Embrace a Modern Data Sharing Solution to Accelerate Data and AI Outcomes – Six Five Webcast

Embrace a Modern Data Sharing Solution to Accelerate Data and AI Outcomes - Six Five Webcast

On this episode of the Six Five Webcast, host Steven Dickens is joined by IBM‘s Spurthi Kommajosula, Product Manager, IBM Data Product Hub for a conversation on enhancing data and AI outcomes through modern data sharing solutions.

Their discussion covers:

  • The critical nature of data sharing in today’s digital environment.
  • Common inefficiencies in current data exchange methods, including manual delivery and lack of data quality.
  • The significant benefits of adopting a data-as-a-product mindset, emphasizing quick value realization, cost efficiency, and improved governance.
  • Exploring the primary features and advantages of an internal data marketplace.
  • Concluding thoughts and actions to further engage with IBM’s Data Product Hub.

Learn more by visiting the Data Protect Hub Product webpage here, or talk to an IBM expert here.

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Steven Dickens: Hello and welcome to another episode of The Six Five Webcast. I’m your host, Steven Dickens and I’m joined today by Spurthi from IBM. Welcome to the show.

Spurthi Kommajosula: Thank you so much for having me, Steven.

Steven Dickens: So let’s dive straight in, tell the listeners and viewers a little bit about what you do for IBM, and then we’ll use that as a jumping off point.

Spurthi Kommajosula: Definitely, so I currently work as a product manager on Data Product Hub, and I’m really excited to kind of just talk about that, because it’s this new offering that we’ve come up with that ties into our data fabric portfolio within IBM data and AI, and it’s really all about just data products, and what we can do with it. So, we’re very excited about that.

Steven Dickens: So, we’ve been tracking this space for a while. Obviously, you guys made the acquisition of Databand, that’s kind of coming through. So, obviously IBM has been in this space for a long time. You guys have been in the data space probably as long as there’s been a data space. Let’s start to unpack that a little. We’re starting to see about data sharing, we’re starting to see the whole sort of data stack start to get reimagined. I’m seeing a lot of AI applications come through. That’s kind of top of the stack for me, but that whole piece below that, I see there’s a whole sort of inflection point. What are you seeing and why is that moving data around and that sharing of data becoming so critical?

Spurthi Kommajosula: 100%, so first and foremost, I think basically what you said really just applies. Number one, data at this point sits everywhere. You could be new to data, and new to data governance, or you could be in a place where you’ve made significant investments in terms of data fabric, and information architecture, or you could be anywhere between the spectrum, and you will still see that data will sit everywhere within your organization. So now say for instance, if I’m a non-data user, I’m somebody who’s really new to consuming, and making use of this data, how do I go about finding where data is, number one?

Number two, if I do so happen to get my hands onto this data, which by the way, from a manual process point takes anywhere between I want to say a couple of days, two weeks depending on how your organization’s structured, but if I do happen to get my hands onto this data, how do I know how to use it? How do I know if it’s good to use and how do I know when will it expire? So, even if I do happen to get my hands on data, there’s still so many open-ended questions, and this particular problem will only go to expand itself as an organization scales and acquires more things into their infrastructure, has more personas to deal with. This problem just keeps getting magnified.

So, how do you make that particular data sharing procedure more streamlined and more effective, so that way when you scale your data sharing also scales with you and puts you in a position where you can truly be a data-driven organization? So, that brings us to the main point of being data sharing focused in terms of the product.

Steven Dickens: So it’s interesting for me, we’re seeing that data everywhere. I think hybrid has won as the deployment model of choice. If you wind back maybe 10 years, it was cloud first. We’re now seeing people with data in the cloud, we’re seeing that with data on-prem, we’re seeing that with data in every combination in between. We’re seeing legacy tech stacks, we’re seeing new tech stacks emerging, we’re seeing AI drive through all of that. What are some of the challenges that you’re seeing? You’re chatting to clients all the time. What are those pain points?

Spurthi Kommajosula: So first and foremost, I think what we just talked about in terms of getting hands onto the right data at the right time, that definitely still applies, but what happens if I do happen to get my hands onto the right data? Now, let’s get into the shoes of somebody who is an analyst at a government or agency. If I have access to data and somebody else is requesting it, simply emailing it to them might not be appropriate. Maybe this person should not have had access to this data in the first place. So again, in terms of keeping aversion control or even for that matter an audit trail of how data is being shared across the organization is super, super crucial, especially if you’re in areas where data management is more federated or restricted in nature, so that’s one.

Number two, if from an organization perspective, let’s go a little bit more on the high level. When we think about data sharing, how do I as an organization know how data is being consumed within my entire firm? Who is driving most of the data usages? Which department uses data the most? Even for that matter, when does data usage spike within my organization? These are questions that are super crucial, especially when you start navigating and becoming a little bit more data mature and implementing these high scale AI initiatives within your organization. So with that being said, having that lens of visibility of how data is being consumed within the organization is super, super crucial. And naturally with that comes the fitting of or the need of governance and establishing governance when you’re sharing data, establishing governance when using data, really just having an end-to-end perspective where data is being used, shared, and managed with a governance structure that kind of ties everything together.

Steven Dickens: I think the couple of things that stood out for me there, you talked about governance, whether that’s for a regulatory perspective, whether that’s just good housekeeping. And the other piece was for me kind of how those organizations can look at the provenance of that data. Where did it come from? Can I check where it came from, whether it was from external, whether it was from internal, which department did it come from? Can I trust this data point and know where it’s come from within my organization? But that kind of leads me to the next question, which is one of the big barriers to this is data quality, duplication, inconsistency. Regardless of whether you’ve got that provenance controls and the governance controls, you’ve got to be able to just check the basics and that hygiene of the data. What are you seeing there?

Spurthi Kommajosula: So, that is actually fantastic. So, when we think of data consumption within the organization, it typically comes down to how good is this data to be used. Now, to kind of simplify this entire concept, think about how we consume products from a grocery store. Typically, if I’m buying milk, when I pick it up, I take a look at how long the expiry for the milk is, if it’s packaged well, and that’s when I go ahead and consume it. The same thing really just comes down to data as well. Your data needs to be in a position where it can be consumed, where the quality of the data is good, and if you have any questions about the data, you need to be in a position to be able to answer them as well.

And that’s really the point where data products really come in, because what we’re doing is really transitioning away from how data is being managed at a project level in a typical ad hoc type basis or a manual type basis to more of a commodified and operationalized perspective on data through data products. This is really a method of packaging data and data-driven assets into one particular product that is curated towards your specific business need or domain. And it’s infused with things like lifecycle management, right from its conception to the retirement it.

On top of that, there’s an SLA, SLO, or data contract structure in place that really kind of goes to highlight if the data that you’re using is restricted, or does it have certain policies or governance in place? Does it need to abide by certain regulations? Can it be used for AIML? How is the auditability in terms of the data as well? So, a data product brings all of this together and really operationalizes the data, so that way when you pick this data up for usage going forward, you don’t have to worry about things like quality, because again, when you consume a product in the first place, be it data, or something like milk from a grocery store, you’re assured with the idea that what you’re consuming is of good quality. And the same thing applies and should apply to data, which is where data products come.

Steven Dickens: I love that analogy. We hear data is the new oil, data is the new gold. I’ve not heard data is the new milk, so I love that analogy. I think data spoils trying to be… You mentioned the sell by date, what’s the shelf life of this data? But the thing that came across for me there in that analogy is data as a product. I hadn’t thought of that before. So the ability to sort of package it up, check the packaging of it, and then being able to deliver that through the supply chain. I’m thinking of that analogy of how that carton of milk gets transported around a supermarket.

Spurthi Kommajosula: Yes, exactly.

Steven Dickens: That exact same amount… Thinking about how that data gets productized, packaged up, you can check the governance of it. Where did the milk come from? Be able to move it through the process, be able to check the timeliness of that data. That’s a really powerful analogy in my mind.

Spurthi Kommajosula: No, 100%, really when it comes down to how organizations are consuming data, it’s such big volumes, and such big need as well. So, if these two are scaling so quickly, you need to pivot to a method of using, managing, and sharing data that also can scale to that particular need. And at the same time it needs to… And this is something that we talk about quite often, which is really at this point, a lot of the organizations understand that. They understand that, “Hey, we need to make investments and ramp up on how data needs to be shared and governed across.” But at the same time, for a user that would mean toggling through 15 different places to find what they need. Which again, in terms of time savings, in terms of the cost of just acquiring this data is so high, because you’re toggling through 15 different applications, talking to three, four people, escalating things, and then eventually getting access to data.

This is where again, Data Product Hub, what it does is just it sits on top of this existing architecture that you already have and connects to all the different layers. So, we essentially become this living room where you can just come in, grab what you need, and go without having to again interact with 15 different individuals, and applications, and depend on manual items to get what you need. You simply have this one-stop shop, almost like an e-commerce type website that can come and give you what you need in terms of your data. If you don’t have what you need, just have a method to just request it, and have it delivered to you. Think of how we consume things along the lines of ordering on Amazon. Within a click of three buttons, you have what you need. Shouldn’t getting access to data be that simple as well?

Steven Dickens: It’s interesting in the pre-read for this and the material that you guys sent over on our pre-discussion, we talked about this concept of a data marketplace.

Spurthi Kommajosula: Yes.

Steven Dickens: I think that’s fascinating. I’m going to keep with the milk analogy, because you’ve got that stuck in my brain now. You’ve landed that one. How does that packaged data product get consumed by the business and how does it get transported? And I love the concept of that marketplace and then being able to… But something else you mentioned, and we’re sort of 14, 15 minutes in, is the cost of managing this data. You mentioned it there and you blew past it, and I’m going to drag you back to it. What’s the implications there? We’re seeing data, just volumes explode. We’re seeing data be everywhere within an organization, both within their four walls in their data center, and in the cloud, and everywhere in between.

We’re seeing some of these data warehouse platforms, and some of these things be beleaguered by cost overruns, and we’re seeing the whole sort of FinOps from a cloud infrastructure perspective come through, but also seeing that come through in the data side. What are you seeing from the cost perspective? You touched on it quickly, but I’d love to take you back there.

Spurthi Kommajosula: 100%, so let’s do almost like a day in the life of a data user. So, without something along the lines of a Data Product Hub or data as a product, plus marketplace type and offering, without that being present in the organization, if I’m a finance user, somebody who is comfortable with data, but not really in the position to produce data by themselves, if I have to for instance, find the network spend of all the buildings that my organization owns, I would probably have to first and foremost try and query that myself.

Now, if I don’t happen to get that, and if I don’t happen to query that myself, I would probably go and talk to somebody from the producer standpoint and say, “Hey, can you send this over to me?” Now, the wait time between you asking that, and you getting that delivered in itself can be, as I said, weeks if not days. So, if you just kind of translate that into cost, that’s easily lots of money just being spent on you sitting just waiting for that data to come in, so that’s one-

Steven Dickens: Is that a sort of time to get the value from the data?

Spurthi Kommajosula: Exactly. I would say that is time to get the data, time to value starts from once you get the data. So again, that’s just extending your time overall. But then on the other side, from a producer standpoint, now this producer will probably have hundreds of requests just sitting on their docket. So, now how does a producer know which particular request to prioritize first? Would it be through a first in, first out, last in, first out?

Again, these are outdated methods of looking at how we produce and provide data. Shouldn’t it be smarter than that? Shouldn’t there be a system that tells you, “Hey, 15 people from your organization have been searching this particular thing, you should probably request or look into that particular request first as opposed to following an old method of managing and getting data sent over?” So again, in terms of time wasted in just producing that data adds up in terms of cost. Now, as a producer, I still get the data in, and I sent it over to my finance user.

Now, my finance user opens it, scrolls through and says, “Oh my God, I think this data is a little bit expired or stale. It’s not really as lively in the mindset of what I was thinking,” but what we don’t know is the producer didn’t look into one of the areas where data was sitting, like BI. They only looked into the warehouse and the catalog, but this particular information was sitting right there in BI, but there’s no way for them to know because there’s no visibility. So, now we’ve missed that big piece of getting that data converted into some level of outcomes or data-driven mechanisms. So again, that cost of missed data sets in.

So all this packaged together, think about it, this is just one interaction. On a daily basis, think about the amount of interactions a mid to large scale organization might have. These might be easily in thousands if not hundreds. So, if you just take that cost and multiply it, you have a crazy amount of cost just sitting, because your methods of managing, producing, accessing and sharing data are not as smart as you would think. So again, that really elevates things and escalates them into a position where you don’t want to be. Why not look into a more productive and smarter way of managing all this?

Steven Dickens: I think all of us have asked for data from somebody else, whether that’s a report we need running, whether that’s, can you get me some project data or I need this type of sales data, forecast data, whatever that data is, we’ve all lived a day in the life of that loop. You go ask a data producer, “Hey, can you run me this?” They come back to you two or three hours later, “Oh, that’s not exactly the report I needed. Can you just tune this a little bit and get me something else?”

Spurthi Kommajosula: Yes, exactly.

Steven Dickens: And that’s assuming that you’ve got an efficient organization where everybody’s responding to the request. Holidays happen, people are out sick. So, you put all the inefficiencies, I think it’s really easy to see where the cost comes, but I think we touched on it. For me, the interesting piece is the lack of time to capture the value from the data. So, you’re not just asking for the data because you want it, you’re asking for that data because you want to make a business decision based off that data, whether that’s to run a cost saving program, drive revenue, expand into new markets, whatever you need the data for, that’s being held up as a decision-making process, because downstream-

Spurthi Kommajosula: Exactly, again, think of the amount of time and money that just sits in this particular decision, but you can’t really act on it or even think about it as much, because the data that leads to this decision is not either in your hands or appropriate, or even if it is you don’t know how to use it or maybe there might be restrictions to it. So again, all of these come down to how Data Product Hub can kind of alleviate that particular issue. And I think as you mentioned, we really have two things going in the first place. Number one, that’s data as a product concept, which we touched upon, but it’s that combined and infused into a data marketplace.

So, now you’ve got two ends of a spectrum. A finance user or an HR user within the organization can jump onto Data Product Hub and look up the data products that they need. If it suits their needs, all they have to do is look into the terms and conditions of use, accept it just like how you would for all the terms and conditions, and then just have it subscribed out. So, think of it like how you would get something delivered to your doorstep from Amazon. Just like that, you would have the data delivered to you, be it in the form of a URL or an Excel or even for that matter be it in the form of a flight service mechanism, which can just simply come into the application that you’re using.

So now again, in terms of delivery, it’s easier, but from a producer standpoint, you now get to see a very smart method of how to access and manage these data requests that come in, along with getting a very clean visibility of how data is being consumed across the organization, who is using what, when, and why, and how often. So again, in terms of visibility, you get that very, very cleared out. So overall, it’s a marketplace with data products in the mind of it, which really would help an organization scale that time to value and optimize that time to value in fact. Number two, give you that ability to get that visibility across how data is being consumed in the organization while being assured that governance and enforcement are still at the heart of all of it. So, you know everybody who’s using it is using the data properly with no open-ended questions of how things are being transported across the organization in regards to data.

Steven Dickens: So especially… I could have this conversation with you for an hour or so, if not longer, but they pay me to be the host and keep things sort of moving along here. Couple of key takeaways from me, but I’m going to ask you the question. This concept of data as a product, you’ve convinced me that data is the new milk, not the new oil, so I love that, and then this concept of a data marketplace. But what would be your takeaways as people are watching this show? What would be the sort of three quick punchy topics that you would like people to think about when they think about IBM data protector?

Spurthi Kommajosula: First and foremost, data as a product operationalized method of managing and looking at data. Number one, I think that is right in our name. That is what we stand by and I think in terms of thought about what Data Product Hub does, data product is right there in the name, so that’s number one. Number two is really that governance structure of how data is being managed and how data is being consumed and shared across, so that’s number two.

But number three is really governed data sharing, and that is something that we really think is a very, very key value of what you do with Data Product Hub. You have this method of now sharing data across the organization with the ability to govern that particular sharing mechanism in the first place, not just governing data, but governing the sharing of data. So, now be it in the sense that you’re a federated organization or an organization that is just slightly getting into the idea of data fabric, whatever it is, you now have a method to manage that sharing creation, access to data overall, giving you that end-to-end perspective on data management for your organization.

Steven Dickens: So, that’s fascinating. Really good three bullet points there to bring us home. What should people be doing if they want to start their journey finding out more about Data Protect Hub? Where should they go? What should they read first? What would that sort of, I don’t know, call to action be if people are interested having watched this show?

Spurthi Kommajosula: For sure. First and foremost, come check us out. There’s a website called Data Product Hub. I think that has fantastic information of what the product can do in the first place, along with giving you that ability to see all of this in motion, because I think what we’ve talked about right now is great, but the ability to tie what we’ve talked about to what we can do, I think really elevates that experience. So, feel free to check out all the videos that we have. We also have a fantastic white paper that can really stand by in terms of statistics of how Data Product Hub can help and how data as a product is really just the next wave of data management and how to consume data across the organization.

So, feel free to check that out. It’s also on our website, along with a very quick, almost like a teaser of what Data Product Hub can do. And naturally, if all of this excites you, we’d love to continue this conversation. So, feel free to book some time with any of our sales folks. It’s, again, the availability of it is right on our website, too. So, really all of this ties into our website. Feel free to come and chat with us more on this. We love talking about this more and more, so our website is right there and then. I think that’s fantastic.

Steven Dickens: That sounds like a great place to start. I’ve got to wrap us up, Spurthi. This has been fantastic. As I’ve dug into this product, more and more fascinating every time I go and peel the next level of the onion, but we’ve got to make this short and impactful. Thank you so much for joining me today. I really appreciate it.

Spurthi Kommajosula: Thank you so much for having me as well, Steven.

Steven Dickens: So, you’ve been watching another episode of The Six Five. Please click and subscribe and do all those things to improve our algorithm and how many people see these shows, and we’ll see you next time. Thank you very much for watching.

Author Information

Regarded as a luminary at the intersection of technology and business transformation, Steven Dickens is the Vice President and Practice Leader for Hybrid Cloud, Infrastructure, and Operations at The Futurum Group. With a distinguished track record as a Forbes contributor and a ranking among the Top 10 Analysts by ARInsights, Steven's unique vantage point enables him to chart the nexus between emergent technologies and disruptive innovation, offering unparalleled insights for global enterprises.

Steven's expertise spans a broad spectrum of technologies that drive modern enterprises. Notable among these are open source, hybrid cloud, mission-critical infrastructure, cryptocurrencies, blockchain, and FinTech innovation. His work is foundational in aligning the strategic imperatives of C-suite executives with the practical needs of end users and technology practitioners, serving as a catalyst for optimizing the return on technology investments.

Over the years, Steven has been an integral part of industry behemoths including Broadcom, Hewlett Packard Enterprise (HPE), and IBM. His exceptional ability to pioneer multi-hundred-million-dollar products and to lead global sales teams with revenues in the same echelon has consistently demonstrated his capability for high-impact leadership.

Steven serves as a thought leader in various technology consortiums. He was a founding board member and former Chairperson of the Open Mainframe Project, under the aegis of the Linux Foundation. His role as a Board Advisor continues to shape the advocacy for open source implementations of mainframe technologies.


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