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Confidence in Your Data: How Actian is Helping Companies Unlock the True Value of a Data Platform | Futurum Tech Webcast — Interview Series

On this episode of the Futurum Tech Webcast – Interview Series, I’m joined by Actian’s Emma McGrattan, SVP of Engineering & Products for a conversation on how to put your data to work and get the most value, and how Actian is differentiating itself to drive businesses and their applications into the future.

Our discussion covers:

  • We get an overview of Actian’s background, and how the company is evolving to meet customers where they are
  • Emma shares her insights into how AI has shifted the ways enterprises are handling their internal data, maximizing the AI opportunities and the challenges within
  • A look at how Actian is differentiating itself, making data “easy” with the right solutions for their customers, including performance and scalability
  • Emma also shares her advice and perspective to IT Leaders as they look to drive AI projects within their organizations

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Disclaimer: The Futurum Tech Webcast is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors and we do not ask that you treat us as such.

Transcript:

Daniel Newman: Hi, everyone. Welcome back to another episode of the Futurum Tech podcast. I’m Daniel Newman, CEO of the Futurum Group. And today, we’re going to be talking about data, data platforms, and one of the big things on the minds of executives of enterprises everywhere, and that’s how to put the data to work to get the most value driving your businesses, your applications into the future. Today, I’ve got Actian’s SVP Engineering and Products, Emma McGrattan. Emma, welcome to the show.

Emma McGrattan: Thanks so much, Daniel. It’s a pleasure to be here.

Daniel Newman: It is. It’s great to have you here. I can tell you, in the business I’m in, we are on a merry-go-round. I go city to city 47-48 weeks a year on the road, and I spend most of my time with technology vendors. And I’m telling you, Emma, you are in the perfect place right now, in the middle of the data storm. If you aren’t thinking as an enterprise about how to put your data to work to differentiate your business, you’re not thinking. I’m hoping today to talk to you a little bit about that because I know you work with mid-market enterprises up to larger enterprises, and you’re focusing on solving their problems.

Of course, the company Actian has really evolved a lot to make sure it’s able to support customers and meet them where they are. But I guess first things first, because Actian is a big company, part of HCL Technologies, but not necessarily a name that everybody hears every single day. I’d love for you to give a little bit of a background around the company. Give us a little bit of the story and, of course, tell us a little about your role and how that’s evolved during your time at Actian.

Emma McGrattan: Sure. I started Actian 30 and a half years ago, and at the time I said I’d give it a year, see if it worked out for me. And here I am, 30 years later, running a distributed engineering organization that spans the US, Europe, and India.

Actian is a company that’s focused on making data easy. We’ve got customers that trust us with their most important asset today, their data. We have technologies that enable them to integrate data from various sources to manage that data and to analyze that data. The typical customer for Actian is a company that’s enabling their workforce to make data-driven decisions. And what that means is, they want everybody who’s making a decision for the business not to work on the gut feel but rather to use the data.

To do that, you’ve got to provide them with access to all of the various pools of data you’ve got across the organization. You’ve got to bring them together in a unified view of what’s happening within the business, and you’ve got to provide them with clean data that they can trust. What we’re really focused on is making all of that easy. Making data easy is what Actian is all about.

Daniel Newman: Yeah, I’ve got a whole bunch of follow-ons to that. I do have to admit that the 30 and a half year, I wasn’t sure when you were starting to say that, were you going to be 30 and a half months or were you…

Emma McGrattan: I was.

Daniel Newman: That is a good stint anywhere, so what you probably didn’t mention, but I have to imagine you would agree, is that you work at a place that has a really exciting, innovative culture. That’s my perspective from the outside, but the reason I’m saying that is because people don’t stay for 30 years at companies that aren’t doing really exciting things and creating a lot of opportunity, which has seemingly led you into this very senior role today.

Emma McGrattan: Yeah, I promised myself when I started at Actian that if there was ever a day I dreaded coming to work, that would be my last day at Actian. And I absolutely love it here. I love the people, I love the innovation, I love the fun that we have as we innovate. I love building differentiated technology, and I also love engaging with our customers. We’re a global customer base, which affords me the opportunity to travel to meet those customers and to talk about what are the challenges that they’re facing and help to design and build solutions that meet their needs.

It really feels like a family, and it extends into the… The distant cousins would be the customers and the partners that we work closely with and with whom we’ve built really deep relationships over the years.

Daniel Newman: This is something I’ve been struggling with, Emma, and someone with your experience, I’d love to have you speak to this, but I know we’ve hit this very popularized moment for AI, which has led to a lot of popularity around analytics and big data, and it’s this life cycle that we hear a lot about. But like you said, you’ve been at this a while, this isn’t a new thing for you.

And I actually said a lot of what we are seeing today isn’t really new, even though we’ve AI-washed a lot of things and made everything all about it, is that companies getting their data right, getting their hygiene right, getting schema right, being able to deal and work more with unstructured, all those things that are going on in the… What’s changed? Over this period of time, maybe even just in the last couple years and even in the last few months, just your perspective on how is it changing to culminate in this really important moment for Actian?

Emma McGrattan: What we’re seeing is a top-down push from the board level for every company to have a story when it comes to AI and to start with what we’re working with our customers on is preparing their data for that. So, they’ve got to make sure that they’ve got clean data that they can trust. They’ve got to do things like preparing that data in so far as removing any personally identifiable information. What we’ve built into the platform is the capability to mask certain data.

So, as you’re running some of the algorithms and the models against your data, you need to make sure that it’s not picking up any identifiable information, certainly for customers within Europe that are protected by GDPR, customers in California. That’s where our customers are today. They’re preparing for this, they’re making sure that they have all of their data assets cleansed, that they’ve cleaned up any potential leaks of PII data, and we’re working with them towards that goal.

And so, what we’ve done with the platform is, we made it really easy to get to all of these data assets to prepare the data to bring it into the platform as a cleansed… I’m sorry, into a cleansed data environment. And that will be the foundation for running their AI.

Daniel Newman: As I’m listening to you talk, and this is something I’m struggling with too, I’m catching that there’s a higher sense of urgency and that the need to move faster is definitely being driven by the board. But to some extent, there’s a little bit of this washing on AI that’s like people are suddenly thinking that it’s all different and that everything needs to change and that… But a lot of the things that you’re talking about is just good hygiene. These are the activities that companies from mid-market all the way to the largest enterprises really need to be focused on if they want to be able to maximize a very vast and diverse hybrid data ecosystem.

Emma McGrattan: This is very true, but what we see customers starting with are plans to enable their customers on self-service. So, what they want to be able to do is to run these algorithms across massive data repositories. It may be history of support cases and a knowledge base, and what they can’t risk is that any customer data gets leads as part of that process. It certainly is good hygiene, but I think there’s a real fear driven by some of the regulatory fines that may be associated with acts like GDPR, and they’re really nervous. They’re also really afraid of the reputational damage that could be done.

And so, we’re being very cautious internally in how we’re using generative AI in the development process, and our customers are being cautious to make sure that they’re cleansing this data, that they’re making sure that there’s no risk associated with any of the product of these generative AI tools. And to do that, they’re masking any confidential and private information within their dataset. Not removing it, just masking it, so it remains as part of their dataset, they just don’t want it exposed.

Daniel Newman: Yeah, I think you bring up some great points, but it is very hygiene-driven and you have to count on the fact that this evolution is going to take place. But along the lines of being able to get your data from where it is to where it needs to be, whether you’re doing it to build gen AI apps, you’re doing it to build better analytics, better applications in general, these practices are all the same. And so in many ways, I guess one of the things I think is important that you reiterate is all this preparation planning is going to be an absolute critical set of activities ahead of being able to take advantage of these next waves of technology.

And I think what you also said that’s important is being really methodical about how you incorporate new tech. We’ve seen a lot of cases, and I know at the data platform layer, this isn’t your exact, but we’re seeing a lot of cases of companies exposing data they don’t want to expose because their employees are finding these tools and these technologies. These are the trend lines right now.

And so, whether it is dumping your code into some sort of tool that you don’t know where that lands or dumping your company’s proprietary data, these are reasons though that some of the really well-designed best practices of the past two decades around big data really haven’t been immediately upended by the new trend lines. It should be a bit of an augmentation, a steady careful analysis, and then move at a pace that you can really do it securely and within the constraints of protecting your customers and your data.

Emma McGrattan: Absolutely. And I can see companies mandating some training around this, right? And making sure that they’re staying up to date with the latest technologies and trends that are happening.

Daniel Newman: Yeah, absolutely. And I think that is a really important point, and I just hope that people out there, Emily, listen to you. Some of your depth of experience, hopefully me too, just to understand that there’s a bit of a checks and balances here. It’s very exciting. Everything’s going on really exciting, but just understand that there is risks in whether it could be compliance and regulatory risk that you could face for a business, especially if you’re in a highly regulated or even in just a small business, just exposing your confidential trade secrets and stuff because you have your employees using apps that they just should not be using to do work because they don’t fully understand the whole risk reward. It’s a pretty significant continuum.

I want to pivot though really quickly to platform. One of the things about Actian that I’ve noticed, as I’ve been tracking the company, is this pivot from product to platform. This isn’t something that no one else is doing, but I do think that the pivot to platform is really important for most software companies in order to stay relevant and to be able to stay with their clients to meet their needs and meet them where they are. Tell me a little bit about the journey at Actian from product to platform, because clearly you were part of that.

Emma McGrattan: We started with our analytics technology. We had an on-premises analytics database that ran on both Linux and Windows as well as Hadoop. And what we saw was a very quick shift from Hadoop to cloud. And what we said was, “Let’s take that data warehousing solution that we have, and let’s re-platform that for cloud,” which means looking at cloud storage is very different to on-premises storage. As you build out elastic solutions that can scale, you’ve got to think about how do we do this with a Kubernetes cluster and so on?

So, we took a step back and we said, “How do we take what was previously in on-premises technology and reimagine it for cloud? We put a lot of effort into delivering that technology on cloud, and then we said, “You know what? The first thing a person wants to do when they purchase a data warehouse is put some data in it.” So, Actian had it in its portfolio a bunch of data integration technologies that allow us to move massive volumes of data quickly, and it also allows us to access hundreds of data sources. We said is, “Let’s extend this from just a warehouse to a connected warehouse.”

Now, all of a sudden, we have a warehouse that you could feed data from a multitude of sources too. We then said, “Yeah, the next step here is data management.” That transactional data, every customer that we have is building up a massive repository of transactional data, which is what’s going to feed the warehouse, so let’s make our transactional database part of the platform.” It’s growing naturally through an evolution of customers coming to us with problems to solve.

The next one that we’re looking at is IoT, right? We have an incredible database that runs at the edge calls in that is being used today in some horticultural warehouses as well as smart cars, right? Collecting a lot of data, making decisions at the edge. But for deeper analysis, you want to push that data from the edge to the cloud and make that part of the platform.

This platform is evolving very quickly to meet the data needs of our customers. We’ve got the ability to reach into data lakes to get to structured… I’m sorry, unstructured and semi-structured data. We’ve got the ability obviously to handle structured data, but a massive variety of data coming from a massive variety of sources and being able to process that data very quickly.

And the other thing that we’ve seen most recently is a lot of customers that were early adopters of cloud technologies are now looking at returning some of their workloads to on-premises. I think that’s largely driven by cost concerns and not so much around data security or data privacy concerns. And what we’ve done in building out the platform is we’ve architected it in such a way that we’ve separated the control plane, which is where you issue your commands, create a warehouse, create an integration job, whatever that may be. And from the data plane, which is where the data assets are.

And in this data plane, we have the ability to deploy that any place at Kubernetes cluster can run. That can run on premises in your data center or that can run in your part of a public cloud or in the Actian part of the public cloud. So, providing that complete flexibility is something that our customers are very appreciative of because depending upon the data set that they’re working with, and they may decide that it’s fine for their website and some of their marketing campaigns to run in the cloud, but they’d much rather that some of the core business systems we’re running on premises.

So, we’re delivering a platform that enables them to handle a variety of different types of data coming from a variety of different sources, analyzing that in real time and being able to provide everybody in the organization with access to data that they can trust to make decisions.

Daniel Newman: I think you hit on a couple of items, maybe even three, that I think are high value and notable. The first is varying data sources. This is a bit of the holy grail right now is the ability to bring lots of data sources. Structured has been: Columns and rows, everybody’s done that for a while. Get it. But as you get to this sort of unstructured and semi-structured data, this has been a bit more of a process for companies. And of course, in the era that we’re moving towards with more and more AI, it does become a different architecture that’s required.

I think what I’m hearing from you is you’re hitting the marks that I’m saying in my analysis to the market are musts. So, Actian is hitting the musts. What are some of the things that you’re finding though that when you are winning customers, you’re growing customers, what are they coming back to saying these are the things Actian does really well, and this is why we’re investing doubling down, continuing to expand, and choosing Actian, and I would say a very competitive market space.

Emma McGrattan: Performance is definitely one area in which we’re differentiated. Let me give you an example. We have a customer in the UK, the AA, the Automobile Association. And they are in the business now beyond just roadside assistance, they’re also providing insurance quotes. And what they need to be able to do is to generate an insurance quote in a less than two seconds. And because a lot goes into an insurance quote when you think about it.

So, you’ll typically go to a website, you provide your address, you provide maybe the vehicle identification number for the car and maybe your driver’s license information. And that information then gets augmented. So, we need to understand the address where your car is parked, what are the chances of it being stolen or vandalized. You need to understand your driving record, right? How reckless are you, and what’s your driving record look like? And the car itself, has it been in an accidents? Maybe there’s some issues with the engine or with that particular model of car.

So, they take the basic information, they augment it, they come up with an insurance quote, and they provide that back to the prospective customer in less than two seconds. They chose our technology because it could do it in a fraction of the competition. And what that meant is that when you go to one of those websites where they list multiple quotes, their quote comes up first. And people are more likely to click on the quotes that come up early as… Well, other people are still figuring out what a good quote might be.

Performance is one area in which we’re clearly differentiated. The second is that our platform is pre-built, so many of our competitors provide you with a Lego box and you can build whatever you want from that. I’m a huge fan of Lego. When it comes to business systems, I’d much rather that they were pre-integrated and pre-built and easy to use for me.

So, we have our data warehouse, we have the ability for that data warehouse to take data from a multitude of sources, so over 200 connectors as well as support for things like REST APIs and Scala and Spark, so you’ve got the ability to bring data in from a massive array of different data sources, and so that connectivity is differentiated.

The third would be the fact that we can deploy hybrid. That ability to deploy the workload wherever it makes sense for the business. Some maybe on-premises, some maybe in cloud. You typically have a combination of those and that hybrid platform as something that’s quite unique.

And then, a fourth area of differentiation for us is that we’ve got a very clever patent of capability that allows us in real time to see what’s happening within the business. We figured out how the instant, so in millisecond, a transaction completes within the business. Any dashboard or report that you’re running against our platform will actually reflect that. So, it’s literally what’s happening in the business right at this moment is visible to the customers from the data platform.

Those are the four areas in which I think we’re quite unique and certainly those are the areas that customers come to us and tell us that the reason they’ve chosen our technology is because of one or a multitude of those four factors.

Daniel Newman: Well, I’m so glad you brought all that together for me too and you actually reminded me because I just gave three earlier, then I only delivered two, but I said the third was ease of use, and ease of use does matter. Now, again, across the spectrum of data platforms, where there are the more self-served all the way to the highly complex, need to be a long PhD in data science to use this thing, I do think we are penduluming towards that citizen data scientist and towards that no-code/low-code.

Of course, that’s the generative thing. Everyone keeps talking about that’s new, but low- and no-code has been doing the generative thing for a long time. That’s actually what was happening all the way around, so I appreciate you running me through all this. I only have a couple minutes here left with you, and it’s been a lot of fun talking to you.

With your great experience, all this customer reference that you have, I would love to end with a little bit of getting your advice. We have a broad listening community, a lot of IT leaders that are probably sitting in those boardrooms or working for someone that is sitting in those boardrooms, that is being mandated to get your data right, drive these AI projects where LTP and data platforms are critical to the outcome, how are you advising? What are some of the advice that you’re giving right now to your customers to be… Beyond just to get your data straight, is there anything that you would really say is cogent that you’re telling everybody right now, knowing what you know about where we’re at in the data life cycle?

Emma McGrattan: Definitely getting the data right, understanding the data, and providing mapping capabilities. I didn’t mention that before now, but one area in which we see a lot of success in our customers is providing the ability to map data from different sources. For instance, you could be using NetSuite for CRM… I’m sorry, NetSuite for ERP, sales force for CRM. And they have different definitions of your customers. You may have something else that’s coming in from social media stream, and you need to be able to bring all of that together and to say, all of this information relates to this particular customer or individual.

And so, being able to understand the mapping, to be able to differentiate that James and Jimmy and Jim are the same person. And so, providing all of those capabilities within our platform is something that customers appreciate in that whole journey to cleansing the data and making it easy for consumers of data to be able to trust it. So, that’s something that’s incredibly important.

We’re talking to our customers about preparing the data for AI and preparing the organization for AI is incredibly important. Making sure that there’s no chance of any private information leaking, any confidential information leaking, making sure that your employee base understands some of the implications of using some of these tools, and that the exposure that an organization could have if either customer private information or if corporate IP is leaked through these systems could be quite catastrophic, so that education’s incredibly important.

We are seeing some customers that are already declaring success with AI, and they’re doing that based upon the fact that for some people, AI is just artificial intelligence as a computer made a decision for me. And that maybe, when we look at things like predictive use cases and predictive analytics, you could say it’s an AI use case. We’re definitely seeing some customers already declaring success on AI. But to me, there’s a lot of fun to be had over the coming years with generative AI.

I’m very excited by some of the work that I’ve seen, both from a technology perspective but also as a geek. And I just love… I’m a consumer of all the latest technologies, but I think in terms of our customer base, they’re taking it slowly, and they’re being very cautious. They’re making sure that they’re not exposed, and they’re making sure that they’re meeting the needs of the board, they’re meeting the directives from the board, but they’re doing it in a way that’s not exposing the business.

Daniel Newman: I think that’s really great. And Emma, I want to just say thank you so much. Lots of good insights. It’s good to learn about Actian and the company’s products, services, differentiations, and migration to a platform. And also, hearing because you do have those customer interactions so regularly, really where customers are at.

Because, again, as someone that speaks to the vendors every day, I know the pressure that’s on everyone to get that AI story out, get that generative narrative out, but there’s also so much fundamental work to be done to prepare a company to utilize its data at scale, to deliver everything from insights that can drive product development to customer experiences, to running back office systems. And it seems you guys are well attuned to how that’s going, and I’m really excited to continue to follow your journey.

Emma McGrattan: Thanks so much, Daniel. It was a pleasure talking to you today.

Daniel Newman: All right, everyone, there you have it. A lot of fun here on the Futurum Tech podcast. Hit that subscribe button, join us for all of our shows. I hope you have enjoyed the conversation today. Come back soon. See us often. Bye-bye for now.

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.

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