The journey of data from being a mere collection of numbers to a pivotal business asset has been transformative. Historically, businesses relied on instinct and basic financial metrics to make decisions. However, the advent of digital technologies has revolutionised this approach. The proliferation of data from various sources, including social media, IoT devices, and customer interactions, has provided businesses with a treasure trove of information. This evolution has shifted the focus from mere data collection to sophisticated data analysis, enabling businesses to extract actionable insights and make informed decisions.
One of the most significant benefits of data is its ability to drive innovation. Data analytics can reveal previously hidden patterns and trends, providing businesses with insights that can spur new product development, enhance customer experiences, and optimise operational processes. For instance, retailers can analyse purchasing behaviour to more efficiently tailor marketing strategies and stock inventories. Similarly, manufacturers can use predictive analytics to anticipate equipment failures and reduce downtime, increasing productivity and cost savings.
“Businesses have historically invested in tangible things like data platforms, analytics tools and building data science teams,” Simon Ferriter, CEO of Anmut, told Silicon UK. “Data is often treated like a by-product or an ingredient for these things but is not managed like an asset in its own right. A continuous fire-fighting approach to fixing data, often means limited capital (human and financial) is left to specify and prioritise the data decision makers need, assess the gap between those needs and the data available today, and coordinate changes to upstream business processes to make the data fit for purpose. Extracting value from data is a team sport that requires end to end process thinking, shared outcomes and incentives, and a rebalancing of capital across the operating model to keep data fit for purpose.”
Data provides a competitive advantage by enabling businesses to respond quickly to market changes. Companies that harness data effectively can anticipate customer needs, adjust to market trends, and stay ahead of competitors. For example, in the financial sector, firms use real-time data analytics to detect fraudulent activities and manage risks more effectively. This agility is crucial in today’s fast-paced business environment, where the ability to pivot and adapt can determine success or failure.
As businesses increasingly rely on data, ensuring data privacy and security has become paramount. The rise in cyber threats and stringent regulatory requirements, such as the General Data Protection Regulation (GDPR), necessitates robust data governance frameworks. Business leaders must prioritise data protection to maintain customer trust and comply with legal obligations.
Implementing strong data security measures, such as encryption, access controls, and regular security audits, is essential. Additionally, fostering a culture of data privacy within the organisation through employee training and awareness programmes can mitigate risks and safeguard sensitive information.
Speaking to Silicon UK, Lauren Hendry Parsons, Director of Communications and Advocacy at ExpressVPN, explained how personal data awareness now impacts business decision-making: “There is an increasing shift among both consumers and regulatory bodies towards greater awareness and concern for data privacy. High-profile breaches and scandals, as well as the rise of audacious tactics to capture and harvest more personal information, have driven consumers to think more carefully about what data they are giving away and what it might be used for.
“We also see regulatory bodies taking stricter action – both in terms of the legislation they are putting in place and the fines they are willing to hand out to parties that fail to comply. This trend is likely to accelerate over the coming years. With that in mind, organisations that continue to pursue their own interests ahead of consumers’ data privacy needs are likely to face reputational and legal consequences along with a massive loss of customer trust.”
Data-driven decision-making is a hallmark of successful businesses. By leveraging data, leaders can make decisions based on evidence rather than intuition. This approach reduces uncertainty and increases the likelihood of positive outcomes. Advanced analytics techniques, such as machine learning and artificial intelligence, further enhance decision-making by providing predictive insights and automating complex processes.
For business leaders, the ability to make data-driven decisions is particularly valuable in strategic planning. Data can inform decisions related to market expansion, product development, and resource allocation. For instance, by analysing market data, a company can identify lucrative opportunities for expansion or determine the most promising areas for investment. This strategic use of data ensures that resources are allocated efficiently, maximising returns and minimising risks.
Waseem Ali, CEO of Rockborne, outlined how data drives businesses:
In the age of the customer, personalisation is key to building strong relationships and fostering loyalty. Data plays a crucial role in understanding customer preferences and behaviours. By analysing customer data, businesses can deliver personalised experiences that resonate with their audience. For example, e-commerce platforms use data to recommend products based on browsing history and purchase patterns, enhancing the shopping experience and driving sales.
Also, data helps businesses anticipate customer needs and address issues proactively. By monitoring customer feedback and engagement, companies can identify pain points and implement solutions before problems escalate. This proactive approach not only improves customer satisfaction but also strengthens brand loyalty and reputation.
Data is also instrumental in enhancing operational efficiency and reducing costs. Through process optimisation and automation, businesses can streamline operations and eliminate inefficiencies. For instance, supply chain analytics can identify bottlenecks and optimise logistics, reducing delivery times and costs. Similarly, workforce analytics can improve employee productivity and retention by identifying factors that influence performance and job satisfaction.
In addition, predictive maintenance, powered by data analytics, can significantly reduce operational costs. By predicting equipment failures before they occur, businesses can schedule maintenance activities during non-peak hours, minimising disruptions and extending the lifespan of assets. This proactive maintenance approach not only saves costs but also ensures uninterrupted operations, contributing to overall business performance.
This metaphor suggests that data, much like oil in the industrial era, is a highly valuable resource that drives modern economies. However, this comparison, while catchy, is fundamentally flawed and overlooks crucial distinctions that set data apart from oil. For business leaders, understanding these differences is essential for harnessing data’s true potential without falling into the pitfalls of the analogy.
To fully leverage data’s potential, business leaders must cultivate a culture of data literacy within their organizations. Unlike oil, which requires specialized expertise for extraction and refinement, data’s value can be unlocked by a wide range of stakeholders through proper education and training. Ensuring that employees at all levels understand how to interpret and use data is crucial for fostering innovation and maintaining a competitive edge.
Investing in data literacy programs and tools that facilitate data analysis can transform how businesses operate, enabling more agile and informed decision-making processes. This democratization of data expertise stands in stark contrast to the highly specialized field of oil extraction and refining.
“In the coming decade we’re going to see epic struggles between the rights of consumers, who will demand more control over their data, regulators who currently lack the policy framework and incentives to prevent unfettered data collection usage across borders, and the profit seeking motives of data and capital rich businesses,” says Anmut’s Simon Ferriter. “Sustaining cross-border E-commerce while simultaneously managing the impact of climate change and reducing biodiversity, and protecting consumer rights, is going to require difficult trade-offs. Organisations that leverage trusted data to both tell the story and evidence the net-positive societal impact their business is having, will be the winners.”
The value of data in today’s business environment cannot be overstated. For medium to corporate-sized businesses, leveraging data effectively is crucial for driving innovation, enhancing decision-making, improving customer experiences, and achieving operational efficiency. However, the benefits of data come with responsibilities, particularly in ensuring data privacy and security. By embracing data as a strategic asset and implementing robust data management practices, business leaders can unlock new opportunities and navigate the complexities of the digital age with confidence.
“I think the idea of data as having ‘intrinsic value’ is problematic and is one of the reasons that some businesses – and sectors – lose out. This perspective can cause them to either hoard their data and ‘mine’ it like an extractive resource or leave it languishing in vast unstructured data lakes, where its potential goes unrealised. It’s become popular – but is rather misleading – to compare data to natural resources like oil. Unlike oil, data can be used repeatedly and often gains value the more it is used and shared, often in combination with other data. In fact, the value of data in the business landscape is often discovered when access to data is opened up – either as open data or as shared data among trusted parties.
“Organisations, including some big tech companies have business models that provide others with access to their data. For example, diffbot builds a knowledge graph, a curated machine-readable data resource which other organisations can use against a fee. Reddit – the online community – licences its content to AI companies. Access to high-quality data for AI purposes is becoming increasingly important in many domains: for instance, publishers are exploring opportunities to monetise their content, while platforms like X (formerly Twitter) have built higher paywalls around their own data, with mixed results across the ecosystem.
“In parallel, we are also seeing a range of organisations involved in data-sharing initiatives that drive social value. For example, the Industry Data for Society Partnership, with which the ODI is affiliated, works with companies like Microsoft, Github and Linkedin to support opening company data for researchers and public policymakers to build into policymaking and delivery, to the benefit of citizens. Initiatives like Strava Metro and OpenActive drive the opening up of commercial data about services that benefit communities.”
“If by ‘traditional analytics’ you mean the ways in which businesses can analyse data, including financial data, to improve their operations or performance, there are many other ways that businesses can create value. For example, the data gathered by organisations may have utility beyond its walls – extending to the whole supply chain in particular sectors. For example, working with our longest-term strategic partner, Arup, we are exploring how sharing carbon – or ‘net zero – data across the built environment sector can help to embed best practices and encourage innovation.
“In the UK water sector, through the Stream initiative, the ODI is working with a group of partners to design and deliver the ‘network of data pipes’ needed to share useful industry datasets in secure, standardised and easy-to-access ways. The goal is for data to flow into larger datasets, enabling us to solve tough sector challenges collaboratively. Stream’s impact reaches beyond the water sector. Experience from other sectors, like banking – for example through Open Banking – shows that publishing data publicly builds trust and arms innovators with the information needed to launch new services which further benefit society. In London, Tfl’s open data initiative powers over 600 apps used by over 40% of Londoners. TfL estimates its open transport data has provided savings and annual economic benefits of up to £130 million for businesses, travellers, and the London economy.
“Another example is OpenActive, which provides the data infrastructure to enable data to flow across the sports and physical activity sectors. This, in turn, can help adults discover sports and physical activities near them, for example, through the Every Body Moves campaign.
“To make all this possible, businesses need to use open, interoperable technologies and data standards to describe the structure of the data exchanged across the supply chain. That does not mean the data itself needs to be open, but the infrastructure underpinning it should be based on standards and technologies that are built with that collaborative, ecosystem-level view in mind.”
“There is massive untapped potential in industries ranging from energy to retail, real estate and financial services, but greater value comes when data is shared between and across sectors.
“An example of what can be achieved is Open Banking, where the UK has led the world in creating the regulatory environment for fintech start-ups to flourish. Open banking allows individuals and small business owners to authorise third-party companies to use their financial data to provide improved financial services. Innovations so far include apps that automatically round up transactions to help people save; payment cards that give holidaymakers access to improved exchange rates without international transaction fees; cloud accounting services that help businesses manage their cash flow and taxes and; smarter credit checks that give freelancers and SMEs with variable incomes access to loans and credit.
“Through this ecosystem, UK fintech start-ups have attracted significant investment. Many have scaled in size, disrupting and diversifying the banking services available to SMEs and consumers.
“What we now need to see is more action to create similar ecosystems in other sectors. One way of achieving this is by encouraging the greater use of ‘Smart Data’. Smart Data enables individual consumers and business customers to access and share their data simply and securely with third parties, enabling those third parties to provide them with innovative services.
“Over the past six months, the ODI has partnered with the Department for Business and Trade, Challenge Works and Smart Data Foundry to stimulate innovation through a Smart Data Challenge. The initial part of the challenge recently wrapped up with 14 diverse innovations competing for four final prizes in the ‘discovery phase’ – and winners across the retail and fintech sectors. The project will now move into a full challenge prize process, with participants expected to prototype their Smart Data ideas in a digital sandbox – with the support of grant funding – creating a community of innovators and partners to accelerate technological innovations in 2024 and beyond.”
“Again, at the ODI, we tend not to think of data as a mineable extractive. Our experience is that if you think of it in this way, it leads to organisations either hoarding, or ignoring the data they collect. What’s more useful is to think of data as valuable information that, when structured, organised, shared, and combined with other information, can create value for businesses, consumers, society and the economy. These points aside, we see evidence that businesses in many sectors still face substantial challenges in attracting talent as well as setting up robust, sustainable data practices in their organisation and across the data value chain.”
“Many classes of data can be used to enhance customer experiences in different ways, and not all of them require the processing of personal data. There is a clear legal basis for how individuals’ privacy should be protected. Indeed, consumers expect organisations to use their personal data appropriately and ethically, and it’s likely this will increasingly come into play when people decide how they interact with businesses. Changes in customers’ preferences can have wide-ranging consequences – for instance, when Apple decided to roll out a new approach to data privacy to give people more agency in setting up their personal data use preferences, businesses that were relying on the use of this data, for instance for advertising purposes, suffered.
“Moreover, people have increasingly diverse expectations of companies – as both consumers and employees. For example, in the UK, only the largest businesses are compelled to report on their ESG (environmental, social and governance) performance. However, there are sound business reasons why companies of all sizes should collect and share ESG data. Attracting talent and being a good, ethical organisation is really important to an increasingly large percentage of the public. Sharing ESG data can create other business benefits. For example, investment performance is a huge area where large amounts of funding get snarled up – whether from philanthropy, private equity, VCs or banks – and there is a requirement for companies trying to access that funding to report on their ESG. In this instance, company growth depends on businesses stewarding and sharing data competently.
“The good news is that we can see businesses are ready to invest. In the past decade, for example, we’ve seen the evolution of data governance practices from a niche concept to a cornerstone of organisational success. At the ODI, we’ve done lots of work to help organisations develop their data governance and stewardship practices, seeing our training courses for business leaders growing in popularity. We have also recently released a piece of research exploring practices in responsible data stewardship, drawing on a range of sectors and organisations all over the world.”
“Recent progress in AI foundational models (FMs) and their accelerated adoption by businesses – and government bodies – can bring significant opportunities for efficiencies, economic growth and innovation. Still, some risks must be understood by businesses if they are to benefit from the potential of these technologies. They include the role of data, computing, governance and regulation. All are important for companies to participate effectively in the field over the next few years.
“There is a risk – as generative AI gains traction and given the costs associated with good data practices – that models will be trained and tested on synthetic or lower-quality data. This could, in time, lead to a degradation in performance and increase the likelihood of harm to businesses, customers and stakeholders. AI data infrastructure and better data practices should be adopted and mandated across industry, informed by the latest advances in data science and engineering.
“At the same time, there is no consensus or detailed concepts of how our AI digital public infrastructure should look like – as the digital world is less open and dominated by closed platforms, how can we imagine a public infrastructure designed by and working for us? What is the equivalent of common platforms such as Wikipedia for AI and what are its main socio-technical components? The ODI is working to develop thinking – and best practices – in this area through our Data-centric AI research.”
“I don’t think it’s particularly helpful to think of data as an extractive that can be traded like some other commodities. However, there are undoubtedly businesses whose whole model is predicated on this idea. The problem with them is that when companies hoard data, its true value – to the economy, society and the environment – goes untapped.
“Quite often, the true value of data comes from combining datasets from different sources and sectors. This can be done by publishing data as open data or sharing data through different forms of data institutions, some of which involve financial transactions. For example, data marketplaces bring together data providers and data consumers. Two examples of this are Dawex in France and iSHARE in the Netherlands. In Europe, we are seeing the emergence of a new class of business models enabled by seamless data flows, so-called “data intermediaries” – for me, this is an example of how regulation paired with open standards such as reference architectures and data formats could foster new types of business models and economic growth.”
“At the ODI, we believe it’s better to see the value of the ‘triple bottom line’ to whole ecosystems – not viewing data as something businesses ‘trade’ per se.”
“I think awareness amongst the general public – about how data about them might be accessed, used and shared – has grown. It’s also my observation that the growth in the availability of generative AI products has caused consumers and businesses to think more about how data moves around the system and how it is used, for example, to create derivative works. High-profile cases like the one brought by Getty Images against Stability AI and the strikes by the Screenwriters Guild are highlighting the potential for new technologies to contribute to misinformation, job losses, discrimination, and social inequalities. They have also sparked a lot of fear, uncertainty and doubt, including concerns about data privacy, the use of copyrighted content, and authenticity.
“On the other hand, this evolving thinking about data enables consumers – and businesses – to be more nuanced about how they want their data to be used. For instance, they may be happy for their data to be used for public interest research without asking for consent again. Currently, there are no standardised digital ways to express this consent.
“Still, there is a new generation of so-called privacy-enhancing technologies (or PETs) that promise to revolutionise this domain. PETs can increase access to data that may otherwise be kept closed for reasons of privacy, commercial sensitivity or security concerns. One example is the Solid Pod, invented by the ODI’s co-founder, Sir Tim Berners Lee. The Solid Pod lets individuals and groups store their data securely in a decentralised data store. These are like secure web servers for data where owners control which people and applications can access it.
“At an AI ecosystem level, the ODI advocates for balancing data monopolies. At the moment only a small number of businesses can make the most of the data available in the public domain, as it requires huge investment in computational infrastructure, as well as access to talent. There needs to be significant investment to enable more research into AI algorithms that make more with less data and are computationally efficient, to allow newcomers and academia to contribute to the ecosystem.
“Levelling up the AI data market is essential if we want to have innovation over time, and particularly to encourage UK businesses to thrive. There are many ways to achieve this, from encouraging big tech organisations to partner with more UK universities and continue to invest in UK data centres, to incentives and other forms of support in the style of BRIDGE (which covered only selected sectors so far), to regulatory sandboxes that manage the risk of compliance for organisations that would otherwise be left out.”
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