Actian’s Emma McGrattan discusses the major challenges of today’s IT landscape and why AI models are only as good as the data that powers them.
Emma McGrattan is chief technology officer (CTO) at US software company Actian. After obtaining a degree in electronic engineering from Dublin City University, McGrattan initially joined Actian to work on the Ingres product line in Dublin as an associate engineer.
“When I started, I made myself the promise that if ever I dreaded going to work, I’d find something new, and here I am 33 years later,” she says.
Over the years, McGrattan has worked on a variety of functions at Actian, including the company’s core analytics, data integration and data management solutions.
In her current role as CTO, she leads the company’s technology strategy, innovation and product development.
“My goal is to position Actian to become a leader in data intelligence and ensure customers, partners and analysts understand what makes us unique.”
What are some of the biggest challenges you’re facing in the current IT landscape and how are you addressing them?
Data is very messy and data ecosystems are very complex. Every organisation we speak to has data across multiple different types of databases and data stores for different use cases. As an industry, we need to acknowledge the fact that no organisation has an entirely homogeneous data stack, so we need to support and plug into a wide variety of data ecosystems, like Databricks, Google and Amazon, regardless of the tooling used for data analytics, for integration, for quality, for observability, for lineage and the like.
At Actian, we acknowledge the fact that they’ve got this great big mess, and we want to help simplify it for them. We believe in the decentralisation of data with strong data governance, allowing the customer to have their data live wherever it naturally fits instead of forcing them to build out a warehouse. As a result, data integration and data lineage become important to understand the full life cycle of the data – from collection or creation to use, storage, preservation and ultimate deletion.
At the same time, traditional centralised data governance models create bottlenecks that slow innovation and decision-making. We believe in federated governance, which combines centralised standards with domain-specific flexibility. The person closest to the data decides who can access it for what use and purpose and so on, as long as it adheres to the organisation’s centralised standards.
What are your thoughts on digital transformation in a broad sense within your industry?
Cloud adoption is causing organisations to rethink their traditional approach to data. Most use cloud data services to provide a shortcut to seamless data integration, efficient orchestration, accelerated data quality and effective governance. In reality, most organisations will need to adopt a hybrid approach to address their entire data landscape, which typically spans a wide variety of sources that span both cloud and on premises.
What big tech trends do you believe are changing the world and your industry specifically?
It’s an exciting time in the industry as I view the impact of AI today as equivalent to when the world wide web went mainstream 30 years ago – it’s a complete paradigm shift.
I am most excited about giving customers confidence to run their AI initiatives across all of the data that their business relies upon. This is something that’s top of mind for all of us in the data world. Most of the AI projects that we’re seeing deployed to production don’t include the crown jewels of the business, which is the actual customer data. Companies are nervous about getting AI wrong and the financial and reputational damage that could be caused by getting it wrong with customer data or data that’s subject to regulatory compliance requirements like GDPR could be catastrophic.
‘Security has to be a core part of the culture’
Another major AI adoption challenge is the time-consuming process of building a semantic layer that understands everything about the business, including the different domains, whether it’s finance or marketing or accounting or any other department. If done in the traditional manual way, discovering and tagging all of the data sources across an organisation could be a never-ending project. People don’t have enough time or bandwidth – or in some cases patience – to make sure that everything is tagged appropriately.
What’s required is an automatically generated rich semantic layer that enables direct conversations with the data. That is one of our goals – enabling a business user to have a conversation with the data and to be provided with rich answers inclusive of visualisations like graphs, charts and trends.
As AI becomes more central to business operations, one truth remains clear: even the most sophisticated AI models are only as good as the data that powers them. I cannot stress enough the importance of data quality, which I view in two ways. First, there are objective factors like accuracy, completeness and timeliness. Equally important is data trust and purpose, which is much more subjective. Understanding these context-dependent quality requirements is crucial to preparing data for successful AI implementations.
What are your thoughts on how we can address the security challenges currently facing your industry?
Data security has become a trust issue, not just a tech issue. With AI, hybrid cloud and complex supply chains, the attack surface is massive. We need to design with security in mind from day one – think secure coding, data-level controls and zero-trust principles.
For AI, governance is critical, and it too needs to be designed in and not an afterthought. That means tracking where data comes from, how models are trained, and ensuring transparency and fairness.
Security has to be a shared responsibility and a core part of the culture, not just a compliance box to tick.
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