newman: Five, four, three.

Doug Merritt: Yeah. It helps answer that question around data principles. There are four things when I look at data, and 30 years of being involved with everything from relational database technology to master data management, ETL, to BI, to now a big data platform like Splunk, the first is diversity. These sound like potential management principles and hiring principles also, because I think they’re very similar given that data has its own life and ethos as well. We all benefit from diversity, and that is true for data as well. I think a key principle around data is, focus on diversity, diverse data sets. The more angles you can get of data, not just what’s inside your organization, but outside the organization, and it’s going to be messy, it’s going to be chaotic. Your job is not to make it clean and scrubbed and vanilla. Just actually take all the garbage with the non garbage, because you can’t often tell a difference between those two in data. So diversity is a good principle around my data strategy.

Collaboration is a really important principle around data. You need to bring as many different viewpoints, skills, backgrounds, experiences of the people that are attacking the data, investigating the data, interrogating the data, playing with the data, to actually get interesting outcomes.

Curiosity is another really important framework for a data principle. I can always tell in my own mind and I can usually see it in writing when I am using data to justify a position versus when I’m actually staring at data, playing with data to try and form a position. And I think that second one is much more important in life. Let data actually help inform what your thesis is and what your actual outcome is going to be.

And then finally, courage. If you bring diverse data sets, you bring a ton of different collaborative sources to that data, you bring your curiosity and your open mindset data, it’s going to tell you things that will force you to reverse positions. And if you don’t have a courageous viewpoint, if you’re not willing to admit that, while you came out with proclamation A based on the data you had available at that time, you’re switching to proclamation B because the data told you something else. And there’s no point in having those data principles and doing all the work to actually let data inform your decision making.

Daniel Newman: I love that. I really liked that last point. I’m going to switch over to employees in a second. But I just want to reiterate something you said. I think a lot of the problems we’re facing in our world right now is, people shape data to fit their view as opposed to look at data to try to learn and expand their horizons and the opportunities that data provides. I’ve said this forever, I’ve said, it seems to me right now, the reason we’re suffering such a great divide in the world perspective, it doesn’t matter if it’s been around equality or it’s been around healthcare with COVID or just a decision in your everyday business, has been people come into things with a certain worldview and then data is used to inform that view and support that view. As opposed to walking in saying, “I have a feeling, but I really don’t know. And you know what, let’s see what the data says, and then let’s make a decision with no bias.”

And by the way, as humans, that’s almost impossible. So I think it’s like a phase thing, Doug, do we go less bias so that we can at some point… Because I just feel like right now, we’re not even trying. We don’t try at work. We don’t try in policy. We basically said, this is the view we have. We’re just going to round up every piece of data. It almost mitigates the success of what we’re trying to do, because we don’t use so much of what’s available to us.

Doug Merritt: Yes. Even this conversation on bias is interesting, because bias is absolutely critical and necessary to extract insight from data. You have to have a point of view on the world to then learn and pivot again. The important part is to be aware of, what biases are you baking in to actually get the results or the outputs or the guidance that you want? And to bring multitude of bias where possible to two data sets, understand what they actually are, and then be willing to switch those biases, going back to the courageous parts, based on what you’re learning from your iterative process.

Daniel Newman: Yeah. Which by the way, also goes back to your comments on a diverse data. Diverse data will mitigate some of the bias. And the courage that you’re talking about is really the courage to say, I was wrong. The courage to say, my bias was incorrect. I’m glad I had a chance to see this and it helped shape a new view. And again, this could be intimate things and this can also be just everyday business decisions. And all of them can benefit from data. So I love that. Flip this for a minute though, and talk about, how do you deliver this at scale?

So you get it and you’re talking about it, but for this to work, it really comes down to companies empowering employees, empowering every worker to do more with data. They need to be able to read it, they need to be able to take advantage, to react, to be opportunistic. Any thoughts about how companies can do it? And by the way, I would expect some of what you just said will be the same to deliver this at scale.

Doug Merritt: Yeah. I think there’s multiple different aspects of scale. One, do you have a set of tools, systems that can actually deal with the volumes of data, the variety of data, the change rate of data so that you have the opportunity to actually get meaning, get momentum, get progress from that data? That’s one of the defining hallmarks of Splunk is, we are unique in our ability to deal with the scale of streaming data, that the scale of data at rest in something like our index, this scale of thousands, tens of thousands, millions of questions in that data.

Then the other part of scale is that human side that you were mentioning, Daniel, which is, how do I bring as many people as possible to the data? And how do I inform them with an investigator’s mindset, that curious mindset, that your job is to have your background, your experiences, your biases, and then bring that to the data and play with the data to get the output? I think that is possible to master through training, education and a continuous set of exercises, hackathons, lunch and learns around data, to see what emerges when you combine HR expert with a manufacturing expert, with a data scientist, to try and solve some vaccine problem within your company, your supply chain or your community, that’s been unassailable for the past five years.

Daniel Newman: Yeah, absolutely. And what you mentioned is, there’s this democratization that takes place that has to be put in place to essentially enable the vast majority of the workforce that’s working with data. Then there’s the other side though, you break it down and then you have to roll it back up and put it all together. Basically, kind of bridging the gap for companies. Because the one thing, and we talked about this when you were talking about those four points off the five points, and maybe you can give me some thoughts in three points, but is, how do you get people off of that gut driven…? Because it’s a culture thing, right? It’s not so much a tech thing. Because the tech has long been able to get people off of the gut. But to really move forward and to realize a data-driven future, we have to eradicate gut driven decision-making that cannot be validated with data.

Doug Merritt: Yeah. There are two things that we’ve done over the past year, that we still have a lot of work to inculcate throughout Splunk, that I think have made a big difference in that culture piece. One, we brought the narrative format to meetings and discussions. It was something that I’ve read about and wanted to do for most of my career. And we finally put it in place 12 months ago or so. Where that helps is, when you write things down into concise three, four, or five page documents that everybody must read at the beginning of the meeting.

Now the potential for ambiguity decreases, because you actually have to define terms, which we skip over all the time in PowerPoints and casual face to face conversation. We say cloud and it’s like, “Well, we both mean the same thing for sure.” And as soon as you define, “Well, what exactly do you mean by a cloud? What do you mean by that metric?” Which you need to do in a narrative. It’s easy to walk out thinking you agree, but you hold different perspectives or biases.

Then the second piece is, only have data-driven discussions from a central data repository, no independent spreadsheets, PowerPoints, or independent calculations are allowed. We’re in progress with that. We’ve established an effective data warehouse. And the only metric that are shown are from that data warehouse. And if they’re wrong, awesome, opportunity to understand why they’re wrong, fix them, but fix them at the source, so that you don’t have two or three people coming armed, weaponized with their data, going back to, I want to prove my point with data, talking across each other and arguing each other. You can sit down with a common sheet of paper, a common set of data elements and get to, is it really a problem or not?

And then the most important thing, what the heck are we going to do about it? Rather than trying to protect yourself by arguing that it’s not a problem, but that data’s wrong… And they’re hard. Both of those take a lot of work. And I know on top of COVID, that’s one of the many reasons that people are tired of Splunk, is because we were constantly asking for narratives, but it forced us clear thinking, I think, and clear discussion.

Daniel Newman: Stories through data. By the way, I like that. I think the first thing about making sure your points are the same and writing it down, how much fun could we have trying to determine if there’s any ubiquity between our definitions of cloud? I just think that could be just a ton of fun. Because I think what you’re really doing, and maybe not on purpose, but you’re actually creating a level of commonality and common ground. Because so often, let’s face it, technical terms are thrown out that people really don’t understand what they’re saying. Like now, geez, like with containers, things like that, people are like, “Oh, we’re seeing the hybrid cloud architecture being driven by containerizing all the workload applications.” And you’re like, “Do you really understand?”

Because, I mean, even myself being a tech guy, I had to actually work inside of a cloud environment, spend some time playing with this to really understand what someone meant when they meant deploying workloads and putting microservices and applications in containers and moving them between two different clouds. There’s so many people that just use words and they’ve no idea what they mean. So I use cloud as an example, because you did, but there’s so many instances where people are actually not operating out of the same playbook.

Doug Merritt: I used cloud on purpose because of that reason. It’s like, oh, everyone knows what cloud is. Well, not really. There’s so many different variants and definitions, and you can use cloud for ease of use, you can use it for automation, as a backup, all the different benefits that we think that we’re getting from cloud, let’s be super clear on what we mean. And then for our own cloud strategy, what have we prioritized? What are we working on first, second and third, and why? What customer value and benefit do you think it gives? And then what data actually substantiates that that is the right definition, that those are the right prioritized items, and that we can validate that they’re actually having the impact that we think they’re going to have. That’s just, when you really investigate the English language, everyone that’s learning it will say, it’s very complex because terms are overloaded, they mean many different things. And I think that that extra time to define, winds up becoming important, which is the same with the whole data strategy.

Daniel Newman: One of my favorite words is double entendre.

Doug Merritt: Yes.

Daniel Newman: Anyway, let me take this home. I have one last question, just a little perspective here, but from your experience, from what you’re seeing, you’re hearing, talking to executives from clients, and across the industry, let’s talk about not only making data actionable, but taking action with data. How are you seeing this happen in really material and meaningful ways these days, Doug?

Doug Merritt: From a technical landscape, we really focus on that, because as machine learning progresses and as the volumes of data and the potential correlations and insights and data increase, there’s a lot of automation that can be thrown at the problems, so that humans can continue to focus on the highly beneficial creative, insightful, inspirational pieces that our brands are so effective at. In addition to that though, so the technical definition is, let’s turn that insight into an automated routine, so it can drive action right away. I think going back to some of these principles and philosophy is a human element that’s also incredibly important to compliment, that orchestration automation element is, the teams must be empowered. If you’re going to spend all this time to bring together petabytes, exabytes, zettabytes of data that people can play with, and stripe in different ways to get insights, you’ve got to also have this understanding that the people that are closest to that decision, to that data, need to be able to act rapidly.

There should be checks and balances for very risky one way door decisions. But for less risky two way door decisions, empower the teams who are closest to the problem, closest to the data, closest to the insight, to be able to take very rapid decision-making on their own. And then again, through automation frameworks and through ML and through correlations, you can double back and check. They made a decision.

They acted quickly. I empowered them to do that. Was it effective or not? Give them the feedback, give management the feedback. But the more that we can invert to the decision-making pyramid, I think the better. There should be very few decisions that ever make it to my desk.

They’d better be really cataclysmic one way door, high risk decisions. Otherwise I’m not doing my job as far as pushing down authority and decision-making to people that probably know their specific area much better than I do. At least, I certainly hope they do.

Daniel Newman: That’s terrific insights, Doug. For everyone out there, wow, what a bunch of content here to unpack. I think you took the five steps and we went a lot deeper, covered a lot more ground. Doug Merritt, thank you so much for joining me again here on the Futurum Tech Podcast.

Doug Merritt: Well, thanks, Daniel, always a pleasure. Hopefully there’ll be many more of these in the future.

Daniel Newman: Yeah. We’d love to have you back. So for everybody out there, if you listen to the show and you enjoyed this Futurum Tech Podcast interview with Doug Merritt, hit that subscribe button, check out the show notes, we’ve got some links, including a link to Doug’s piece, The Five Steps For a Data-Driven Future, on Splunk’s website. A lot to learn there. A lot for all of us to learn. We can keep doing more.

We can keep doing better. As we come back from everything that’s going on in the world, data will be one of the big things that will power us into the future. But for the future, right now, I got to go, Doug’s got to go. But thanks, everyone, for joining us today. We’ll see you all later. Bye bye now.

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.


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