From data silos to actionable insights: Innovating within legacy constraints for the digital enterprise
Market View
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Interview with
Sara Butt
THOUGHT LEADER IN DATA STRATEGY AND TEAM DEVELOPMENT
about
Sara
Butt
Sara Butt is a thought leader in data strategy and team development across various industries. Her role involves creating comprehensive data, AI and ESG strategies for organisations, focusing on building and structuring effective data teams. Sara works closely with senior leaders to emphasise the importance of data in organisational decision-making and to establish a clear North Star for data and ESG initiatives. Her responsibilities include laying the foundational elements for data capabilities, both from a team perspective and a technological standpoint, to support overall organisational goals in the evolving field of data analytics and management.
How is your organisation approaching the challenge of extracting more value from its data while working with legacy systems?
The primary focus is on cultivating a strong data culture and development of robust strategies that enable future-thinking and ways of working. This is particularly crucial in government-related organisations where legacy systems and traditional ways of working are deeply ingrained. “Although my organisation is data-forward, there's still an element of fear when it comes to change. Governance plays a significant role, but understanding its importance is key.”
It's about changing the culture and mindset, helping people understand what data can do for them and how it can lead to better decision-making. At the same time, we need to acknowledge the limitations of legacy systems and find ways to work around them. It's a delicate balance between pushing forward with new data initiatives and working within the constraints of existing systems and processes. The goal is to find that sweet spot where we can innovate and extract more value while maintaining the integrity and security that's crucial in government organisations.
When it comes to AI, understanding that it is a tool, just like technology is crucial. To utilise it properly, data needs to be in the right state. Otherwise it’s a case of ‘rubbish in, rubbish out’.
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It's about changing the culture and mindset, helping people understand what data can do for them and how it can lead to better decision-making. At the same time, we need to acknowledge the limitations of legacy systems and find ways to work around them.
What do you see as the biggest obstacles to innovation when dealing with established tech stacks?
The biggest obstacle is always governance. It's about understanding what new technologies mean for the organisation and how to implement them effectively. We need to consider what the transformation will look like and how the organisation will adopt and adapt. It’s about transformation and learning, which doesn’t stop. Another element is that technology strategy should align with the data strategy, not the other way round, as technology is an underlayer that supports data. So technology has to align with the needs of the data teams, and the data needs to align with the business needs. This landscape is ever changing so it has to be scaleable.
Older organisations, like those in government or banking sectors, often find it challenging to adopt new ways of working because they're set in certain cultures and methodologies. Working with new tech stacks and environments can be very difficult in these contexts. That's why adopting data culture needs to start from the core, involving everyone from senior leaders to the delivery side, and even the HR system. Getting HR to understand the importance of data and its role in recruitment and development is crucial. Making sure analysts development programmes are in place and there is a space for evolution from data analyst to data engineer or from data scientist or whatever data requirement is needed within that area, is key. HR is crucial for that transformation piece. Data work can be mundane, so there needs to be an element of motivation and incentives, to help re-engage constantly to avoid burnout. That’s why it has to be at the heart of everything you do because it's so connected.
Can you share an example of a successful initiative where you've managed to innovate using existing data and systems?
During the height of the Covid-19 pandemic, I was a chief data officer for a major government organisation at the forefront of the response. Data was absolutely key – we couldn't have done what we needed to do without it. Innovation was a necessity. We developed algorithms for wastewater projects and smart city initiatives. One particularly innovative project was "cough in the box," which involved building algorithms to differentiate between a Covid cough.
Government systems are legacy, and during that time, a lot of the systems we had to use weren’t equipped to deal with the data being produced in a coherent manner at such a fast pace. That was stressful. It was also a time where new data strategies had to be formed overnight, and people had to change ways of working overnight as well, so it was constant learning and relearning, applying different methodologies and strategies to an ever-changing landscape - test and learn, test and learn, which is what data is all about.
These projects required innovation out of necessity. I think companies often become most innovative when they're pushed into extreme environments. Many organisations weren't tech-ready for remote working but had to adapt overnight during the pandemic. It's unfortunate that it often takes such extreme circumstances to drive innovation, but I believe organisations are slowly learning from these experiences.
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I think companies often become most innovative when they're pushed into extreme environments. Many organisations weren't tech-ready for remote working but had to adapt overnight during the pandemic.
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The future of data utilisation in our organisation revolves around ensuring we're 'data-ready' and cultivating a strong data culture. This involves carefully considering the adoption of new technologies like AI, while being mindful of avoiding technical debt.
How are you addressing the challenge of making data from various sources more accessible and usable across your organisation?
The key is strategic data pipelining – understanding exactly what data is needed rather than pipelining every possible data point. Many organisations feel overwhelmed by data, consuming more and more without effectively using it. This can render a data team useless if it's not producing valuable insights for the organisation.
It's crucial to have conversations about the 'why', 'what', and 'how' of data collection and use. What are you trying to achieve? This approach ensures that you have usable, strategic data going forward. It's not about collecting data for the sake of it, but understanding the purpose behind each piece of data, how it fits into the bigger picture, and how it can drive real value for the organisation. We need to be strategic, focusing on quality over quantity, always keeping the end goal in mind.
How do you envision the future of data utilisation in your organisation, and what steps are you taking to prepare for it?
The future of data utilisation in our organisation revolves around ensuring we're 'data-ready' and cultivating a strong data culture. This involves carefully considering the adoption of new technologies like AI, while being mindful of avoiding technical debt. We're conducting thorough research, producing option papers to compare and contrast different solutions, and examining relevant case studies for our industry. Everything comes with governance and before we take on AI, we’re looking at AI ethics and AI readiness. Are we implementing it because it's the new thing today? Do we have an AI? strategy established which makes sense to implement this? And do we need it for everything?
It's crucial to ensure that any new technology we adopt is suitable for our specific industry requirements. In aerospace, for example, data needs to be accredited, and the requirements are unique. We're very careful about vendor selection, ensuring they understand our industry-specific needs and regulations. We're also considering broader issues like ESG readiness and compliance with evolving regulations.
Looking ahead, location-based data will play an increasingly important role, particularly in managing our carbon footprint and addressing ethical considerations in our supply chain. We're using this data to optimise operations, reduce environmental impact, and make more informed decisions about our global presence.
Lastly, we're focusing on upskilling our workforce to better utilise data from both legacy and new systems. This involves creating a culture of continuous learning and growth, where employees feel empowered to develop their skills. We're implementing workshops, training programmes, and career progression strategies to motivate staff to enhance their data capabilities. The goal is to create an environment where upskilling benefits both the individual and the company, fostering innovation and adaptability in our rapidly evolving data landscape.
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