PODCAST: COVID 19 | Redefining Digital Enterprises
Episode 2: How Data & Analytics Can Help in a Downturn
Listening time: 13 minutes
How Data & Analytics Can Help in a Downturn
In this episode, best-selling author and expert on Infonomics, Doug Laney delves into how enterprises can navigate their way out of the crisis by leveraging data. Despite the downturn in the market, Doug explains that enterprises should focus on data and analytics investments. He emphasizes that companies should shift from a trend-based outlook on analysis to a driver-based one. Now, enterprises need to re-examine their products and service portfolios and identify potential new revenue streams based on their data.
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Aruna: Hi there! You’re listening to AI to Impact, by BRIDGEi2i, a podcast on AI for the digital enterprise. My name is Aruna Babu and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business, technology and user needs. The world we live in today is very different from the world we lived in a few weeks back. So, in our AI to Impact podcast, we’ll now be focusing on conversations with business leaders, digital transformation advisors, as well as AI and analytics thought leaders to discuss the impact of COVID-19 on enterprises, and how enterprises can recalibrate their focus for continuity and resilience.
Today, I’m going to be chatting with Doug Laney, one of the world’s finest thought leaders in the field of data and analytics. A three-time recipient of Gartner’s Annual Thought Leadership award, he’s the originator of the field of Infonomics, which is about evaluating and accounting for data or information as an asset. He’s also the man who coined 3Vs- volume, velocity, and variety now commonly used in defining big data. He’s also been featured in the Humans of Analytics series by BRIDGEi2i Analytics that celebrates renowned analytics leaders across the globe.
Aruna: Hi, Doug. Thank you so much for taking the time to talk to us today.
Doug Laney: My pleasure. Good to be with you, Aruna.
Aruna: So unprecedented times, as we all know, all over the world. And obviously, such times call for truly unprecedented measures. Since you’re one of the top global influencers in the world of data and analytics, we wanted to take a little bit of time today to talk to you and understand how data and analytics can really help in a crisis.To start off, how do you think data can actually help enterprises make better decisions during a crisis like this?
Doug Laney: Good question. You know, you mentioned the concept of this crisis being unprecedented. And it’s certainly a word that we’ve all been seeing and hearing a lot lately. And, you know, it’s a word that’s pretty much intended to encapsulate the enormity of the surprise and the magnitude of the change that the world is undergoing at the moment. But I think it has another implication – the word unprecedented kind of admonishes any people or organizations that are either too comfortable with, you know, our ignorant of history or too intellectually lazy to comprehend how even one event can deterministically lead to another. Or they were kind of too fixated on riding the wave to envision and plan for its eventual crash, which it always does. So every, you know, occurrence and situation has a precedent. This may be an unusual or uncommon time for sure. But, you know, we’ve experienced recorded instances of pandemics and market crashes and so forth, and a recurrence is different. But a lot can be learned by gathering and analyzing the data from them rather than just kind of throwing up our hands in kind of complete ignorance. So, what happens during times like these is that companies get kind of really paralyzed and rather than being paralyzed, you know, there’s kind of an old expression, which is ‘You don’t waste a crisis.’ So, what do experienced or unnerved business leaders tend to do? Will they kind of hunker down or they tighten the belt or they jettison the ballast or worse, they just stay the course because they are basically paralyzed. Crisis – it introduces a set of opportunities and so while it may make sense to push the pause button on particular kinds of capital expenditures, I believe that investments in data and analytics is a great time to accelerate those investments, not to abate them.
So, how do companies handle this kind of crisis? One real challenge that we’re seeing is the focus on forecasting. Let’s talk about forecasting for a moment. Everybody’s very concerned about forecasting. Most companies will forecast their business based on trends. You know, what have we been selling by season, and how has that been changing over time? And really just kind of, as I say, staring at our own navels when it comes to the data that we used to do forecasting. And that’s called trend analysis. And what companies really need to shift to very quickly is a move to more driver-based analysis where they’re looking at external indicators of things that impact their business or leading indicators that impact their business.
That’s very complex to do. There are a lot of external indicators out there. There are billions of websites you can harvest. There are millions of open data sources. There’s data that you could gather from partners and suppliers. There’s thousands of data brokers. So, there’s a lot of data out there and it’s very hard to figure out what data to use. And so that process with curation or identifying which data potentially is a leading indicator and then test those leading indicators. But it’s not a real easy process. It takes a lot of data science, a lot of data curation, a lot of data integration that many companies are not prepared to shift to as quickly as the current crisis demands.
Aruna: Got it. Ok. So since we were talking about forecasting, do you think enterprises are going to look at doing that differently now? Are they going to look at, you know, maybe new business models using data? Do you think that’s something that’s happening already?
Doug Laney: Yeah, there’s a few things. Like I said, moving from that, the trend based to driver-based forecasting and using external data is really big right now to understand, you know, how the virus is moving, how it’s impacting different aspects of the economy. I’m looking at countries that perhaps are ahead of other countries when it comes to dealing with the crisis or starting to normalize economically. So, looking at that is going to be really important. Changing customer behaviors: What are our people buying while they are sequestered at home? How is that shifting? Certain aspects of the economy are going to suffer. Others are naturally going to blossom. And so, understanding that is really, really important and you can’t understand that just by looking at your own data. Now, the other thing that we find a number of companies are doing is looking for new revenue models. So how can they digitize or digitalize existing products and services that traditionally require a kind of a hands-on or manual set of activities?
How do you change that to something that’s more digital and online, yet still kind of mimics the interactivity that people get used to? So, I think companies really need to look at their product and service portfolios to identify what aspects of those products and services can be digitalized or digitized in some way that takes online and doesn’t require a lot of manual intervention like you know restaurants are offering curbside service now. So, the experience of going into a restaurant has been replaced with the experience of ordering online, even paying with your credit card online or by phone, and then driving up and picking up your food or having it delivered. So that kind of digitalization is something that all companies need. But also, in terms of new revenue streams, many companies are starting to look at their data itself as a revenue stream. How can they make their data available to others in return for favorable commercial terms or in return for goods and services or even selling it or licensing it for cash. So we’re doing a lot of work with companies to identify new revenue streams based on their data itself.
Aruna: Aren’t there privacy concerns there?
Doug Laney: For sure especially when it comes to customer data, called PII(Personally Identifiable Information). And obviously, many regulations like the GDPR, the California Privacy Act, and HEPA in certain industries regulate what data you can share externally or even how you can use customer data. But what we’ve come up with is a way of helping companies monetize their customer data through what we call an inverted data monetization model. So, let me explain. Rather than me selling you my customer data, I can sell your products and services to my customers and take a cut of that, you know, of that action. And in doing so, I can monetize my customer data without exposing it externally.
OK, so imagine I’m a hospital that well, hospitals are kind of doing well right now. But let’s say you’re a hospital and you have data on your patients, but obviously, you can’t share that externally. But let’s say I know which patients of mine have, say, diabetes. And I can make products and services from others available to those customers or information is not available to those customers. I can sell them at home, glucose monitoring, kits, or healthy living or exercise plans or things like that that might help them. Yeah. And so that way I’m expanding the ecosystem of my business from just, you know, the existing health care related services that I offer to an entire universe of products and services that are related to healthy living that are targeted at particular kinds of patients. And I can do that without exposing who those patients are at all.
Aruna: Got it. Ok
Doug Laney: In highly regulated industries, it’s a great way to monetize your customer data. So, you know, anyone who says that we can’t monetize our customer data because of regulations is entirely mistaken. They’re just not thinking innovatively or broadly enough.
Aruna: Got it. Ok. So, since you were talking about digitizing or digitalizing a lot of processes, do you think it’s really important for enterprises today to have a certain level of digital maturity to be able to embrace these new business models?
Doug Laney: You know, I don’t know that it requires a level of the enterprise, digital maturity. Many of the use cases that we see where organizations are using data and analytics innovatively are quite vocational. They’re very specific to a particular business function. So you don’t have to create an enterprise-wide strategy or plan to digitize or digitalize a particular service or offering, maybe something that is done within a certain business unit. So it’s great to have a strategy overall. It’s wonderful to have leadership that is encouraging of experiments, that kind of experimentation and innovation. But I don’t think it has to happen necessarily at an enterprise level.
Aruna: Got it. So it’s more of the functional level, you know, a certain level of maturity to function.
Doug Laney: It certainly can.
Aruna: Yes. Yes. Got it. Ok.
Doug Laney: And often involves a greater degree of collaboration with customers and partners than perhaps many companies are used to. When we’re developing these data monetization approaches for our clients, we regularly request the involvement of key customers or suppliers because that data is going to be made available to them, or these digitalized products are going to be perhaps sold through their channels. And so it makes a lot of sense to not do this in a vacuum, but recognize that your customers and partners and suppliers are all part of a big ecosystem that ought to be engaged when considering how to digitize or digitalize your offerings or monetize them.
Aruna: Got it.
Doug Laney: Ok. Yeah. Ok, good question.
Aruna: That was actually a really good conversation Doug. Thank you so much for taking the time. It was a pleasure.
Doug Laney: Thank you. Great to speak with you. Good luck.
Aruna: Well, you, too. Thank you so much for listening to this episode of the AI to Impact podcast. I really enjoyed talking with Doug – one of the world’s top influencers in data analytics. It was a really interesting conversation about how enterprises are now looking at multiple external indicators that have an impact on their business and how the drive towards digitization is not always at the enterprise level. And of course, how the changing landscape today represents a great time to make investments in data and analytics. If you found this interesting, please do share it forward especially as we continue the conversation with other experts and thought leaders about the business impact of COVID-19 on enterprises across industries. Do subscribe if you’d like to stay in the know. Once again, thank you so much for tuning in. I’ll catch you next time. In the meantime, do stay safe. Buh bye.
2020 will long be remembered for the pandemic that wreaked havoc on the global economy and disrupted communities and businesses in unprecedented ways. In our latest podcast series: COVID19 | Redefining Digital Enterprises, we will be interacting with several thought leaders, BRIDGEi2i Business Heads, Domain Experts, and reputed AI and analytics leaders to understand the various challenges emerging out of this crisis and the way forward for enterprises in the new world order. Tune in to know more!
Douglas B Laney, or Doug Laney as he’s called, is a best-selling author of Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage. He’s an expert in data and analytics strategy who advises CXOs and senior business leaders on data strategy, data monetization, data governance, and analytics best practices. Previously, Doug served as Vice President and Distinguished Analyst with Gartner’s Chief Data Officer research and advisory team.