ROBIN SUTARA - Field CDO at Databricks on Talking AI enablement at the edge

“I'm probably the most non-traditional technologist you'll ever meet,” Robin tells us. With academic achievements including a Master of Law in Intellectual Property and a career that began with a non-data role in the US Army, Robin has since risen to the role of CDO at Microsoft UK and now is Field CDO at Databricks, the company that’s on a mission to simplify and democratise Data and AI, helping data and AI teams solve the world’s toughest problems.

It’s clear her experience is as broad as it is deep, making it little wonder that Robin’s been recognised in the DataIQ 100 Enablers 2023 and recently our Digital Edge Data & AI Leadership Award, recognising her exceptional impact, innovation and leadership.

Though very deserving of this award, it’s something she wasn’t expecting, “I have to admit, it was a total surprise. The UK community has a very strong data and AI community across the entire ecosystem and so for me, it is a complete honour to be recognised. I know all of the nominees this year were completely amazing, so I'm super honoured to be recognised in this way.”

We set out to discover Robin’s take on what’s driving successful digital transformation, any shifts she’s seeing in how enterprises are approaching data strategy today and how she’s supporting organisations across continents to execute against their data and AI strategy. Plus, she shares some of the lessons she’s learned as a woman rising into leadership roles and how she’s helping others on their way.

In Partnership with KPMG

An impressive career drivingdigital transformation

Robin brought her 23 years of experience from Microsoft into her role at Databricks, where she’s been working for the last three years – first based in the UK, now in the USA.

At Microsoft, her first role was delivering IE5 support on Windows 3.1. Having continued her career journey across multiple technical and business roles, her final positions there were Chief Operating Officer of Azure Data Engineering and Chief Data Officer of Microsoft UK, where she supported the UK leadership team to be more data-driven while also supporting their UK customer base.

Now she’s travelling across the world with Databricks in a role that sees her, as she says, “Working with some amazing organisations, helping them figure out how to tackle their data and AI strategy and executeagainst that.”

“Databricks is a very technical organisation and we have a great technical platform. But oftentimes I find that organisations don't necessarily always struggle with the technology – it's often people or process, operating models, organisational design. AI enablement is a big conversation these days, and how do you do that in a governed, controlled way?”

Supporting organizations in Europe and the Americas, she’s able to bring a more global perspective to their many customers.

Robin continues, “Databricks is on a phenomenal growth scale right now. I'm super fortunate that we have more than 20,000 customers that are using Databricks platform to solve their data fundamentals on how do they make sure that they have a single source of truth that their organisation can build their business insights and analytics on top of, as well as their experimentation around AI as and the traditional Machine Learning AI solutions that they're integrating.”

Over her career, she’s developed a deep knowledge of what truly drives successful digital transformation. And while she finds most organisations focus on the technology, for many of the organisations she works with, what’s central is the people and the process.

Right now, a lot of Robin’s conversations are based around this. She tells us, “Lately, I am spending a lot of time helping organisations think about ‘How do you define what an AI enabled workforce means?’

“And that doesn't mean we treat everyone the same when it comes to AI, so how do we really start to think by persona and by domain area of expertise? What is the expectation for them to use and leverage AI to make them more productive?”

While workforces may be concerned about loss of roles, this isn’t something Robin is witnessing, ”What I'm actually seeing is organisations figuring out how to do more with the same number of people, not how to do more with less.

“The ones that are being the most successful have placed their focus on how they make sure that they're taking the people in the organisation along on that journey. How do they reassure them that it's not displacement roles? And how do they make sure that they are meeting them where they are based on their current maturity around data and AI – and take them on the transformational journey without it being focused just on technology and tools?”

Creating a data centric mindset

The people and culture aspects of data transformation are key but can be challenging too. We wondered how, in her role and with her experience, Robin is supporting leaders to embed a truly data centric mindset across their teams.

She answers, “I think some of it is traditional change management. I think for many organisations, it is thinking about how you are designing your organisation in order to be able to empower that?”

While there’s much talk  about democratisation, Robin thinks it’s more about enablement, “It's not necessarily forcing people to change, but how do we make sure that you are tying it to actual, tangible business outcomes so that they understand and it creates a desire in them to change?

“For example, I've spent the last week or so speaking to energy companies across the US and Canada. And that's an industry where hundreds of years’ worth of data assets exist across an organisation.

“So how do we get them to attract net new talent, which I think is very interested in using AI to improve processes to minimise carbon footprint and achieve those societal, organisational and commercial benefits?

“But a lot of it is bringing the existing workforce along on the journey. How do we make sure that data teams are sitting with the business so that they understand the day in the life of an electrician or a nuclear engineer or whatever it might be, so that we stop giving this high level perspective from the centralised data team about what the business needs to do to change?

“How do we actually get to that closer integration? So, I'm talking to most organisations about what they think about AI enablement on the edge? How do we make sure that we have analysts and data scientists that are in their domain area of expertise – for example, they were a nuclear physicist at some point or they can do that translation layer between the two.”

Robin is seeing many industries thinking about how to add this expertise to their data teams to complement the traditional STEM role. She points out, “You need that STEM talent and those capabilities, but how do we up-level some of these domain area of expertise too so we can have a community that exists at both of those resources, so that it's really clear line of sight on the return on the investment as well as driving that desire for those roles to culturally want to change their existing regular processes?”

Spotting shifts indata strategy

With Databricks at the heart of the data and AI ecosystem, Robin has a vantage point from which to see how enterprises are approaching data strategy today and any trends that are occurring.

She’s noticed many are still focused on the fundamentals. Conversations often centre on how to get data ready for AI, and how to make sure the right governance structures and policies are in place so the organisation can innovate at the edge in a controlled way that minimises risk at a time when regulatory and legislative requirements for AI and data are still shifting.

Robin expands on this, “I think many organisations are looking at it in a way that says, ‘How do we make sure that we're meeting the fundamental ethical and moral requirements that we have as an organisation? And then how do we make sure that what we're building today has explainability, transparency – the basic fundamental governance structures that we know will be in place whenever regulatory or legislation continues to evolve?’ I think that is a global trend.”

She notes there are two schools of thought around AI, “There are people who say, ‘There’s just this big hype around it’ and there are others who say, ‘Nobody’s doing it’. And while people who read that MIT study on the volume of failures that we’re seeing in AI experimentation were looking at it quizzically, I’m actually not seeing that type of volume.”

Instead what she’s seeing is that most organisations she works with are actually at scale in production, rolling out AI solutions Robin shares, “Many of them are built on traditional AI, which has been around since the 70s.

“I've seen lots of organisations leverage the capabilities of generative AI and agentic systems to solve back-office process issues – things like internal policy aggregation and summarisation, things that they can very much control. I do see organisations actually rolling that out at scale globally. I think there's lots of opportunity for us to continue to see the evolution of that.”

“But the majority of the focus is how do we make sure that we're getting our data and the fundamentals and foundations in place? How do we continue to drive the existing AI and traditional machine learning use cases that we were already executing across the organisation? And then how do we make sure that we're minimising the risk and continuing to experiment as we're learning more and more in this generative AI, agenticAI future that we see in front of us?”

While these fundamentals including breaking out of silos, modernising, ensuring that the right governance structures are in place and enabling the organisation are something many organisations have in common globally, the legislative and regulatory requirements that exist around AI and data clearly vary for the organisations Robin works with based on their geography.

These differences can impact how organisations approach AI maturity and data-driven decision-making while working to comply with whichever requirements apply to them.

“I think that's where I see the biggest differences, how they're enforcing governance and policy to be able to support the legislative requirements. But in my opinion, when you start to extrapolate it, the fundamental requirements are the same.

“Do you have transparency? Can you do end-to-end lineage? Do you have explainability – can you actually explain to legislators, even if legislation doesn't exist today but will tomorrow, what transformation has happened? What model do you apply to it? What weightings? What's the algorithmic logic that went behind the AI that you're creating? What data products or AI products did it create? What consumers did it touch? What employees did it impact?

“I think all of that will be fundamental requirements regardless of a region, but right now it is very region-specific on how they're creating their governance structures to be able to support that.”

Leadership in the age of data and AI

With AI now influencing almost every function of business, what does good leadership look like in Robin’s opinion? She spends a lot of her time talking to execs and boards, so she’s in a great position to share what works well.

“There are a couple of things that I've seen that have been very successful. I think for most organisations, where there is a strong innovation culture that's still within the governance requirements for the organisation, I see transparency and clarity from the leadership team.

“I think the ones that are super successful are the ones that say, ‘We don't have everything figured out, but here's what you can do and we'll continue to work on these other aspects to make sure that we're mitigating them in a way that allows us to proceed and allows you to innovate without having to create your own separate governance, platform, technologies, tools, and so on.’

“So, I think for most organisations, it's that transparency between what you're building as a data leader, or an AI leader, or a technology leader and making sure that you're clearly communicating to the business what that's going to look like, as opposed to setting up roadblocks for them.”

And when it comes to minimising risk while enabling innovation, Robin has some specific skills that support organisations to find the correct balance for them.

Dual domain expertise

With a plan to be a lawyer at Microsoft, Robin went back to university where she added a Masters of Law in Intellectual Property to her Juris Doctorate qualification. While her career plans changed, her legal knowledge has been helpful in her data career – particularly when negotiating on technology risk.

“With a lack of legislation or regulatory requirements, I think legal and security officers tend to lean toward risk aversion and put in as many controls, policies and limitations around technology as possible to minimise the risk to the organisation. But oftentimes the business then feels stifled because they don't have the capability to innovate,” she explains.

“I think a legal background gives me a great vantage point to go in and operate as a negotiator between the different vested parties and the governance steering committee within an organisation to help them think about, ‘Yes, there's the possible, but what's the probable when we think about your organisation?’

“Of the 62 risk factors that we know exist according to the Databricks Security Framework (which is also being adopted by NIST) standards, not all of those will apply to an organisation. So how do we make sure that we're only creating limitations and restrictions on the things that absolutely matter to the organisation? And how do we then enable the organisation to innovate where we don't necessarily have to lock it as tightly?”

Some of the softer skills she learned in law school have also proven to be beneficial in data, “If we think about storytelling, executive buy-in, a lot of that is no different than aggregating law and presenting to a judge – it's aggregating data, presenting it to the business, or to a board, or to a CEO.

“I think for my career, it's been super helpful to have that dual technology plus another domain area that I could bring a different perspective to.”

And this isn’t only limited to legal knowledge, as Robin is keen to mention, “This is why I love data, actually. I think there's so much transferability of other areas of study into data and technology. It's super exciting.

“You could be in fashion design and that can help you with user interfaces, or know foreign languages, which helps you think about how to do data science and navigate the different computer languages.”

This is something Robin has experienced not only with her legal studies, but earlier in her career too, which started in the US Army. “I'm probably the most non-traditional technologist you'll ever meet. I did not study undergrad in a technology field.

“My first job was repairing electrical and weapon systems on Apache helicopters. And from there I got into data because we were looking to use Excel to track parts and the maintenance records of the Apaches.”

“There’s no better technology space than data and AI where non-traditional backgrounds are super valuable.

“I think having some level of domain area of expertise, having some knowledge that is non-technical, but you have a passion for technology and you can start to learn technology on top of it – that is where I think the real power is as we think about the future and the opportunity in front of us.”

Welcoming people from different professional backgrounds into data isn’t the only thing Robin is passionate about. She’s also keen to attract more women into data and support them on the way to data leadership positions.

Promoting gender diversity

Robin is heavily involved in both Women in Data^®, which has a 90,000 strong community of women and allies, and Women Leaders in Data and AI.

Robin explains, “I am the chair for North America expansion of Women in Data that we're currently working on as we continue to grow our footprint here in the US, which is a completely different market around DE&I and gender diversity and representation to the UK.”

In addition to this, she was the founding member of Women Leaders in Data and Analytics in Europe, “I continue to maintain that membership here in the US. This is more focused on executive women and how do we get more female representation in leadership roles in data and technology, whereas Women in Data is for women at every level.

“They both have a phenomenal charter of how to make sure that we're driving and addressing the systemic issues that are precluding us from having more diverse representation across every level and making sure that we create that environment.”

Being part of creating positive change is important to Robin, “I always think, if I can do something that will help the next generation be more successful than I was, then I've done something right. If they're fighting the same battles that I fought 20 years ago, then we’re missing the opportunity – I haven't changed the system in a way that actually sets them up for success.”

Words of wisdom

While the journey to where she is today may not have been without its challenges, Robin has learnt some important lessons on the way. We wanted to know what advice someone in her position, with her level of experience, would give to women aspiring to leadership roles in data and technology today? And we got some great pointers.

“There are a few things,” Robin answers. “Throughout my career, I think I was always my own worst critic. There are lots of roles in hindsight that I wish I had applied for so that I could have had a bigger impact, or driven greater business value, or had a bigger influence on some of the thought leadership in the market.”

She explains that she’d talk herself out of these roles because there was one requirement out of the many listed that she didn’t have. Whereas now she realises that these requirements are often a wish list and organisations don’t necessarily expect one person to meet all of them.

She tells us, “Now I really wish that I had just taken a risk on myself and not been my own worst critic to talk myself out of those opportunities. Because I often find that men tend to apply for those roles even if they don't meet all the requirements, and they often get the role because those requirements sometimes are just wish lists of a unicorn candidate.

“So, number 1 is just take the risk. Don't talk yourself out of potential future opportunities because you feel like you have some gap in competency. I don't think there's a negative consequence in risking that.”

Robin moves on to her next piece of advice, “I also think everyone needs to think about their board of directors.

“As you go through your career, as you work through networking and communities etcetera, you will find people that you build a great rapport with – whether it's at the same organisation or a different organisation, they will become your sounding board.

“You have a close enough relationship with them that they know who you are, they know what your superpowers are and where your weaknesses are. They are great for you to reach out to when you're struggling with a problem or you're looking to make a change – and they're also your cheerleaders, so when you have a really bad day, there will be somebody on your board of directors that will talk you off the ledge. And you can operate in that same capacity for them and for others. I think you need that group around you.

“So, I think there's a real opportunity as we think about female communities. How do we get more board of director exchangeability between us to build that out?”

And the last piece of advice is to secure an executive sponsor. Robin explains what this means because earlier in her career, she wasn’t completely clear what her expectations of an executive sponsor should be, “For me, an executive sponsor means someone within my existing organisation that is at a level above me where they are in a closed-door conversation that I would never be privy to, but they are making strategic decisions for the organisation that would result in a net new opportunity for me.

“Typically, I've worked with them in some capacity, so they know my strengths, my aspirations and what impact I'm looking to have and the business outcomes I'm looking to drive. As new roles get created within the organisation, I want them to proactively put me forward as being someone who's the ideal candidate.

“I think as you go through your career, you should always be looking either at your manager level, or across your stakeholders at that same level, or your skip level. And be really clear with that person or persons what you’re expecting from them as your executive sponsor.

“At the level I'm at now, I've actually figured out multiple executive sponsors because they're all in that same conversation with the CEO and I want them all to be putting me forward as they're having those conversations.”

She has some advice about mentoring too. She’s found this has been helpful through her career in supporting her to address specific competencies or capabilities that she wanted to develop, but at first she struggled with what she could offer to any potential mentors.

“Early in my career I really avoided mentorship because I always felt like I had to have a value proposition to bring back for the mentor and I often struggled to determine what that was. Clearly I would get value from the mentor, but I could never figure out what I was going to provide,” she shares.

Now she wants to tell people, “Don't feel like you have to have a value proposition. The value proposition is getting insight, having the conversation. People sometimes call it reverse mentoring. I feel like as a mentor, I get so much benefit from talking to those that are early in their career.

“They have a different point of view, they have a different background than I do. And that for me is really helpful, as well as hearing what their aspirations are, or about whatever it is that they're coming to me to help them to develop.

“I just get excited. It's always nice to talk to people who are new in their career, really excited to be there and passionate. You feed off those conversations just as much as a mentor as you do a mentee.”

Clearly, the journey towards gender parity in data isn’t only about external communities addressing systemic issues or individual women gaining the skills or sponsorship they need to progress within the data industry. It’s also vital that organisations take practical steps to foster more inclusive environments. One way that Robin thinks they can do this is by creating internal networks or communities.

She tells us, “I think every organisation has the ability and capability to create communities around attributes such as gender, cultural, racial, sexual orientation, where people feel like they have a safe space they can go where that attribute is similar to others, where they can bring problems and successes and there is a group within the organisation that can celebrate those with them, or commiserate, or help problem solve – particularly if that then leads to requests for policy change or programme changes within the organisation.

“I think every organisation should be creating those safe spaces.”

Looking back – and forward

When we ask Robin to look back over her career journey so far at what stands out the most, she has a very generous answer, “I think what I'm most proud of is the data and AI community. I remember when I first started, it felt very alone when you were trying to drive digital or data transformation across an organisation.

“I think it’s the phenomenal ability that we have created to come together, to be able to share issues and concerns and problem solve together. It isn't a competitive space, it's a collaborative space. I'm super proud that I've had the ability to participate in that, to meet others across the network in the community and learn from them, as well as be able to provide some level of insight or knowledge.”

As for what’s next for Robin, “Right now I'm really working on how to help organisations create that AI enablement definition. Again, a very people centric approach.“

While the traditional approach has been that everyone has to be AI literate or data literate in some capacity, Robin firmly believes that the level of literacy needed varies widely depending on the persona of the people within the organisation.

She explains this further, “For example, a line worker in an energy company is going to be very different from a finance officer at a global 500 or a global 100. And so how do we make sure that we're not creating generic enablement which either, in my opinion, tends to be a super high level – because a lot of people still think AI is magic – or tends to be very low level where we're getting into just the technology and the platforms and tools.

“How do we start to create the right level of enablement so that people are thinking about the power of data and AI in the context of their role, their persona and how they would actually change these legacy processes? How can they have societal impact? How can they have environmental impact? That sort of knowledge and transformation comes from the people within those roles and functions.

“And I'm super excited that AI is now making it accessible for people that don't necessarily have STEM or technical backgrounds. So how do we now make sure that we're creating the right enablement flywheels, that we're not trying to put everybody into a data science or data engineer or data analyst bucket?

“If you're a power line worker or you're an electrician, for example, AI has the power to help transform the way that you do your job today so that you can do so much more, or you can do the things that you enjoy doing as you continue your career.”

It’s clear that Robin will continue to bring her people-first approach and an incredible level of clarity and commitment not only to her role and clients at Databricks, but also to support the next generation of women data leaders.