How is your organisation using data and analytics to support the corporate vision and purpose?
Domino is the open, industrial strength platform for organisations that have large teams of code-first data scientists. It provides centralised data science infrastructure that lets data scientists run their existing workloads in one place. We take their existing work - the tools, languages, and data sources they use - and centralise them.
With Domino, data scientists can research faster because we handle the devops distraction for them. In doing so, they can automatically track activity to gain more visibility into their work and progress toward corporate goals.
2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?
The switch from in person to virtual events was the most obvious impact to our field activities in 2020. The biggest casualty of which was our Rev3 global data science conference - an important annual event that provides an opportunity to engage directly with both our users and the wider data science community. We were able to switch a lot of the speakers and content online and delivered a series of bite size webinars throughout the year.
Creating the Data Leaders Executive Lounge with Dan Harris was a great success. What would normally have been a series of executive briefings, morphed into an online community of data leaders coming together on a bi-monthly Zoom call for an expert guided tasting experience, talks from a range of guest speakers and ample networking opportunities. Some of the experiences provided a great ice breaker and certainly enabled some lively discussions.
The concept has proven so successful, it’s been adopted by our team in the US and rolled out globally to 200 data leaders.
Looking forward to 2021, what are your expectations for data and analytics within your organisation?
If 2020 showed data science was maturing, 2021 will see that trend accelerating. This year will be less about a single data scientist pushing a model to production and more focused on enabling growing teams of data scientists to deploy and maintain a great many models.
At the heart of what we’re doing is shortening the time to value for data science teams. By improving data science efficiency through optimisation, reproducibility and operational ease, we help data science leaders industrialise their work and communicate the value it creates. In 2021, we’ll release a raft of features which will improve on an already great platform.
Is data for good part of your personal or business agenda for 2021? If so, what form will it take?
Domino supports many academic institutes by providing its software free of charge to make it easier for students to get access to the environments and runtimes they require, work collaboratively and build their experiments. We also work with consulting partners who do diverse “AI for good” use cases, such as helping conservation efforts on the Safari Plains in Africa.
What has been your path to power?
I joined a healthcare company in the late 1990s, primarily as a software developer, but found the volume of data we were collecting to be fascinating. This led me down the line of analysing and accessing new insights and then starting to apply different probabilistic models in a way that helped us better predict and understand demand for services. I fell in love with problem solving through the use of data and have been involved in it ever since.
Now I find myself combining a mixture of keeping skills sharp, so I can still operate at the coal face, and my real passion for helping executives understand how to get the best out of their analytics and data, and how to build evidence-based change programmes to leverage data science.
What is the proudest achievement of your career to date?
Creating a model as part of some charity work with a mental health organisation that helped intervene in at-risk childcare cases and build the support layer and right steps to make a very real impact on individuals and their families. It was a project spanning many years that we got to see some very real positive results for individuals.
Tell us about a career goal or a purpose for your organisation that you are pursuing?
Creating a world in which data science is better understood, that we’re not sucked into the marketing hype behind AI and machine learning and more so that we’re better able to equip these capabilities in a disciplined way that makes true business value for organisations. We provide the platform that enables organisations to build successful data science products - that’s different from projects that are code, data and a result - but more the change management programme, the tracking, the knowledge management and the deployment to end users in a way that lets them impact the business with better decisions.
How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?
At an industry level many organisations are still formulating their data science strategy. I’m often surprised at how few data scientists are employed at large enterprises who look ostensibly data-driven. Companies who have embraced models at the core of their business are much further along their journey to AI and have the potential to generate substantial advantage over their more cautious peers. In each case where models are adopted at scale, there is strong executive support from the business - having a forward-looking board sponsor seems to be the differentiating factor in unifying data and analytics to business outcomes.
What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?
Building an organisational culture that can implement evidence based change is essential in getting the best out of data. This requires a combination of data literacy training, particularly on how models work, how to use probabilities and how to better influence decision-making.
On the flipside, not treating analytics initiatives as projects that sit stale once completed is important as data, much like the real world events it tracks, is not static. Therefore, being able to update pieces of analysis and models on a regular basis becomes critical in driving true change and results. Embedding data-driven change into the way that team meetings and one on ones are performed also helps reinforce any new insight that could become transformative within a business.