Cybit Announces Appointment of James Hunnybourne as Executive Chairman
Newcastle Upon Tyne, UK - 4 March 2025 - Cybit, announces the appointment of James Hunnybourne as its new Executive Chairman.
Read articleA CTO Perspective: Rob Hankin, CTO, Cybit shares views on why data-driven value creation is the secret weapon to growth
According to the findings of the UK Business Data Survey 2024 only one-fifth (21%) of UK businesses in 2024, that handled digitised data, analysed data to generate new insights and knowledge.
However, in today’s rapidly evolving digital landscape, data has emerged as a critical asset for driving business growth and innovation. Rob Hankin, CTO at Cybit, shares his insights on why data-led value creation is essential for companies aiming to thrive in 2025 and beyond.
Understand your commercial growth drivers
To get ahead, businesses must first understand the key drivers that influence their commercial success. These drivers include market trends, customer behaviour, competitive dynamics, supply chain performance and operational efficiencies. So, it all starts (and potentially never stops) with data; where it is, what it looks like, how it is used and accessed, how trustworthy it is and how it is secured and plus who holds the accountability for data management? Obvious perhaps, but how many leaders across businesses and organisations can actually feel confident of a positive answer to one, some or all of those questions? In fact, more crucially, how many can confidently say they have a data strategy at all.
Quantify your business value
One of the most significant challenges businesses face is quantifying the value derived from their business initiatives. Smoke and mirrors often comes to mind and yet, by employing a data led approach, clear metrics and KPIs can better measure the impact of commercial and business growth strategies.
Discover the crucial role of data-led decision making
Data-led decision making is the cornerstone of successful businesses in 2025. Companies that harness the power of data to inform their strategies are better positioned to adapt to changing market conditions, innovate, and stay ahead of the competition. Conversely, those that fail to embrace data-driven approaches, risk falling behind.
6 Common data challenges and solutions
Exploring the 6 most common data challenges experiences:
- Data Silos: Fragmented data across different departments and IT systems can hinder comprehensive analysis.
- Data quality: Inaccurate or incomplete data can lead to misguided decisions.
- Data integration: Integrating data from various sources can be complex and time-consuming.
- Data security: Protecting sensitive data from breaches and cyber threats is non negotiable.
- Data governance: Establishing clear policies and procedures for data management is essential.
- Data literacy: Ensuring that employees have the skills to interpret and use data effectively.
A pragmatic way forward is to a) implement a data strategy that involves assessing the current state of data management, identifies gaps, and provides a roadmap for improvement and b) to work with an AI and Data Analytics expert who can help.
But isn’t AI going to do this all for us? Maybe.
5 key considerations
AI is rapidly becoming the norm rather than the exception, but when integrating AI into a data strategy, organisations should be mindful of the following:
- Data quality: AI models rely on high-quality data to produce accurate results.
- Bias and fairness: How to ensure that AI models are free from bias which could just repeat past mistakes and promote fairness. The reason for this is because AI bias is often seen as a diversity & inclusivity focused thing. AI is designed to be biased for good reason. The business challenge is making sure it doesn’t tell you to keep making the mistakes of the past as that’s what you have told it. I.e. Ask it to write a tender document but you gave it all the historical tender docs including the ones you lost!
- Scalability: Investment into AI solutions must be scalable to accommodate growing data volumes and business needs.
- Ethical considerations: Organisations must consider the ethical implications of AI deployment, including privacy and transparency.
- ESG impact: AI requires more computing power than standard analytics so ensuring that the benefits of the output justifies the resource cost is essential. This is a big business driver with CO2e being published in audited accounts now. Not something always considered at junior levels of the business.
Finally, 4 questions to ask yourself
- How much value could be added by accurate and personalised customer data as opposed to the stand-alone value of your proposition?
The higher your knowledge of your customer, the greater the chance that you will be relevant to them. Let’s look at a business industry where the value of customer data is very high:
Car supply chain: A leading B2B distributor sought to improve their customer experience to be the first choice, every time in a landscape where time to service often outweighed cost performance. By analysing their customer base’s buying habits and enriched with economic data, they were able to predict where demand volumes would be greatest and the likelihood of more expensive brands being preferred. This has led to greater stock optimisation, improved delivery times and subsequently, the growth of the business as the industry leader.
- What are the mistakes to avoid ?
Beware the danger of collecting frivolous data in that it can pollute good data and make it harder to find what the good insights are and make processes to get data harder. The point here goes back to the imperative of having a data strategy in the first place and as a result, being better informed as to what and how data needs to inform core business decision making.
- What is the competitive advantage of real-time, AI-data driven insights rather than relying on retrospective reporting?
In this rapidly evolving business landscape, the ability to make informed decisions swiftly is the difference between having the advantage vs missing out on opportunity. Traditional retrospective reporting, while valuable, often leaves organizations reacting to past events rather than proactively shaping their future. As a Chief Technology Officer and with years of experience within the data analytic industry, I advocate for the transformative power of real-time, AI-driven insights. With the right insights, business leaders can unlock competitive advantages, drive better efficiency, and outcomes.
- What industry sectors can benefit the most from a data driven strategy?
It is easy to reply in just one word. All.
However, to consider a few key ones:
Housing and Construction: Real-time data insights can revolutionise the housing and construction sector by enhancing and also validating project management, safety, and efficiency. It can predict potential delays, optimise resource allocation, and improve safety compliance by analysing data from IoT devices and BIM models. This proactive approach reduces costs, minimises risks, and ensures timely project completion, giving companies a significant edge over competitors who rely on retrospective reporting.
Manufacturing: In manufacturing, AI and data driven insights enable predictive maintenance, real-time quality control, and supply chain optimisation. By anticipating equipment failures and optimising production schedules, manufacturers can reduce downtime, improve product quality, and respond swiftly to market demands. This agility and efficiency lead to cost savings and higher customer satisfaction, setting AI-enabled and data strategy led manufacturers apart from those using only retrospective methods.
Legal: For the legal sector, think about the benefits of being able to streamline case management, enhance legal research, and improve decision-making. AI tools can analyse vast amounts of legal data to predict case outcomes, identify relevant precedents, and automate document review. This not only saves time and reduces costs but also increases the accuracy and effectiveness of legal strategies, providing a competitive advantage in a fast-paced legal environment.
Health: The UK NHS is increasingly under pressure and underfunded but real-time AI insights can transform patient care, diagnostics, and operational efficiency. AI can analyse patient data to provide personalised treatment recommendations, predict disease outbreaks, and improve hospital workflows. This leads to better patient outcomes, reduced operational costs, and enhanced patient satisfaction.
Policing: Another environment that is under chronic performance pressure but where AI-driven insights in policing can help to pre-empt and even prevent crime, so that resources can be better allocated and deployed to the benefit of community safety. Predictive policing models can identify crime hotspots, optimise patrol routes, and analyse patterns to prevent crimes before they occur. This proactive approach improves public safety, builds community trust, and allows law enforcement agencies to operate more efficiently and effectively compared to traditional reactive methods.
In conclusion, by investing in real-time, AI-driven insights, organisations have the opportunity to go from reactive to proactive decision-making and by doing so, delivering better use of investment as well as a more agile response to market factors and opportunities.
About Rob Hankin
Rob Hankin (CTO) has 25 years of IT leadership, including a decade in a multi-national manufacturing environment. Over the past 15 years, Rob has worked with leading technologies, vendors, and distributors including Microsoft and AWS. He leads Cybit’s technology partnerships, supporting private and public sector customers across the globe. Rob’s expertise is highly valued by key industry players, where he has served on Partner Advisory Councils and Product Advisory teams. As a trusted advisor, he helps organisations from small enterprises to global industry leaders unlock opportunities through technology.
