During times of uncertainty, data can be a powerful asset. But without an overall strategy—without bringing big data and small data together—key insights will be missed. For this episode of Connected & Ready, Gemma is joined by Lisa Morgan, a journalist, analyst, and strategist focusing on emerging technologies. They discuss the current state of data, some of the key strategies and principles companies are using today, and the importance of identifying the problem before collecting the data. Learn how Microsoft Power BI empowers employees at every level of an organization to make decisions confidently using up-to-the-minute analytics. Watch a demo now: https://aka.ms/AA8ku9e
Writer Lisa Morgan joins host Gemma Milne for a discussion on how companies are using data and analytics today, why using historical data is becoming increasingly flawed, and how focusing on improvement and adaptation can help companies become more resilient.
About Lisa Morgan
Lisa Morgan is a seasoned journalist, industry analyst, and content strategist who focuses on the business impact of emerging technologies. Lisa's insights are frequently quoted verbatim as the launching point for intellectual discourse about what businesses and IT departments should be doing in the age of AI, cloud, digital, and hyper-competitiveness.
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Music playing [00:00:01]
Gemma [00:00:05] Hello and welcome. You're listening to Connected and Ready an ongoing conversation about innovation, resilience, and our capacity to succeed. Brought to you by Microsoft. I'm Gemma Milne. I'm a technology journalist and author. And I'm going to be exploring trends around how companies are adapting to a disrupted world and preparing for tomorrow. We're going to speak to the innovators who are bringing products, operations, and people together in new ways. In today's episode, I’m chatting with writer and commentator Lisa Morgan, who covers big data business analytics, enterprise software, emerging cultural issues affecting the C Suite and more. We explore the world of analytics and get into why building strategies rooting in historical data is flawed in a constantly changing world.
[00:00:50] We dive into the power of small data and we consider how businesses can adapt their strategies to be more resilient by focusing on continuous improvement and paying attention to change.
[00:01:03] Lisa, thank you so much for coming and joining us on the show. Why don't you start by giving us a little introduction to yourself?
Lisa [00:01:10] My name is Lisa Morgan, and I am a journalist and industry analyst and also content strategist. I develop all sorts of content for various organizations. I've been in the high-tech industry for quite some time and acting as an observer. And I'm loving where everything is going. And I love tech and innovation. So, I'm always trying to stay on the leading edge of emerging technologies and what they really mean to business. And as we know with digital transformation, tech and business are no longer separate. So, it's a really exciting time.
Gemma [00:01:41] So it sounds like you're obviously both excited by but also massively informed in this area. We're obviously today going to be talking about data and analytics. Let's maybe start with a little bit of an overview of where we're at. What are the typical approaches organizations are taking, the various models or strategies that they've been using when it comes to data and analytics?
Lisa [00:02:05] Well, it really depends on their level of maturity because there's different ways that people are actually procuring the tools. They're doing it on a departmental basis. They're starting to work, inter-departmentally and also at an enterprise level. And you see that in the maturity model. And if you're really at the beginning, you're probably getting all of these different analytical packages that are specific to your department. That's all fine and good. But what organizations are realizing is that if you don't have an overall strategy and if you're not connecting these things that you're doing, then you're missing things. For example, you know, to figure out a customer journey, to really understand it, you need to go from beginning to end. And that's going to cross different departments. So, it's really important, I think, ultimately to have an enterprise strategy for that.
Gemma [00:02:54] How would you say sort of the strategies and models have evolved over the last few years? Because obviously technology is changing so fast all the time. And we're hearing about things like artificial intelligence or machine learning, predictive analytics. You know what has been able to improve the business insight organizations can actually uncover and use with all these new technologies? How does that link back to the general worlds of data and analytics?
Lisa [00:03:20] So where we started was with business intelligence and reporting. And you had to go to IT to have them build a report for you. The business moved much quicker than that, so that didn't work out so well. So then we had self-service reporting, which, again, you're kind of constrained by what IT is providing you. And that wasn't really enough. But then as we were able to get data in more real time and use analytics, we were able to have dashboards. And so we could have KPIs that we could look at on a dashboard. And that was great. Except the problem is, is what do you do about it? We've been looking at historical data. We really want the ability to understand the context of where the customer is. What are we analyzing? What are we trying to do? So, we've moved from a historical perspective. We've now added the predictive analytics to it. Of course, it's not a guaranteed future. It's you know, it's a probability. And we've gone from that to prescriptive, which is great if you can kind of skate to the puck. Right. With the predictive analytics. But if you don't know what to do about it, what action to take? How do you optimize all of this stuff, the knowledge that you have and the action that you should take based on that? And now we're seeing augmented analytics and augmented analytics uses AI and machine learning to help people in the enterprise, you know, whether they're data scientists, data analysts, and even citizen data scientists to use analytics in a more intelligent way. And so it's really an assistive technology. And so what that's doing is that's actually allowing us to interact with data even more than the interactive dashboards that we've had. So you can use typed in queries now and it won't be very long before we're doing voice and using that a lot more. There's some of that now, but the problem with it is that it's constrained by the language you have to use. You have to put your query in a form that it understands. Eventually, you won't have to do that. Basically, you can do a stream of consciousness stuff and interact with it and uncover some really exciting things. And one of the things that AI and machine learning also does is it's able to recognize patterns that we humans can't. Either they're too subtle or they're too complex. It's really exciting. So it abstracts up, particularly for the data scientists so they can get to the business question that they really have because they tend to work in lines of business. They’re power users.
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Gemma [00:06:20] Yes. So there's been quite a few phrases you've said and in the answer there, and I'd love to dive into a little bit deeper some new phrases that I think probably quite a lot people might not have heard about before. So, let's get some examples and a bit more information. First of all, this idea of prescriptive analytics. The idea of not just being able to skate to the puck, but also knowing which, I don't know enough about hockey, which I guess is sweep to use in order to score the goal. What does that look like in practice? Is that an algorithm that spits out an action or is that a person that's being able to look at their predictions and understand it and put it to use? Give us a bit of an example of what you mean by prescriptive analytics and practice.
Lisa [00:07:00] What you get to is you get to that action that you want to take and, you know, it can be more automated, for example. Maybe I want to offer a customer in a retail situation an offer. So, understanding what sort of action we need to take. You know, it can also tell you, hey, a recommended action is this. If you're, for example, in IT and you're seeing something that's going off the rails. Well, what's the root cause of that and what should you be doing about it? If you're working a help desk, your hair's on fire all the time. So, you know what? What is it that you need to do right now? And let's just cut to the chase rather than trying to figure out what it is. So, it's helping us actually problem solve. What's really cool about it, too, is its taking the data, too, of the actions that we take and the outcomes that we have and it's using that as more data. So, this really is kind of like a feedback circle. So it learns and it gets more intelligent and you figure out what works, what doesn't work. Like in IT, what was your mean time to remediation? That sort of thing. So you can get better at what you're doing.
Gemma [00:08:06] Amazing. And another one of these, um, phrases that came up was the citizen data analyst, the citizen data scientist. This is a new one for me as well. So, tell me a little bit about what you mean by that and why that's kind of relevant in the current situation.
Lisa [00:08:18] So data scientists are very rare human beings. They understand math and statistics at a level that most of us don't. The real ones, I call them, have advanced degrees. And most people are afraid of math. It's a language that they don't understand and they don't want to understand. What we're doing is we're actually democratizing analytics a bit. And I want to make a distinction because with citizen data scientists, you're talking about power users like those Power Excel users, you know, of yester year. But the difference with this is you can't just have a group of citizen data scientists and not have data scientists. Data scientists should be there to solve the really difficult, esoteric problems that you have, the really strategic problems that you have. On a day to day basis, though, they can't solve every problem in your organization. So, you need a larger army to do that. What I need is tools that speak English and allow me to interact in English. But allow me to get to data and understand data. So, it's nice to have those tools, but you really need to keep it in perspective. I mean, it can all work together, but it's - one is not a substitute for the other. But the exciting thing is that you're able to interact and understand data a little bit more. And I think one of the pieces that people are missing with citizen data scientists is they think that if they hand people abstract tools, that they're automatically going to be able to use them. And really, you need to learn how to think more like an analyst. So, there's some basic training that everybody should have about analytics. Everybody should be data literate. They should understand what words mean. They should understand what the thought process is of interacting with data and thinking more critically about the questions. Because really, with these systems, when you ask questions, you know, it's said if you want a better answer, ask a better question. Well, you have to learn over time. That takes a little bit of experience. If you're trained in data analytics, you know how to approach problems. But if you're just kind of a normal line of business person, you're not used to thinking in this way. You're used to thinking kind of in absolute terms, in simplistic terms, and you need to get a little bit more nuanced. And as you're interacting with these systems, it also helps with training because you're getting experience, you're seeing and experiencing what's working and what isn't and how you can adjust what it is that you're doing as a human to optimize what you're doing. You're going to have people that are better and worse at it.
Gemma [00:10:54] That really resonated with me. I mean, I actually studied maths at university and whenever I tell people that, you normally most the time get response of people being like, oh, that's so difficult. I was so bad at maths at school or math, as you say in the States.
[00:11:06] And from my perspective, I'm always like, it's not so much about learning each and every single thing that you can possibly learn in the curriculum, but rather, as you say a sort of way of thinking or perhaps I don't know, it is almost like trying to get small amounts of foundations that then allow you to build on top. What does that look like, though, in practice in terms of I guess, I mean, you mentioned training. But, you know, is it about finding use cases, perhaps for individuals so that they are empowered themselves to go, oh, here's a problem I have. Now that I know this new way of thinking, I can apply, you know, X, Y, and Z and come up with a new idea or do a new bit of analysis. Or is it about tools that we need from vendors that speak kind of this translated language between the two? What does it look like from sort of an example standpoint in practice?
Lisa [00:11:56] Well, starting with tools is not the right way to start. First of all, you have to understand what is it from a business perspective that you're trying to achieve in the first place? Where is your organization going? And how does your piece of it fit into that? Organizationally, everything should align. And in really well-oiled machines, it does. But what you need to look at is what is the problem, the use case? What is the problem that I'm trying to solve? And why am I trying to solve it? What difference is it going to make? What difference is it going to make to the customer? For example, we scrambled earlier this year to figure out how do we even exist as businesses in this new reality? Some people closed their eyes and thought hopefully it'll just go away in a few weeks. And other people started thinking, well, what is my business position here in this new reality and how do we capitalize on it? Which is why you see some businesses folding and you see other ones that are doing the curbside pickup and the delivery, and they're taking advantage of Uber and all of these different things. You're presented with a problem or a set of circumstances. How do you deal with those? How do you optimize your own processes? Analytics goes into that. Every single department is trying to get more efficient at what they're doing. And so they're working against KPIs. Well so how do we get our processes to be optimized? How do we get our people, our human capital optimized? How do we make that work together? And then these tools really help. But I have to say that I'm a big proponent of an enterprise strategy when it comes to these tools. While there are tools that are very specific to certain rules, I'm really into the empowerment of data analytics of everybody kind of using the same toolset to the degree that they can. And I'll say that Power BI is a good example of a tool that can help you do that. Some of these are enterprise level. And if you look at the Gartner Magic Quadrant, for example, you'll see that there are specialists, there are people or companies that are kind of more leading some or more innovative, but they're not as quite as well as delivering. So, you need to know your vendors. You need to know their capabilities. You really need to understand what is your problem? Why am I trying to solve it? What do we have as a technology infrastructure? So, it's business and technology. How do we get this to work practically in our organization? And then how do we use it? So, I mean, there's several pieces to really get it right.
Gemma [00:14:25] No, but listing out these questions is so helpful for people just even knowing how to begin thinking about this overarching strategy. But you mentioned, of course, there's been such a huge change over the last six months, over the last year. And obviously a lot of what we're talking about with data analytics is looking at previous data and using these feedback loops that you spoke about to inform future behavior. Now, with such a big change and with so much shifting in consumer behavior. How are data on analytics models holding up? You know, are there any examples that you have where perhaps they're not quite living up to what they were before because of such changes? Or are they, you know, robust enough to able to shift with the times as quickly as I hope humans have been able to, too.
Lisa [00:15:09] Well, there's no one answer, because it really depends on how agile is your organization and really how data savvy are you. I can tell you that the more agile organizations and the ones who are more sophisticated when it comes to analytics, were able to use kind of the opportunity that this presented.
[00:15:29] But it also presented a problem, which was for the last several years, everybody's been focused on digital transformation. And what digital transformation is, is a knee-jerk reaction to digital disruption by these unicorns that are coming in and completely disrupting entire industries. And so we've been on that path for quite some time. And we had a cadence going with it. We got it. We understood. We had plans. We had rollout strategies and all of this. And then all of a sudden the pandemic hit and we had these executive mandates where all of a sudden everybody has to work from home and all of a sudden you don't have customers coming into your stores. So the predictive analytics that you had, everybody's predictive models got disrupted because all of a sudden the historic data that you had for 2019 and beyond does not apply. And here's the magic word again, in the context of where we are. Right. So but what do we use? Well. OK. So, there are other crises that we've had. We've had 9/11, we've had the 2008 financial crisis. I mean, we've had the dot bomb happen, you know, in 2001. But none of those are really on point. I mean, is this economic, yes, but it's also health. Well, OK. Spanish flu, that's the closest parallel. But we don't have a lot of data about the Spanish flu like we would have about these other things, because obviously we didn't have computers. We weren't collecting electronic data. And the pattern isn't exactly the same. So, what is it that you do then? So if you can't, like, plug in something and you can use some of these data sources about these things to understand the dynamics, so you're not starting from scratch. But you have to understand that they're informing you, but they're not giving you, quote unquote, an answer. We've been in the era of big data for a while and everybody is thinking about big data. And you need big data to do analytics and you need to have a data lake and, you know, to have AI and machine learning and all of this stuff. Well, I can tell you right now, over the last six months, small data has become very fashionable because now we're collecting new data about a new context and we need to be able to act on that in the moment. So you're using small data about changes in customer behavior, changes in attitudes, changes in economics, changes in the fact that these executive orders are opening or closing all around the world. It's been a very interesting, fascinating, actually journey, but very frustrating also to a lot of organizations, because when the disruption first hit, it's like, oh, my God, what do we do? But then, you know, as you start thinking about it. It's like, OK, how do we problem solve? And that's when you start pulling in more data and start looking at the smaller data. And to the extent that you have streaming data. Great. I mean, you need fast access to data and it needs to be contextual because things are changing so quickly right now. And we're kind of at a point where that's a muscle that we need to be able to exercise going forward. What happened in the past, what's happened in the last six months is all context is going to inform what we do in the future. We don't know. I mean, they say that there may be a second wave. How are we going to deal with that? I think we have a great opportunity now to take our learnings and apply those to a longer-term strategy that's going to serve us better. But we definitely need to be agile and we need to be paying attention to data. And the other thing that matters is your data. I mean, what is the quality of your data? You know, it's garbage in, garbage out. So, and organizations really are struggling with that one, too.
Gemma [00:19:11] For anyone who's possibly a little bit new to data, are new to these newer ideas than the data and analytics world, you mentioned both real time data as well as small data. Are these the same thing?
And if not, what is small data?
Lisa [00:19:25] One has to do with speed and the other has to do with size. So, when we're talking about real time data, like, for example, you and I are sitting together, we're looking at each other and we're communicating in real time. You could argue it's say sink or swim or sleep because we're not both talking at the same time, but we're both in the same space in the same time. So this is real time communication. And it's the same with analytics, too. Do you have access to that data? We're seeing that a lot more with streaming data, for example. So, we're seeing things in the moment. When we're talking about small data, we're talking about the amount of data that we have. We may not be talking about terabytes of data. We may be talking about much smaller pieces of data and just data from the last week with all of these changes that happen. Data from the last day. I mean, from the time the executive mandate hit and now we're a week later, what does that look like? So that's much smaller data.
Then, for example, the data that we had about the same topic over the last, say, five years, 10 years, 20 years.
Gemma [00:20:30] You know, looking at this sort of approach to our small data versus big data, how does that change in terms of collecting data, particularly about customers, in order to kind of understand customer data and make sure you're serving in the best way possible? You know, is about more real time information now, or is it about more frequent analyzing or more frequent collection of new things now as opposed to previous? Like, how do you adapt your, I guess, processes to fit with small data versus big data?
Lisa [00:20:58] So we've been moving towards real time data over the last couple of decades. And so we've had faster access to data. What we need to pay attention to here is not only the speed of the data that we're getting, but we need to pay attention to the signals in the data because the signals in the data are what's changing quickly. And that's really what you need to pay attention to.
Gemma [00:21:22] So you mentioned the importance of noticing signals in the data. I suppose that's the kind of the name of the game when it comes to data analytics, right? You're trying to spot these patterns and find the signal in the noise, as the famous phrase always says. But, you know, the big golden question is, well, how do you do it?
Lisa [00:21:42] When you're using analytics, you're always tracking something. You're always analyzing something and what's happening in the signals in the data. So, what you can do is you can take a look at how are, for example, how are the numbers changing? If it's sales or if it's marketing or if it's HR, I mean, there are qualitative and quantitative pieces to this. And so you need to understand how those are changing. And you need to have analytics, really, that monitors those so you understand how they're changing. If you can't measure it, you can't monitor it. Right?
Gemma [00:22:18] Is it a case of being able to still use the same tools and the same methods, the same, I guess, expertise that you've had before? Or is a complete shift of way of thinking about handling data when you're talking about, you know, different sizes, or is it just plugging it in a different way?
Lisa [00:22:35] Well, OK. So, organizations are kind of struggling with this. I mean, do they have the tools they need? The answer is it's really not a tool problem. Do they have the tools they need? Maybe or maybe not? Maybe you do have the right tools for your organization. Maybe you don't. It depends. I can't tell you whether you have the right tools or not. But what I can tell you is that a lot of organizations have been turning to the consulting organizations. If you have the money, the medium and large organizations are working with the EYs and trying to figure out what do I do. They've really been looking for what is a guiding light here? How do I deal with this? I mean, how do I deal with the historic data that - I have, all of this data that I've collected over the years? It could be for decades, right? It's all over the organization. And now we have this new data coming in. How do I make all of that work together? And the key is, do you have access to the right data sources? You probably are going to have to pull in some other ones that you hadn't thought of before. That's a problem because a lot of enterprises really have no inventory of their data sources. There's nobody in charge of that. In the larger organizations you have like a chief data officer, somebody who's in charge of that. But it really is about do you have the right data to answer the questions? Can you blend the small data and the large data? Yes, but you need to understand, what is it showing you, the historical data showing you some patterns? The new one is showing you newer patterns. What does that mean? It's kind of that middle point there that you need to learn how to deal with.
Gemma [00:24:08] So I wonder at least if you could give us some of your favorite examples of where companies have utilized data in a really interesting way or has been sort of at the core of what's made the business thrive.
Lisa [00:24:19] Well, actually, I was talking to a consultant the other day about how he had been using analytics to really understand some of his customers’ problems. He goes in and he works with them quite often on where are their customers going as a retailer, big retailer they're working with. And basically what they did is they went in and understood that the model that they had for customers was not quite as well tuned as it should be. And so what they did was they got some outside data and they also took a look with the tools that they had and they were trying to understand better what the customer journey was looking like. And they had a couple aha moments. And what they figured out was they were looking at signals in the data. And what they were finding was that some of their customer attitudes and behaviors had changed more radically than they had realized. So what they did was they completely re-did what their campaigns were. And the results have been really phenomenal because they really understood exactly where the customer was. And it was a completely different place than they had been before. The next one is for an IT organization. And this, of course, had to do with how they were empowering their workers at home. And so basically they were paying attention to how are people using the tools that we're giving them. And are they really as efficient as they want? They figured out that, no, that if they made some tweaks in what they were doing, that they could get more productivity from people. And so what they did is they actually got more tools in that were easier to use for everybody and were more collaborative. And really, the organization benefited a lot from that because everybody became more productive. And the interesting part of that, too, was there were also relationships that really hadn't been there before because, you know, now we can do all this online collaboration, which people weren't doing before. It's really a lot easier to work across departmentally. So that was their big win, is that people are working together and collaborating a lot more than they were before with people of different departments within the organization. So that was a big win for them. That was about internal efficiency, and so that's a good one.
Gemma [00:26:32] Got it. So let's zoom out a little bit and go back to one of the things you said right at the beginning, which is around getting that sort of overarching strategy, a sort of resilient data strategy and analytics approach. What are the sort of big lessons that organizations and people listening can take away to build this more resilient strategy, both now to react to what's going on in the world, but over the long term, too.
Lisa [00:26:57] So you need to be just, generally speaking, more agile. You have to - the best thing to do really is to focus on continuous improvement. Like I said, we were getting kind of a little bit too comfortable. In fact, if you think about the digital transformation we were doing. Because we had a pattern, because we had a journey that we were on and it had not been disrupted yet. We got a little too comfortable and we were kind of resting on our laurels. This has been a huge wakeup call that anything can happen. And there are a lot of things outside your control that can happen. We thought about disasters in terms of more regional things like, you know, a flood or a hurricane or, you know, that sort of thing. But this has been global and this has been systemic. So, what we need to do is we need to be a lot more agile and we need to break what we're doing down into smaller parts. And from an analytics perspective, that means really paying attention again to those signals and how they're changing on a much more nuanced level. You need to really pay attention to change because there's a lot more of it and a lot quicker now. So that's what we need to get used to.
Gemma [00:28:04] Amazing. I guess, finally, how do we move forward and make things like all these new technologies, AI, big data, machine learning, how do we make all of these new ideas work in the face of uncertainty? How do we keep up with that and make sure that we're pulling the right things in at the right time?
Lisa [00:28:22] Well, so that kind of gets in to cross departmental good collaboration. Part of it is an IT problem and part of it is a business problem. So, from a business standpoint, you need to really align everything you're doing in your organization, including the analytics. Do you have the right KPIs? Are they aligning to your goals? You know, and as your goals change? Are you revisiting those instead of looking at things in terms of like planning? I mean, all of our cycles have been shrinking. We need to constantly be monitoring from a business perspective, where are we at? What's our external situation? How are our customers' expectations, their behavior changing, all of these different things? But to make this work, you have to work cross departmentally to make sure that you're aligned. And also, do you have the technology piece right? Do you have the right tools? You may not have the right tools, but it's this constant look and analysis and assessment of where are we and where do we need to go. You're never going to get to a perfect state. Just understand that. It’s all about continuous improvement. So you have to just understand. And the technology is changing all the time. So there is no destination. We tend to look at these things as a destination. You know, we look at the digital disruption. If we just get to hear, we're gonna be OK. That's not the case, because by the time we get to hear the whole situation, the context is changed again. So, we have to change. So that means that we all have to be psychologically agile and the workforce has to be agile. The organization has to be agile. And the approach to analytics also has to be agile as well.
Gemma [00:29:59] I suppose a lot of what you're saying is the ability to look at what your business is doing at various different scales. Right. So, you have to understand, okay, what's the broader mission? What is it we're even trying to do? Why do we even exist? And then starting to look like wait how do we get better? Then, going further, a little bit down, what are the tools? Little bit further down what are the skills we have and so on and so forth. So I guess it also seems like when you talk about cross departmental, it's also a sort of vertical question, too, if I'm if I'm hearing you right.
Gemma [00:30:28] Absolutely. It's very complex. It is. It's complex and it's tough to get right. But that's the whole point. If it's tough to get right, if you focus on continuous improvement, then you start thinking about what's going wrong. You start thinking about instead of problems, you're thinking of solutions. And that's really the mindset we need to have.
Gemma [00:30:47] Awesome. Lisa. Thank you so much for sharing all of your incredible insights and so many, I guess, brilliant questions that you're planting seeds in the listeners, I hope. Lots to take from this episode. Thank you so much for joining us.
Lisa [00:30:58] Thanks for having me.
Gemma [00:31:01] That is it for this week. Thank you so much for tuning in.
[00:31:06] You can find out more about Lisa's work and indeed some of the broader themes we discussed today in the show notes. If you enjoyed the episode, please do take a few moments to rate and review the podcast. It really helps other people discover the show.
[00:31:17] And don't forget to subscribe to tune in next time to continue our conversation about innovation, resilience, and our capacity to succeed.
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