As the 21st century becomes increasingly data-driven, organizations turn to data and analytics to make faster, more informed decisions. However, the sheer amount of big data can be overwhelming, especially without a solid data strategy in place.
Enter data analytics — the science that uses complex processes like AI to analyze data and assist organizations in making faster and better, data-driven decisions that drive efficiency and profitability.
According to Forrester, data-driven companies are almost 60% more likely to achieve their revenue goals than those companies that don’t rely on data.
In this interview, we speak with Hypercube Consulting Director Adam Sroka to discuss addressing the need for organizations to add data science to their business practices. He also discusses how they can build a solid data strategy and high-impact data teams.
Who Is Adam Sroka?
Adam Sroka currently serves as the Director of Hypercube Consulting. He is an experienced data scientist and AI leader on a mission to help organizations unlock value from data and make better business decisions. Adam also worked as Head of Machine Learning Engineering at Origami Energy, a leading independent energy data platform, providing advanced SaaS applications for asset owner-operators and energy services companies.
Can you tell us about your career beginnings and experience working as a research engineer and data scientist? How did you become interested in data science?
Adam Sroka: I did an industrial doctorate doing computational modeling and optimization research for high-powered lasers. Towards the end of that time, I built a simple reinforcement learning tool that helped design novel laser systems and it was incredibly powerful for the use case at hand. I enjoyed the data and machine learning aspect of that project and wanted to explore it as a career. So, I shortly went out to start my first data science role.
Why do you believe data science is one of the most sought-after careers today, and what does it take to excel in the role?
I think it’s unfortunate how much attention and allure data science has. The “Sexiest Role of the 21st Century” article has drawn a lot of hype and attention from senior executives, investors, and aspiring professionals just getting into their careers. The gap between the examples in the press that get lots of attention and the reality of what’s achievable for most organizations is huge. Many false starts lead to frustration and pain all around.
That said, it’s a great career if you know what you’re getting into. If you just want to build complex analyses and machine learning-powered products all day — then seek out high-maturity organizations and hone your math, stats, and fundamental scientific skills. If you’re lucky, engineering will be taken care of for you. For the vast majority of organizations though, you’ll likely land in an environment where getting your hands dirty building data pipelines and production rising your ML systems are a significant part of your role.
Wherever you end up — curiosity, adaptability, and a willingness to learn will be key to success.
Why should organizations add data science to their business practices?
Most organizations want to be “data-driven” but they don’t know what that means or how to get there. Adding data scientists with deep technical skills and a commercially focused mindset will help you move along that journey. You’ll begin to use data to drive decisions and not just reinforce thoughts and convictions you already had.
Why do many companies struggle to extract the full value from their data despite technological advancements like AI and ML?
It’s hard! There’s a huge technical barrier to being able to do this stuff well. The foundations just aren’t there for most organizations and they try to run before they can crawl.
Even when the technical hurdles are conquered there’s a commercial element that can be hard to overcome. Knowing where best to put your effort or how to align the technical possibilities with the wider business strategy and value centers takes a lot of experience from technologists and business leaders alike.
Finally, the human side can foil even the best data team. If you can’t communicate the importance of your work or manage expectations well you’ll soon struggle.
As a seasoned data and AI leader, can you offer some advice on how businesses can build a solid data strategy and high-impact data teams?
I write a lot about this specifically online, on social media, and in my newsletter. It depends on your specific context — there’s no silver bullet.
If you’re starting your journey and this is all new, reach out to people that have done it before. That sounds self-serving coming from a consultant but it’s the best route to success, learning from the mistakes of others. The community is great and you’ll get a wealth of free support and guidance.
If you’re making hiring decisions it’s always best to get someone that can understand the business context and start to build the foundations — aligning the technical effort and implementation with the business value. Don’t jump straight to the high-profile projects, prove some wins and build confidence first. And don’t underestimate the cultural factors at play!
You are currently working as the director of Hypercube Consulting. What inspired you to start Hypercube Consulting, and what are the company's primary goals and values?
I’ve seen so many companies get mis-sold on the promise of AI and Big Data or start with the best intentions but struggle to make any real progress. I started Hypercube to share what I know and build robust, scalable data and analytics solutions that unlock value for my customers, growing their knowledge and capability in the process.
As for goals, we’re strongly focused on the energy sector. We’re all passionate about the positive impact that can be made there and there are huge inefficiencies that data can help fix. It’s a complicated sector that we know well and have years of experience in, especially where we’re based in Scotland.
How do you ensure that Hypercube Consulting stays ahead of the curve and continues to provide innovative solutions to clients?
I do this by deeply listening to our customers and focusing on the pragmatic, simplest solutions that let our customers grow with us during each project. These things are partnerships. Every organization and project is unique and only by working closely with the experts within an organization can we help guide them to the best tools and approaches to help them unlock their data and analytics.
We understand the importance of small bets to minimize risk while putting tangible deliverables in the hands of the end users to iterate and improve upon until the desired outcomes are achieved.
What are the biggest challenges facing businesses in your industry, and how is Hypercube Consulting working to overcome these challenges for your clients?
The energy sector is vast and the data requirements are changing rapidly. Many organizations are trying to build their own data and forecasting platforms from scratch or are working with decades of data of varying quality. We want to empower these organizations to do what they do best by giving them the tools and approaches to get back to their day job. Most organizations only build a data and analytics platform once, we’ve done it dozens of times.
Can you share one successful project where you helped an organization transform the way it works thanks to data science?
We’re currently working with InterGen, an energy provider responsible for 5% of the total UK’s energy generation. We’ve rebuilt their data strategy from the ground up and deployed data scientists, data engineers, and BI developers to quickly deploy a modern data platform and help them migrate to the cloud.
We’re now in the final stages of upskilling their internal teams so they can take full ownership of the new solutions. We have automated data pipelines, machine-learning powered forecasting of the UK energy markets, and a data strategy that will enable them to make the right hiring and technical decisions in line with their business strategy — without reliance on any third party.
