Intellyx Development Stories video for Zoho, featuring Chandrasekhar LSP and Jason English
In part 3 of the Zoho Creator – Intellyx Development Stories, Intellyx President Jason Bloomberg talks with LSP, Senior Evangelist, Zoho Canada, about the future of Analytics and Business Intelligence as it will be operationalized for real business.
Click here to watch the video on YouTube: https://youtu.be/as884Kv415g
The Future of Analytics & Business Intelligence – Zoho Dev Stories #3 Transcript
JE: Well, hello and welcome to the Intellyx Future Developments video series with Zoho. In today’s episode, episode 3 is the Future of BI and Analytics, and my guest today is Chandrasekhar LSP He’s a lead evangelist for Zoho in Canada or sometimes just called LSP. That’s easier. Welcome to the show.
LSP: Thank you very much, Jason for having me on the show.
JE: Yeah. you know business intelligence and analytics tools have been around almost as long as enterprise software itself, and in essence, it’s like the first things companies need for planning. Once you get beyond ERP and transactional and customer management systems, it always tends to go to this management level experience where we’re looking at analytics and, and BI tools. So you know, how, how did companies adopt and govern these kinds of strategic packages in the past, and what results would companies expect out of their investments there?
LSP: Well Jason, you know, back in the days the whole idea was you know, the whole idea of analytics or anything pertaining to understanding what your business or the metrics and mo you know, any KPIs that you wanted to monitor. It was always the centralized IT team that basically got the data together, and then people who wanted specific types of reports or some kind of metrics would have to talk to the IT team. And then they would create using the tools that were available back then, they would create these reports and then hand it over to the decision makers or whoever wanted those kind of you know, data points. And you know, that was essentially how it all began, right? I mean, like, you had data, you had people who understood the data, they created the models for analysis, they created those reports and handed it over to the people.
This, this is essentially how it has always been, but now a lot of change. A lot of things have changed. I mean more and more. Mm-hmm. <affirmative> people are very what you may call they data natives, as they would call them. I think the president generation of workforce, they’re so data driven. Everybody has data is in everyone’s remit, so they want to be able to do it themselves. And as you rightly said, while these things have been always available, I think the last decade or decade and a half, I would say has been interesting because a lot of things have turned into self-service. So you now have a line of business user who wants to have access to, you know, the transaction systems to do their day-to-day processes, but they also wanna understand what’s happening within the system. They want to get insights about specific aspects of their business, and they’re able to accomplish all these things using self-service BI tools. It’s no longer that much of a I would say a requirement for it to be involved on a daily basis to give these insights to business users. I think business users have gotten to a point where they’re able to do it themselves today.
JE: Yeah, that makes a lot of sense. It seems like self-service is a common trend we’re seeing everywhere as everybody becomes a kind of a practitioner in their own space. I mean another aspect of that is, companies started moving from having these functions as enterprise silos to a more services based approach, where they’re using a lot of different SaaS software packages for almost every different business function now. so how did that kind of affect the landscape for BI and analytics as well?
LSP: I would honestly believe in my personal opinion, I think that’s been a major accelerator for self-service bi because on an average a typical enterprise uses more than a hundred applications today. And then every every one of those apps has data that can unlock insights for you to take better decisions about your business. And just having a single team perform all these things. I mean, it’s humanly not possible, right? No matter how much governance and everything that you wanna have. You know, there are things that are happening now, even around governance, I’m sure will probably be gaping to that at some point. But interestingly, self-service has been accelerated by all these SaaS apps. And also the fact that now a line of business user wants to be able to connect to the operational system that they’re using. For example someone in a sales department may wanna be connecting to their CRM system to understand, Hey, what’s happening to the leads?
What’s happening to the conversions? What’s happening to my marketing campaigns? And then on the other hand, you would have somebody in the customer support team who wants to be able to connect to their customer support ticketing system and analyze what’s going on with support. Are, are they able to keep up their SLAs or, you know, how many escalations are they dealing with, how many level fives versus level one? So these kind of metrics are able to kind of help businesses to operate in a more efficient way. And these kind of ANA analysis and analytics and the capabilities that self-service BI tools gives these line of business users is, is tremendous in terms of what it allows them to do today.
JE: Yeah. And it’s interesting how it’s also affected really the, what the sources of this data are. They can live in so many different places now. They’re not, they’re definitely not in the silos either. The, the sort, the data itself is coming from everywhere. it, it’s interesting how, I mean, who should own this business intelligence function within a company now? I mean analytics were traditionally the, the realm of data scientists, but how is it becoming more universal for the rest of the company to interact with all of this data?
LSP: I think in terms of who owns this today, I think from a governance perspective, I think a central team is significantly important. I mean it, it has not changed. I would say it would leverage change, but what needs to happen is how, how about having, let’s say people who understand data governance being embedded into all these business teams as well, so they can be a part of the IT team, but then embedding them into specific you know, operational side of the business is also gonna be critical. Just having a I type of central IT team trying to understand data, I think that is gonna be impossible. I think that’s a that’s, that’s a problem I don’t think anybody can actually solve. I think it has to be a lot more collaboration between IT and line of business users.
And then I think what’s also important in what we discussed Jason is, is the fact that, you know, today the self-service BI tools allows a line of business user to connect to different systems. And then these systems automatically create models for analysis, and they also create reports and dashboards out of the box. So it is gone. The needle has swung so much, or the needle has moved so much that even from a governance perspective, I think there is only limited things that needs to be done because whatever the operational systems are, whatever their you know, access control cap and access controls are, those are actually inherited into the analytics systems as well when you do the integration. So it’s not like you have to do a separate you know, governance as far as analytics is concerned. The fact that the operational system has some semblance or some aspects of governance handle analytical systems kind of inherit that.
But then if you want to get to the next level, even within the analytics systems these platforms allow you to do all those things. So to answer your question, I think it’s, it’s teamwork. I think it’s that’s, that’s, I think that’s how I would qualify it. I don’t think the line of business cannot take care of governance. It being independent of the operational teams can understand how data has to be consumed to get the insights of the line of business users need. I think they have to kind of collaborate of the work and have this taken care of.
JE: Yeah, so I mean, I guess the need for a global awareness of data still hasn’t gone away. It’s still good to have a team that can do that, but it’s, it’s kind of forcing a sense of data literacy down to the rest of the company to understand what’s coming in. and that, that’s a, a tall order for a lot of companies, but I think, you know, the ones that succeed will probably be the ones that are good at that transition. you know, what could help with all of this, this flood of data that we’re being sent, as well as some other aspects of trying to interpret that data is the idea of AI driven analytics mm-hmm. <affirmative> mm-hmm. <affirmative> of using AI in some way to, to help whether that’s filtering or processing data helping produce it for the consumers. But it, I mean, isn’t a lot of that the current claims of this kinda AI based analytics or data, isn’t it kind of AI washing, and how do we kind of avoid keeping that from becoming a, a reality in today in the future of, of BI and analytics?
LSP: You know, it’s actually a question that has different aspects to it when you talk about it, but before we get to that aspect, I think one of the other aspects of the platforms today, the self-service platforms, BI platforms today, is the inherent capability in those systems to also manage and prepare data. Because, you know, that is also a trend that is happening. The all along, we talked about self-service bi, but then there is this whole category by itself called self-service data preparation, where vendors now have, or you have you know, platform providers who now gives various capabilities point and click functionalities, and also, you know, AI and ML embedded into these self-service data prep tools, which allows a line of business users to prepare the data before they can take it for for deriving insights, because otherwise it’s garbage and garbage out.
Think about so many things we talked about, let’s say the CRM system. You could have duplicate leads, you could have duplicate contacts and all these things. So trying to understand a system that has a lot of duplicates or even the way data is presented, maybe for example, let’s say the way you know, I categorize my regions one data op, one sales user or sales agent may enter the state name as Washington. Someone else may be doing it as wa, but again, it’s actually the same data. So what if you can actually have systems that can help you to organize this data before you take it for analysis? So a lot of these self-service platforms today also start, have started to offer in self-service via, and you self-service data prep, you’ll see that this is a new trend, and then the needle is going from just about reporting an analytics to taking it to the point of doing data preparation.
Now, to come back to the other part of your question about AI and ml, you know, I, I generally think that when it comes to the BI space specifically AI and ML have been kind of infused into various aspects and has been done so in the past as well. But it just is not as much of a buzzword today as far as ai, as far as BI is concerned. Because if you look at, at capabilities such as natural language interactions, data, it’s there, it’s actually AI to process a query line of question in English, and then translate it into a query and then get the right set of data to present the, the visual or the report to a user. These things have been happening. And then I, I see that a lot more and more vendors don’t try to kind of whitewash AI and ML as far as the BI space is concerned, because it’s expected to be there.
And you’ll also see capabilities like forecasting, trendline analysis, all these are pre-practice libraries that vendors actually a plug in into their offering. And then when you bring in data, it runs through those algorithms. And then you see all these interesting insights based on which you can take decisions. I would say things like even natural language generation where a visual is taken and explain in plain English or a specific language, right? It could be Spanish, it could be print or whatever. You have vendors who have multi-language support for national language querying and national language generation and so on. But this is, this is a trend that is gonna keep on moving. And I don’t think bi vendors will stop to talk about, Hey, how was AI and changing the world today? Because I think it’s in everybody’s I met today in terms of all these capabilities as far as a platform is concerned.
JE: Yeah, you’re making a good point here, because a lot of it really isn’t we haven’t changed our, our power to you know, run algorithms or, or do the actual computational exercises, but a lot of it has to do with our interface with the systems itself, and the fact that the systems understand like the context of what we’re asking, or, or maybe even our sentiment, like when we ask a question that we wanna get results that appear like I’m comparing apples to apples, and that I’m seeing, I’m seeing the relative values that I was looking for. you know, we’ve seen this notion of, of self-service evolving for a long time are, are we gonna get to this kind of Star Trek place where we’re just asked the computer for whatever it is we need, and, and, you know, it’s just gonna, computer’s gonna tell us what we want. I mean, is, are we gonna see that kind of future coming up soon?
LSP: Jason? We are already there. Okay. In case <laugh>, but, but honestly, I think, I think today’s systems have evolved, and then all these capabilities are only get gonna mature over time. And I’m a hundred percent confident that some of these capabilities will kind of drive insight driven decision making in a business. Okay. What’s the idea about, let’s say, national language quitting? You know, it might sounds interesting, it might sound pretty, you know crazy. But then the idea is when a line of business user, a frontline worker wants to make a decision, let’s take, let’s go with an example, right? Let’s say I’m with a telco mobile service provider, and I’ve been with them for 10 years, and I call the customer support. What do they know about me? And if they wanna know things instantaneously about me, okay, how long has this phone number been with this customer?
And what is their in a monetary value? How frequently have they been buying things from me? And so on and so forth. If you wanna understand different aspects, you know what if the agent can actually type a question, show me the rfm, the recency, frequency and monetary value of the individual who I’m talking to, in this case a phone number or maybe my last name. And right there, they have enough information based on which they can talk to you. Let’s say I’m aggrieved at this point. I wanna kind of switch off my service, but then they can talk to me with data in front of them, and they’re able to take decisions and say, okay, we understand your frustration, and then we wanna kind of give you a discount or whatnot. In the absence of this, I’m just spending ton of time explaining everything when the data is available today in the enterprise.
This data is, it’s, it’s not that it’s kind of new, this information is available, it’s just that it doesn’t permeate to this line of business user, but the frontline worker who’s kind of giving the experience to the customer. Most of these, what I see in general is most of these visual dashboards kind of get imprisoned in ivory towers. It’s mostly the decision makers, the management you know I mean the directors of marketing and so on who kind of access have access to this data, but what if we can actually put this in the hands of a customer support agent or a sales agent who can interact with the system in the most in a natural way that they can. So that’s where I think this is pushing the needle. So I would say this is actually democratizing data. These the, the manifestation of AI and ML is actually democratizing the access to data within an organization. And I think this, this is a trend that will go on, and I don’t think people will kind of sit back and kind of talk about AI and ML to be the next big thing because it is already a big thing and it’s doing a lot of interesting things within organization and enterprises today.
JE: Yeah, that makes sense. You’re, you’re basically you’re democratizing, but you’re also bringing it to kind of this capillary level where it’s right at the right at the connection point with the customer, which is where we, we need it the most. absolutely. so what would you say, lsp, what would you say your predictions are for the future of BI and analytics? Where can you see this going in the next, say, a five to 10 year horizon?
LSP: I personally, I’m very bullish on self-service, data preparation, and this capability becoming a inherent part of every BI platform, because you don’t want to kind of deal with junk data. You know, we always know this axiom garbage and garbage art. But then I think it’s where the next set of challenge, you know, we have tried to solve self-service, BI reporting capabilities and connecting to data sources, creating the models. So as an industry, we have solved a lot of those problems. The next set of problems are about data. And then the challenge with this Jason, is the, the amount of data is never gonna reduce. And more and more enterprises, you’ll start seeing things like data from sensors, real time data, everything is going to be a part of the enterprise. How you wanna deal with so much of information unless you have the ability to, you know, understand how these are connected.
How do you prepare the data for analysis? I mean, let’s say a simple example here. Let’s lead with an example again. Let’s say you know in my hrms in my HR system every time I key in my badge at the sensor in front of the office, auto biometric device or whatever, you know that information can go into my HR system to indicate that I have checked into work, right? Yeah. And then how do you actually connect these things and do an analysis later in, you know, at some point, you know, how much of space are you using in a specific facility based on how many people are coming into the office? Do you really need such a large space or do you need to kind of grow bigger? So these are all the things that, you know, once you connect all these data points, it’s gonna get interesting.
But then how do you do it? You need the right platform. You need to kind of you know, make people who are making those decisions be able to manage data. And I think, I think so in that sense, self-service data prep is gonna be big, real time data analysis, streaming analytics, as we call it, it’s been going on, but then it’s going to get more and more mainstream. You know, a lot of places you are seeing a lot of streaming analytics, but then with the compute powers and everything that we have at our disposal today, our ability to do that even more effectively and you know, kind of take this to the next level that’s gonna happen. And I would say data preparation and being integrated into a BI platform, I think that’s gonna be the norm.
JE: Well, great. Well, thanks so much LSP for joining me. It’s been a good conversation.
LSP: Thank you very much, Jason. Thank you very much for your time.
JE: Yeah, well that does it for our third episode of the Future Developments video series with Intellyx and Zoho. Thanks so much. And have a good one.
©2022 Intellyx LLC. At the time of recording, Zoho Corporation is an Intellyx subscriber.
