Jennifer Belissent works for the market research firm Forrester Research as an analyst focused on the data economy. Her research and analysis have appeared in media outlets such as the Wall Street Journal, Time, Computer Weekly, and CIO Magazine. She also has industry experience having worked 8 years at Sun Microsystems. On this episode, Jennifer dives deep into the data economy. She talks about where the industry goes from here, the evolution of data cloud solutions, data commercialization, and much more.
Jennifer Belissent works for the market research firm Forrester Research as an analyst focused on the data economy. Her research and analysis have appeared in media outlets such as The Wall Street Journal, Time, Computer Weekly, and CIO Magazine. She also has industry experience having worked 8 years at Sun Microsystems.
In this episode, Jennifer dives deep into the data economy. She talks about where the industry goes from here, the evolution of data cloud solutions, data commercialization, and much more.
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[00:00:00] Stephen Hamm: [00:00:00] Jennifer, great to meet you online. Great to talk to you and, uh, . so earlier in your career, you focused on politics and economics of Eastern Europe. Then you spent some time in the Central African Republic, then you shifted gears and moved into the tech industry.
So how do those themes intersect or do they.
Jennifer Belisse: [00:00:21] yes, I was a math teacher in the Central African Republic. I was a peace Corps volunteer. And then as an urban policy analyst with, it was with the urban Institute working on projects for the world bank and USAID and Russia and Eastern Europe. And of course that does seem pretty far from the tech industry.
but actually they, they do intersect. but the honest answer is that I moved from Moscow to California and did my PhD in political science at Stanford. And so the real, the intersection is that I was in the Silicon Valley when the tech industry was just booming. so after I finished my PhD, I decided to leave academia and going to tech and spent quite a few years at sun Microsystems, doing some software marketing, doing some business [00:01:00] development.
But then when I joined Forrester, I was able to actually bring a lot of things together. I started doing research on smart cities, and really I'd come full circle. I was doing economics and policy and politics and urban policy and technology. , so then at some point , I was looking at open data, which is something that was a big deal with smart cities and government for awhile, and started looking at how companies were using open data to really drive their businesses and create new businesses using that data.
so that's really kind of how I evolved from. you know, that earlier work in economic development and politics and, and began to really look at what has now become an, you know, exploded into the data economy.
Stephen Hamm: [00:01:44] Well, a lot of this really gets back to systems thinking in a way, understanding that a lot of different domains obviously intersect with each other in all sorts of ways, all sorts of complicated ways. And, and, and they don't just intersect. They affect [00:02:00] each other in all sorts of ways, some of which are unpredictable.
So I think the fact that you kind of bring it, you look at this from a really high level, I think is really helpful as it could be helpful today in our discussion. you know, there are lots of buzz words in the tech and data world, and I think it would be really helpful if right at the top here, you'd provide your definitions of some of the key ones and the two ones I'd like to hear from you about our data economy and the data driven organization.
Jennifer Belisse: [00:02:28] Absolutely. So I mentioned the data economy, the way I would really define the data economy is, and we're increasingly seeing companies enter, it is the ability to use data and to drive value from data. And a lot of companies today kind of give lip service to that. They look at their data, they, they have a lot of it.
they immediately jumped to what the value, you know, what is the value of that data? and I actually provocatively say to a lot of our clients, I tell them that their data is worth nothing. And [00:03:00] then I pause. and I like to refer to a quote by Thomas Edison, the value of an idea lies in the using of it.
and that's really the bottom line. That's what the data economy is, is deriving value from that data. so data in that case is like those ideas. It's really not valuable unless you start to put it to use. and that's when you get to the data driven organization or as we refer to them at Forester.
Insights driven organizations, because it's not about the data itself. It's about the insights that you derive from the data and the value that you bring to the organization.
it's not about being data-driven, but it's really about being insights driven or, or even outcome oriented. you know, insights driven organizations are those who systematically use their data. And they, they create using that data to create differentiation and competitive advantage.
And as we see it at Forester, there are really four pillars that support that organization. we [00:04:00] see them investing in their people, their processes, their technology. And of course their data. So we start with people for a reason. those are the ones who will be driving the organization. So in many ways, the most important investments on this front are our leadership, and, and, and literacy, which I'd love to talk about as well.
Stephen Hamm: [00:04:22] Now you talked about leadership. Many companies have hired or appointed chief data officers in your view. What's the best way for a chief data officer to operate within the Csuite and more broadly within the organization.
Jennifer Belisse: [00:04:36] So we've seen a really interesting evolution of the chief data officer over the last few years when over having hovered at about 50% of organizations that had chief data officers, You know, over the past few years, in, in 2000, 1950 8% of our survey, respondents told us that they had appointed a chief data officer and, uh, and another 26% said that they were [00:05:00] planning to do so.
So we've seen a really massive uptake in appointing leadership to really , being insights driven or data driven. One of the interesting things that we've seen recently in addition to the number is where they're reporting in the organization. early on, we saw a lot of CDOs chief data officers reporting into the CIO.
So having more of a technology focus, Nowadays we in the, in these 2019 survey, 38% of the CDOs that we spoke to, reported to the CEO, so reported right up into the leadership of the organization. And interestingly, that really reflects the change in the mandate that we've seen of CDOs there. They used to be more focused on.
Data management data governance, kind of more of the infrastructure around the data and getting the data house in order if you will. But nowadays we see much more of a [00:06:00] focus on driving the use of data. And driving value from the data. So that's really an, an interesting shift that we've seen. and so having them report into the CEO and having them really have a, more of a business mandate reinforces that, that notion of using the data to derive value.
Stephen Hamm: [00:06:20] It's interesting that you talk about the, the change in the reporting structure. I think about it, you know, and you know, in lots of companies, the, it, people report to the. CFO, the chief financial officer kind of suggesting, Oh, this is all about money and saving money and efficiency. And maybe automation is that changing as well?
That reporting structure.
Jennifer Belisse: [00:06:43] , so as, as technology has taken on a more strategic role in an organization, it's no longer just about keeping the lights on and keeping the systems running it's really about driving the business. And so as we've seen that shift, particularly as we've seen [00:07:00] digital transformation, we've seen the technology leaders report directly into the, CEO.
so similarly with the CDO, as we've seen the orientation become much more business oriented, much more strategic to the organization. You know, whether we're talking about technology or whether we're talking about data, or even whether we're talking about human resources, in the past human resources
as a function didn't necessarily report into the CEO. it was something that also reported into a CFO or a chief operating officer. but likewise we're, you know, we're, we're anything that is really a, a strategic pillar in the organization. It's not just about cost and keeping the organization, you know, keeping the lights on it's about.
Driving the strategy of the, of the company as a whole. and I really think we saw that evolution with the CIO probably starting 20 years ago. And now we're seeing a similar evolution with, with the chief data officers as well.
Stephen Hamm: [00:07:59] Now you've been talking [00:08:00] about leadership, but you know, when you're talking about a data driven or insights driven organization that involves everybody, you write a lot about data literacy. Why is that so important in an organization and who needs to become data literate?
Jennifer Belisse: [00:08:16] literacy applies to everyone in the organization. and that's one of the things that I've been a little bit frustrated with. Sometimes you talk to people about data literacy and they think you're focused on just, you know, Talking to the data scientists or talking to the business analysts and increasing, you know, those who are already data savvy.
but one of the, one of the things that we've found is that it's really important to focus on all employees. and it's, it's an interesting story. When, as part of our data literacy research, we asked, one of Forrester's online panels, a few questions, and we were really looking at, we thought we were looking for their preferences about how they wanted to be trained to better use data.
so we, we asked three questions. We asked, do you work with data? Are you comfortable [00:09:00] with data? And then we asked, what type of training would you like? And really, I thought question three was going to be the most important, you know, obviously, you know, how would you like your training to be delivered?
But it was actually one and two that were the most interesting. when we asked you work with data, we got answers like, and I'll just read one to you. we don't really analyze data where I work. We don't calculate or do any aggregations of data. We will create spreadsheets on various subjects.
It's more way of keeping track of issues. It isn't data calculations. So, you know, hearing that was a wake up call, all that. People don't really recognize that they're working with data. They don't really know what data is today. and so that really, drove some of the work that we did around later data literacy.
And we, we created a framework where data literacy starts with awareness. It starts with just this basic understanding of what is data, how is it used in my organization? [00:10:00] And what's my role with it.
Stephen Hamm: [00:10:03] So, do you think that particularly any employee and an organization should be using data? I mean, either through a dashboard like Tableau or even. AI like ThoughtSpot and data robot and some of these others, you know, they claim that their tools could be used by anybody in the organization.
I mean, is it going to be that prevalent?
Jennifer Belisse: [00:10:28] So I might be somewhat controversial here. you know, people talk about data democratization and I actually often kind of provocatively again, say I don't believe in democracy. , but that doesn't mean that, that. That there are people who don't touch data I do believe that everybody needs to be data literate in to a certain extent.
and let me tell, tell a quick story about it. it was talking to the chief digital officer of Sedexo, which is a food service management company. And she was telling me that when they looked at some of the data at one of their food sites, one of the [00:11:00] cafeterias. They started seeing really odd things.
over time they started selling more breakfast sausage in their cafeteria in the mornings, to the extent, to which at some point people were not buying anything else. So, and this was a restaurant, this was in France, so they weren't buying croissants and they weren't buying coffee or whatever.
And it turns out what happened was they'd switched their cash registers. and they had preprogrammed registers where people just press a single button and that button was capturing obviously capturing what was sold, capturing inventory. but the closest button to total was breakfast sausage. And so if we had asked this cashier, in our.
Survey. If they worked with data, they would probably have said no, but in fact, they did, they were pressing a button for the transaction that was capturing inventory. So down the line, that button that they were pressing was telling them that everybody was purchasing breakfast sausage.
and so we've heard a number of stories, similar stories, that illustrate that. [00:12:00] If everyone at the organization doesn't understand that they have a role in either capturing or protecting or potentially using data. there's going to be an issue. And so, you know, that data quality relies on the awareness of what is data and recognizing data.
Stephen Hamm: [00:12:16] I want to make sure I understood that example. It was the issue was that the cashiers were pressing the button, just picking a button that was easy to press.
Jennifer Belisse: [00:12:25] Make it a button.
Stephen Hamm: [00:12:26] And that was sending a false signal.
Jennifer Belisse: [00:12:28] Exactly. Exactly. They didn't realize that they were actually capturing data.
Stephen Hamm: [00:12:35] My guess was going to be that maybe there was marijuana in the breakfast sausage or something like that, but now it's much simpler than that.
Jennifer Belisse: [00:12:42] Right. and it's, certainly not malicious in any way. , it's that they're just. Weren't necessarily thinking that somebody was going to be using that data later down the line. So when we talk about data literacy, we've created a framework where we start with awareness.
and then we focus on also [00:13:00] on decision makers, who they themselves might not be using Alteryx or a data robot. but we're expecting them to make decisions based on, on data and insights. But yet we're not necessarily teaching them how to do that. So that's another element of data literacy that I think that people don't focus enough on.
and then of course there is an aspect of data literacy that does focus on the, you know, the experts. so we tried to create a, a pyramid that starts with creating that awareness as a foundation and then working on comprehension around data in the middle, and then looking at the experts.
Stephen Hamm: [00:13:38] when I think about different roles and companies, I mean, I certainly think in retail, I mean, assuming retail comes back and we have brick and mortar retail, again. , the companies that succeed are going to win with a superior experience for the, for people to come in. And it seems to me that clerks, in many cases, we really [00:14:00] need to do some kind of analytics.
They may not think about it as analytics. They may just ask a question, but. The, to get them an answer may involve analytics. I think that's going to be a big, big deal. And maybe even like in places like warehouses and things like that, where, you know, having knowledge at your fingertips could be very important
Jennifer Belisse: [00:14:20] absolutely. I mean, there are a couple of things, a lot of retail clerks or , salespeople in shops are now expected to use a lot of different applications for checking inventory for, for. Getting information on a particular product. but we also need to keep in mind that that digital literacy is not the same thing as data literacy and digital literacy means that we can navigate an app.
And, you know, we live our lives on our smart smartphones and we know how to use, you know, a variety of different tools, but it doesn't necessarily, mean that we really understand the data, the statistical or analytical concepts of data.
Stephen Hamm: [00:14:57] Yeah. Automation can [00:15:00] congeal somebody from that complexity or something like that.
Jennifer Belisse: [00:15:03] if it can, but on the other hand, if we're expecting somebody to make a decision based on a findings or insights that an algorithm delivers to us, we can't necessarily expect them just to take it at face value. We need to be teaching them the underlying logic of, of how that, that answer has been derived.
And that's one of the challenges I see with, insights driven or data driven decision making is , we keep telling people that they need to make decisions based on data. But I don't think we're giving them enough of that underlying logic. , we've been asking our survey respondents for years.
What percentage of decisions in their organization are made based on quantitative information as opposed to gut experience or opinion? That's one of the questions we ask. and it has not, it has stayed relatively the same over the last five years. It's now just [00:16:00] under 50%, five years ago, it was about 43%.
Now I think it's actually 48. Yeah. So it has increased a little, but not significantly. And with all of the, you know, the kind of the hype you hear around data and insights and analytics and AI I would have expected more more decision makers to embrace data driven decision decision making, and they haven't.
Stephen Hamm: [00:16:23] So do you think this signals that companies just haven't been able to penetrate with the, ideas, the data literacy, the, the tools into the organization I mean, is there, this sounds like a bad result for a lot of investment, right?
Jennifer Belisse: [00:16:40] I think that we've been, you know, like I said, insights driven organizations require people, process technology and data. And I think that we've focused on the technology and data side of things. Yeah. To the detriment of the people in process side of things. and that's, it's not to say that they can't get there, but, you know, with all of this investment, I think it's time to take [00:17:00] stock and say, okay, now how can we drive the use of this?
and it's really about investing in the people. and they, they get it. but they need to need to be given the tools and by tools. I don't mean another, you know, not necessarily, another software package. Exactly. Exactly.
Stephen Hamm: [00:17:20] I want to ask you a big trend question and it's one that this podcast is fond of. Do you see a lot of data moving to the cloud and assuming you do, what's behind the migration and what are the advantages of doing so.
Jennifer Belisse: [00:17:36] we do see a lot of data moving to the cloud in our data. Infrastructure experts they're telling us that there's a strong evolution towards the cloud. Although evolution doesn't necessarily mean. You know that it's a final state, it's evolving, it's happening. It takes a long time.
So, you know, just as we've seen certain, you know, certain apps and workloads move to the, to the cloud, some [00:18:00] happen more quickly than others, you know, that's what we're seeing and we'll expect to see with data. So it was some data particularly that which will be shared or sold. I say that because people often don't like the term sold.
you know, that that data will move more quickly. Others might take more time. and, and in our surveys, we are seeing that, you know, quite a big percentage of data is moving into, yeah. Cloud it's being it's coming from cloud apps and it's being stored in cloud databases.
Stephen Hamm: [00:18:30] And do you see that happening mainly in like large enterprises or small and medium businesses also doing this kind of thing?
Jennifer Belisse: [00:18:39] I don't have the specific figures on how it breaks down, but I see it happening in perhaps different degrees, but across all sizes of organizations. you might think it's a big organization thing, but smaller organizations might have less of their own on-premise infrastructure.
or they might be natively cloud. No, [00:19:00] they've built up their infrastructure by taking advantage of, of public cloud.
Stephen Hamm: [00:19:04] So, uh, a lot of companies seem to be opting to have a hybrid cloud strategy, you know, moving some of their data and computing in the cloud, but keeping some in their own data centers.
Do you think that's a smart strategy overall?
Jennifer Belisse: [00:19:19] Smarter not it's a strategy that we're definitely seeing. So 75% of our survey respondents tell us that they described their cloud strategy as hybrid. , obviously doing that allows them to take advantage of, you know, The capacity, the extra capacity that they might be able to get from cloud.
it allows them to differentiate between, you know, things that they want to keep on premise for whatever reason or that they want to migrate later. so you know, there variety of different reasons to why things might be In one place versus another. but we're definitely seeing that happen.
Stephen Hamm: [00:19:56] , I want to talk about sharing for a minute here. one of the challenges [00:20:00] in business today is the fact that organizations have data trapped within departments or business units in silos. It does seem like there's more interest in sharing data, either within an organization or between organizations, what is driving the data sharing trend and what technologies are enabling it.
Jennifer Belisse: [00:20:21] So what's driving the data sharing trend is the recognition that. External data, whether it's external to your specific business unit and requires you to find data from another business unit within the same company, or whether it's outside of the company altogether. but recognition that, that additional data sources, external data, Deliver differentiation can give you lift in a particular model, can improve your ear, the predictability of, , what you're trying to predict. and so it's that recognition that, that additional data, Can deliver value and we're increasingly seeing companies recognize that. [00:21:00]
and so, you know, technologies that enable that, well, one is the cloud themselves. It itself having data that's more easily accessible. that's one, new marketplaces that, that make it easier to find, to discover and, and access that data. That's a, that's another, obviously new capabilities like.
API is, or , easier integration. those are basic, but those types of technologies make it much easier, to share the data.
Stephen Hamm: [00:21:29] You mentioned marketplaces data marketplaces, and I'm sure you're familiar with snowflakes, public and private data exchanges. What role do you think data marketplaces will play in the next era of business?
Jennifer Belisse: [00:21:43] So I'll be honest. I've had mixed feelings about data marketplaces. There there's been an explosion of them, you know, they've popped up like mushrooms after the rain. and, and VC has poured into them. Some won't make it, some of the smaller marketplace marketplaces won't make it. others [00:22:00] definitely will, but data's not a commodity, you know, it's not like this field of dreams.
If we build it, they will come. And so some of those smaller marketplaces that are, are, are just aggregating data. Like I said, some of those might not make it, there will be consolidation. what the important aspect. And I think that this is an advantage that snowflake definitely has is that, where there's data that's already, where people are and where other data is, and that that market place can make discovery and access to that data much easier.
That's going to really drive that data sharing and the, and the commercialization. and so we're going to see a big difference in, the marketplaces that are out there today.
Stephen Hamm: [00:22:44] one of the key things , is bringing data in that's not from your organization, from other organizations and some people call this.
Alternative data, alternative data sources. So I'm wondering if you could kind of go down and take us through that. [00:23:00] what kinds of alternative sources are people bringing in? How are they matching that up with data? They produced themselves. And what kind of new results are they getting that they couldn't get before?
Jennifer Belisse: [00:23:11] Absolutely. we're seeing a significant uptick in interest in the use of external data. I'm actually enforced your surveys. 56% of our respondents tell us that their organizations prioritize being. Able to better leverage external data.
another 26% tell us , that they planning on doing that in the near future. so really just a very, very significant interest in external data sources. . Yes. Some of that is , all data. There's no real formal definition of it, but really it's, it's anything that comes from a nontraditional data source.
and so, you know, in the olden days we might have referred to it as proxy data. It's something that you can use to replace some data that you don't have, or you don't know. , we've seen, for example, , you want to know [00:24:00] how a business is doing, and you don't necessarily have their sales figures. Well, people are now using satellite data to look at how many cars are in their parking lots. so satellite data is one other kinds of, IOT data or data that like you said, That comes from outside of their organization.
So we're seeing know there historically, there was a lot of interest in this alt data within the financial markets. So hedge funds used a lot of alt data to predict how a stock was going to move. but now we're seeing companies increasingly interested in that. So , one interesting is , the telecom operators are, taking their data to market.
You can look at where there's a lot of. traffic from mobile phone. So you can look real estate developers, know which corner in a city is most interesting from a placement perspective, you know where to put a store because they can see where their concentrations of people. where do people going or cities [00:25:00] are using that data to understand traffic patterns, and know where they might want to put a bus route or some other type of transportation route.
So those are, those are examples.
Stephen Hamm: [00:25:10] And , there's other things like whether, anything that could have an impact on, on what you're doing or suggest causal relationship, those kinds of things.
Jennifer Belisse: [00:25:18] Yeah, whether it's a great one or just local events, you know, people want to know what the demand for something might be. You know, what, what type of product to stock in a story, you look at the weather, you know, today it's going to rain. You want umbrellas, you know, it's going to be really hot. You're going to stop flip the stock.
Flip-flops you know, how much ice cream are you going to sell that? Yeah, it depends on the weather. there's a, you know, what, what the local news was or whether there's going to be a concert locally. So all of those types of, data and that's increasingly available, and anyone who's got a, some sort of sensor, I mean, this is one of the, we talked about marketplaces.
but one of the advantages of marketplaces is obviously buyers can go and find, data that [00:26:00] they might not have had access to. But the sellers of data, those who have sensor data that they're looking for to commercialize our offer to others, they can make that data available through a marketplace.
Stephen Hamm: [00:26:12] People have been talking about IOT for about 20 years, but now we really have a lot of it installed and there's just a, a flood of data pouring from those devices into servers, the cloud and things like that.
Jennifer Belisse: [00:26:26] Yeah,
Stephen Hamm: [00:26:26] So it's just a, it's a data rich environment, isn't it?
Jennifer Belisse: [00:26:29] Absolutely. and I think that, you know, it was kind of slow to take off. People talked about IOT a lot of time, , there was a long period of time where, we hadn't really seen the promise of it because we were still investing in instrumenting things, and making things interconnected.
but. The next step is to really make them intelligent. And so to be intelligent, you have to have access to that data, but know how to use it and really put it to use.
Stephen Hamm: [00:26:58] You know, for years I've been hearing [00:27:00] people talk about data monetization, you know, the idea that, but companies that really aren't in the data business will discover that within their organizations, they are producing data that would be valuable to others and they can sell it. Do you actually see that happening?
Jennifer Belisse: [00:27:18] Absolutely. So it's interesting. I actually differentiate between what I call , data monetization and data commercialization. . And the way I differentiate is this. So companies use their data internally to better understand their customers or better understand their operations in order to improve them, improve their customer experience, identifying next best offers those kinds of things, or streamlined processes that is deriving value.
That's monetizing the data, and. then they often realize that something that they're doing internally or the data that they're, that they have generated could be, and others could derive benefits from it as well. And [00:28:00] so they take that data to market in some fashion. And that is what I refer to as data commercialization.
And we've seen a massive uptick in that when we first started to look at this probably five or six years ago, about 10% of companies told us that they were doing it. now over half of companies tell us that they are, selling or sharing their data for revenue. So they're selling it, essentially.
Some people don't like the term, you know, selling, they say sharing for compensation, but.
Stephen Hamm: [00:28:30] I'm really surprised to hear this. Can you give an example of a company that's done that?
Jennifer Belisse: [00:28:35] Absolutely. So there are a couple, take GE aviation, for example, they create.
commercial airplane engines. And they've been, they've got sensors all over the engines that are capturing a lot of different data now. And this is an interesting, you know, the example that illustrates the monetization versus commercialization.
So they've been using that data to, to improve the products themselves. So understanding, [00:29:00] the, the feedback from the various. You know, pieces of the data, to improve the performance of their engines. But then they realized that actually the airlines can use that data as well. So they can use it to better understand fuel efficiency, which might give them indications of how fast they should fly or at what altitude they should fly, or, you know, when they needed certain types of maintenance.
And so they've started structuring, Data packages or products and it's not just the data, but the insights from the data. and they're offering that to their, to their customers. So it's like saying, do you want fries with that here? You know, here have an engine and would you like some insights on top of it so that you can optimize your flight paths or, better schedule maintenance and reduce downtime.
and so that's a classic example of a company that was, you know, they started with that monetization exercise using that data internally to improve their R and D and improve their product offerings. And then they [00:30:00] commercialized it with data products and services that they, that they sold to their customers.
Stephen Hamm: [00:30:05] that's a great example. earlier on you talked about the four pillars, you know, the, the keys to becoming an insights driven organization and they were, they were people, processes, technology and data. And then you, you talked a little bit about how kind of adoption of the technology of the approach to doing business has been relatively slow in some cases, a question for you.
So here we are , in the COVID 19 era. And in some ways some businesses have kind of slowed down or reset. Do you think there's kind of a pause moment that people are taking advantage of to kind of rethink well, is there, are there ways we could be operating better? you know, when you have a crisis, if sometimes Claire clarifies thought, do you see that happening in companies now?
Jennifer Belisse: [00:30:57] . So I mean, companies that are going to come [00:31:00] out of this, they know that they need to do things differently. for starters, we've seen companies that, you know, they, they know that they need to be more digital. , I mean, take grocery, for example, you.
There was, you know, there's been online grocery shopping for a while, but it's not that prolific. and one of the things that we've seen in this pandemic period, is that there's a lot more demand for online grocery shopping. and, and maybe in certain markets in the U S that's, it's really common.
I, I live in Europe. It hasn't been that common. but. even with my local grocery store, you know, it's been hard to get the window, you know, it's hard to get a time slot to get groceries delivered because there's so much demand for it. And that really , it's driving, e-commerce, it's driving digital transformation and so companies are going to be increased, you know, accelerating some of those shifts that yes, we've seen them happening, but the, the need to do that is much more urgent now.
and for [00:32:00] in many cases, there's not going to be a turning back. The other, the other thing that's going to be interesting is, you know, companies focus a lot on customer insights and really understanding their customers and knowing when to offer a complimentary product, what's the next best offer?
How do I improve that customer experience? and this pandemic period has been, Really a massive exogenous shock. It's really changed the way customers are, are acting and reacting to things. and some of those historical patterns that we've relied on to do that prediction and to do that forecasting and to understand what those, those offers might be.
that's changed. So there are a couple companies that are gonna look for a lot more real time data. they're going to look for new data sources. They're going to look for new ways of understanding their customers because those customers are not necessarily the same customers that they were.
and I don't mean [00:33:00] different people. I mean, really they've changed, you know, then they were maybe three or four months ago. So I think that there's going to be, a real Renaissance in the way that we approach being insights driven and much more of an urgency.
Stephen Hamm: [00:33:12] You know, it's interesting. We were talking about COVID-19 and not crisis. I actually think there are three crises going on at once. And one is COVID-19 another immediate crisis, especially in the United States is race and inequity
Jennifer Belisse: [00:33:28] Huh.
Stephen Hamm: [00:33:28] and then even more broadly. But I think on a lot of people's radar right now is climate change.
it's almost like you've got to keep all three of these things in your head right now, different people with different focuses are going to be focusing on different of them. But in fact, we've got. You know, an array of crises. And you know, when I look forward over the next few years, I think there may be massive changes coming, when you look ahead how do you see data and data analytics impacting business [00:34:00] and society and the economy.
Jennifer Belisse: [00:34:03] So a couple of things. I think that the, just living through the pandemic and at the, the exposure to, The data and the stats about the pandemic, you know, how many cases are there? How many active cases, how many critical cases, you know, death rates and numbers of ICU beds for millions and all of that kind of data that people are hearing about all the time.
I think that that's going to. Res the awareness of the need for data literacy is one. because we've seen so many misinterpretations of the data, we've seen people manipulate data. We've seen people try to present data, , in different ways. so I think that that's, that's one of the changes that we'll see is, is really the increased, Awareness of, of the data that's around us, hopefully that awareness will, lead us to raise the level of, of, of [00:35:00] understanding of what data is and how to use it.
but I also think that, you know, just in terms of these large trends, I think this period has given us pause, and allows us to, reflect more on some of these. other crises as you call them. And so I think that we're going to see, maybe it catharsis where all of this change comes together.
I don't think that we will. I mean, personally, obviously I don't think that we'll return to quote unquote normal. I think there will be a new normal, in a where it falls out. I don't know. But I don't think that it will. I mean, I think that we're, we're in for a significant period of change. Oh, over the next few likely few years, you know, what Forrester w we look at and forecast, you know, changes in the, in the global economy and how that impacts technology and technology spending, and our scenarios.
You know, [00:36:00] don't bode so well for this year. and, and some of them extend out even into next year as well. So as we're all kind of feeling the effects of, of, of this current crisis and, and experiencing the ongoing nature of some of these other crises. I actually don't live in the U S although I'll admit that what's happening in the us right now is something that's being observed by the world.
and, but, but I, I, so I think that, you know, and, and, and even seeing what's going on in the U S allows everybody else to reflect on, you know, Similar situations that may, you know, may exist in, in, in their countries as well. but I do think that we're in for a extended period of, of change.
I don't want to say unrest because I, I would like to pitch it as a more positive. I see opportunity in this for everyone.
Stephen Hamm: [00:36:51] I hope you're right. You know, one thing that I observe it, I think, you know, in parts of the world and in parts of society over [00:37:00] the past few years, so it was kind of a devaluation. Of facts and knowledge. Do you think that covered, and maybe some of these other things maybe especially triggered by covet, do you think that you think that facts, expertise, and knowledge are going to become, kind of have a resurgence in the popular mind?
Jennifer Belisse: [00:37:23] I would like to think so. although I'm not a hundred percent optimistic, I think that there, I think that there's still too many people who are, are wary of facts and how they're delivered to them. And I think we need to focus on education. And I think that in many places, there, there hasn't been enough focus on an investment in education.
and you know, I'm, I'm going to beat my data literacy drum, but, but I think that a big part of that education is starting with, you know, STEM, [00:38:00] math, data, data literacy at, at very early ages. and starting very basic. One of the things that, you know, just to go back to the beginning of the conversation where we talked about me living in Africa, you know, I worked on some literacy programs.
when I was in Africa and you, you don't start by handing somebody a book, you actually start by handing them pictures. because one of the things that was recognized in teaching literacy to people who haven't had a lot of exposure to print material is just seeing a picture of a tree. you have to explain that that abstraction.
Represents a tree because actually what we think of as a tree, you know, the pictures that we see in children's books of the kind of, you know, green, cloudy thing with a Brown stick on the bottom, it doesn't really look like the actual trees that you see outside. and so I think we need to really start at a basic level, we can't present [00:39:00] somebody with facts or data or insights and expect them to.
To accept and understand it just like you can't hand somebody a book when, when they, they don't really know the alphabet or they don't understand that the level of abstraction that's required to start from letters and create words and sentences and tell a whole story in written material.
and I think that elevating the, the value of facts and having a fact based society, is going to require a lot more education and, and, back to the basics in terms of data literacy.
Stephen Hamm: [00:39:37] Well, Jennifer, I want to thank you so much for your time today.
Jennifer Belisse: [00:39:40] Well, thank you. I really enjoyed it.