Chief Content Officer,
Techstrong Group


In the Digital CxO Insights Leadership Series video Mike Vizard talks to Jitesh Ghai, executive vice president and chief product officer for Informatica, about how artificial intelligence (AI) will advance digital business transformation by democratizing data loading and integration.



Mike Vizard: Hello, and welcome to the latest edition of the Digital CxO Leadership Insights series. I’m your host, Mike Vizard. Today we’re with Jitesh Ghai who is executive vice president and chief product officer for Informatica. And we’re talking about the democratization of data integration and data loading using AI. Hey, welcome to the show.

Jitesh Ghai: Mike, thank you for having me. Pleasure to be here.

Mike Vizard: What exactly is happening with AI and data integration and data loading? Because we have relied on data engineers to do a lot of this work over the years, and they’re expensive. And sometimes we have them do things that are, quite frankly, beneath them. So what’s what’s the impact is AI about to have on all this?

Jitesh Ghai: Yeah, you know, great question, Mike. Our point of view: We all understand and know that for trusted AI outcomes, you need trusted data. At Informatica, we embrace that philosophy but critical to delivering trusted data, you need AI. So it goes both ways. And what we at Informatica are doing is leveraging the power of AI to ensure our customers have access to easy, accessible trusted data for the purposes and use cases that they need. Whether it’s driving analytics, whether it’s, reporting, whether it’s driving data science and AI/ML outcomes. So from that standpoint, organizations want to be more data driven than ever before. There’s more users in organizations from deeply technical to non-technical users that want to have access to data; that want democratized access to data. And our point of view is, well, if you want to democratize access to data, you need to democratize data integration; you need to make it easy to discover, easy to access, easy to use. And by doing so, you’re enabling skill sets of all types, to connect to data, process data, and drive their data outcomes in the line of business, in IT, across the enterprise. Central to that is AI, which is our AI/ML Engine, CLAIRE that makes it simpler to do all of this that automates all of this, abstracts away the complexity of doing data integration. Equally important is ensuring that it’s done in a frictionless manner. We’re using our free offerings, and when appropriate, upgrading to our pay as you go offerings with a simple swipe of a credit card.

Mike Vizard: Do you think in some ways that the data engineers have been a bottleneck to integration because we do require their time and expertise and if we can kind of maybe elevate this task in a way that allows them to go do some things that are more useful we can actually accelerate the number of digital business transformation initiatives we have underway? Because a lot of it is just tied to melding the data together.

Jitesh Ghai: Yeah, you know, I think I agree, but I think it’s not a problem of the data engineers have been resource constrained as much as they are more – data engineers are those, they are more folks that want to participate in engineering of data. They may not be called data engineers, they may be called data analysts, or marketing ad ops analysts or sales ops analysts in the line of business. They might be data savvy business, business analysts, they might be data engineers, enabling data scientists as well. So there’s more folks that want to be able to connect to data, bring disparate data sources together, process it. And we’re making them we’re making them more productive by building a free offering. That enables them to easily with a simple four step wizard load data unlimited. And once they’ve loaded the data, then the curation and processing and bringing together of the datasets to create a trusted, curated data set happens – that’s when you’re not ingesting – now you’re doing what they call ETL or ELT extract, transform loading. And we’re making it simpler for them to now not just load data into their favorite analytics platform, Snowflake, Databricks, Redshift, Synapse, BigQuery, you name it, but also curate the data once you’ve landed it in your analytics platform and get it ready. And we’re doing so with AI power, no code experiences, and it’s free.

Mike Vizard: Free is always good. What makes it possible to do this now? Because of course, there’s always a lot of skepticism anytime you use the word AI. So what exactly is happening here and what gives people confidence in that these algorithms are going to get it right?

Jitesh Ghai: Well, there’s a few things that make it possible. It’s, you know, as with all things, there’s some element of people process and technology. First we’ll start with the organizations, organizations are our are embarking, have embarked on democratizing data. It’s a key initiative across the global 2000. And beyond, where it’s not just data as the remit of the data office or of it. And if you need any data pulled, you have to talk to the data officer; those organizations want the business to be empowered. They want the business to have access to these data integration capabilities. So there’s been a major cultural shift. Equally, the lines of business want access to these capabilities want to be more data driven, and want to be able to access these capabilities as easily as possible not having to rely on submitting a ticket and working with their IT teams to get access to some tools, just they want their integration capabilities when they need them for the data set that they’re looking to work with. And then last, is our multi-tenant AI/ML engine, CLAIRE, which powers over 3000 customers in our intelligent Data Management Cloud, which is understanding deep predictive data intelligence insights from workloads deployed across industries, across use cases, across geographies, and is able to automate the process of building data pipelines, making it simpler for these business users to connect to various sources, we automatically generate and deliver intelligent recommendations of how these data sources should be put together. And then we automatically put the data into the source – the target that the data engineer, the analyst, the marketing ops analyst wants to analyze the data in. So it’s a confluence of cultural change within the global 2000. This appetite for people to want to be more data lead, and self serve data to themselves in the business and being equipped with AI powered data management to make it simple for them to do so. And make it free and frictionless.

Mike Vizard: How do we know that the data quality is there? Because one of the issues we’ve had historically is, a lot of times data gets entered. And it’s kind of sloppy, very few organizations would get a good housekeeping seal of approval for the way they manage data. And we want to empower end users, but we don’t want them just kind of mixing and matching bad data. So how do we ensure that the data has a quality to it? Because that’s typically one of the important functions that the IT team plays in an organization is to make sure that people are not, you know, mixing and matching data and making assumptions that don’t come true.

Jitesh Ghai: Mike, excellent question. And what I’ve described so far as we’ll call it, the tip of the sphere, the sphere of the data journey of the data analyst, the data engineer, or the marketing ops analyst, sales ops analyst journey, which is they want to be data lead. And they want some capability to help them easily connect to a source – Marketo, Salesforce workday, you name it, and ingest that data for analytics. We launched our data loader capability with free unlimited volume in May of last year. And we’ve had over 800 organizations benefit from this free capability. That’s step one of that lifecycle of being data driven of driving analytics. Step two, as I described as now you want to curate the data and that’s where these folks were roadblocked in that they couldn’t bring different datasets together, they couldn’t join them, they couldn’t transform them, curate them to the structure that’s needed to drive analytics. And that’s where they would have to do some sequel hand coding, they’d have to rely on their technical counterparts. And that’s where our no code experience our AI powered experience simplifies the ETL, ELT curating, of bringing together all of this data into a structure that you can run analytics reporting and data science off of. And then last, is while we’re enabling them with these free capabilities at some point, what happens in this lifecycle is these data initiatives become increasingly relevant to the business. They in fact become mission critical to the business as these data lead practitioners in the line of business are delivering more relevant insights that are increasingly actionable. As that’s happening, their need for advanced integration grows, their need for, to your point, data quality grows their needs. For business glossaries and governance policies and a data catalog. And what we have done is through our free offerings with it, with one click, they get, they get access to more advanced expanded capabilities, as their data use cases mature. So while we’ve built out this free capability for data loading, data curation and integration, as this community that we’re enabling, as their work loads mature, they have the comfort of knowing that with the swipe of a credit card, they have access to advanced capabilities to help them clean the data to help them bring together larger datasets, and expand into broader sets of use cases like data cataloging, like data governance type, data quality, and so forth.

Mike Vizard: What becomes of the data engineers and the teams of folks who used to do this work for us? How do they kind of evolve alongside this?

Jitesh Ghai: Yeah, it’s a great question. From our standpoint, we’re in the business of making everybody productive, regardless of skill set. So we have AI powered productive experiences for data engineers that like to hand code, that like to code in SQL or in PI Spark, they equally benefit from our intelligent Data Management Cloud. We equally empower technical users with no code experiences with a graphical drag and drop. And we equally empower non-technical users that further benefit from the simplicity, ease of use, and frictionless experience of what we’re launching, which is our cloud data integration, free and cloud data integration, pay-as-you-go services. So we’re making all of these personas within the enterprise more productive, more data led more empowered, and enabling them to deliver transformative experiences for their respective businesses.

Mike Vizard: So looking down the road, do you think this will accelerate digital transformation initiatives? Because it seems to me just getting the data online is more than half the battle.

Jitesh Ghai: You know, at Informatica, our point of view is you can’t do digital without data. Data is at the heart of every digital transformation initiative. You want to build out a customer system of record with Salesforce? Well, you can instantiate the Salesforce instance. But it’s only as good as your customer data. In that Salesforce instance, we solve that problem. You want to instantiate a human capital management system, you want to populate workday? Well, it’s only as good as your people data. We make that happen. So enabling free fast, frictionless access to integration capabilities is going to make our customers our global data community of practitioners much more productive, making their organizations more productive with digital transformation. And they have the confidence of knowing that as their use cases evolve. They have access to a broader array of advanced data management capabilities, powered by Informatica intelligent Data Management Cloud. We’re extremely excited about all of this.

Mike Vizard: All right, we all know that the thing about machines and AI is it gets smarter every year and maybe faster than we do. What’s next? You know, looking down the road, what do you think we should be anticipating?

Jitesh Ghai: But surely we are at an exceptionally exciting time in harnessing the power of artificial intelligence and machine learning; there’s an immense amount of innovation that’s been happening over the years, an immense amount of innovation just happening over the last six to 12 months around large language models and generative AI as an example. And while what we see in the media is, you know, large language models trained on, on public data sources on various web data, internet data, the immense opportunity for organizations  – the immense opportunity that we’re empowering is bringing the power of large language models applied to enterprise data, applied to Informatica as metadata to make data management more intelligent, more automated, and to drive digital transformation for our organizations faster, helping them build new products and services, accelerate revenue growth, as well as drive efficiencies, all powered by AI. In fact, Mike, I would encourage you and everybody else that’s listening to join us at Informatica World in Vegas in May where I’ll be showcasing some amazing AI powered data management insights.

Mike Vizard: All right, folks, you heard it here. It’s just the very beginning of the journey. Hey, I want to thank our guest for being on the show and sharing his insights.

Jitesh Ghai: My pleasure to be here. Thank you for having me.

Mike Vizard: And thank you all for watching this latest episode of the Digital CxO Leadership Insights series. You can find this episode and others on the website, and we invite you to check them all out. And we’ll see you all next time.

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