Caught somewhat uncomfortably between the dominance of tech giants and the agility of digital start-ups, mid-sized businesses often find themselves in a challenging middle ground when it comes to leveraging data. The likes of Amazon and Google have the deep pockets to experiment, refine, and push boundaries. Meanwhile, digitally native, greenfield start-ups benefit from their lack of technical debt and the luxury of learning from the mistakes of their predecessors.

For most mid-sized businesses, however, achieving data maturity – the progression from functional to exceptional use of data – is more often than not an uphill battle, with the transition from good to great a cultural and strategic hurdle as much as a technical one.

While digital transformation initiatives might be common amongst mid-sized businesses, many still find themselves plateauing (see ‘Where is your business in the data maturity matrix?’ below). They possess data systems that are operationally competent but struggle to unlock their full potential, which leaves businesses stuck in the past as the world advances around them, constantly raising the bar in the eyes of consumers.

Furthermore, historical dependence on third-party data has left many mid-sized businesses ill-prepared for the current era of privacy regulations and cookie-less tracking. As consumers opt out of tracking mechanisms and regulatory pressures grow, the need to prioritise first-party data strategies takes centre stage, which can complicate matters.

In light of this, how can mid-sized businesses hope to compete? The rather glib answer, which you may hear from experts in this space, lies in being ‘smarter’ and ‘more focused’ in their approach. The more nuanced answer begins by accepting the era of ‘track and target’ has finally given way to ‘listen and engage’ where businesses must leverage the rich insights of first-party data to enhance customer experiences and drive loyalty. So, let’s jump into that nuance and see how we can help matters.

The evolution of data maturity, 1995 – 2025

Even in 2025, most medium-sized enterprises are stuck at levels 2 and 3 – yet most can do so much more to augment their original investments and step up without a radical overhaul.

1996-2000 – Early digital marketing (Level 1): Rise of basic email marketing, rudimentary website analytics, and the beginnings of online advertising. Segmentation is crude.
2001-2005 – Web analytics and search (Level 2): More sophisticated web analytics tools and the growth of search engine marketing become a core business focus.
2005-2010 – Marketing automation and CRM (Level 3): The rise of marketing automation platforms and CRM systems enable more complex campaigns, lead nurturing, and basic customer segmentation. Reliance on third-party cookies grows.
2010-2015 – Data-driven marketing (Level 4): Increased focus on data integration, A/B testing, and more sophisticated campaign optimisation. The use of cookies becomes widespread for tracking and targeting. Mobile marketing gains prominence.
2015-2020 – Personalisation and advanced analytics (Level 5): Shift towards more advanced personalisation techniques, including predictive modeling and machine learning for recommendations and targeted offers. The limitations of third-party cookies become more apparent.
2020-2025 – First-party data and customer data platforms (Level 6): Privacy regulations and deprecation of third-party cookies signals shift to first-party data strategies and investment in CDPs to unify customer data and deliver more relevant experiences. This is an ongoing transition.
2025+ – Intelligent automation and contextual experiences (Level 7): Greater use of AI and machine learning for real-time personalisation, predictive analytics, and automated decision-making. Greater focus on contextual experiences and privacy-preserving tech. The shift away from third-party cookies accelerates.

Overcoming the reliance on third-party data

For years, businesses have relied on third-party cookies to track and target customers across platforms. This ecosystem enabled precise ad targeting and contributed to what felt like highly personalised customer experiences. However, with the decline of third-party cookies and the rise of privacy-first frameworks like GDPR and CCPA, this model is no longer sustainable. Companies must now switch towards first-party data gathered directly from customer interactions on owned platforms such as websites, apps, and physical stores.

The first step in this process is ensuring robust systems for collecting, storing, and synthesising first-party data. Here, a Customer Data Platform (CDP) can play a critical role by unifying data from various touchpoints. The tricky part is developing strategies to use this data effectively — from personalising marketing efforts to tailoring product recommendations and improving customer support.

And mid-sized businesses certainly do find it tricky, because despite advancements in data collection tools, many remain stuck at rudimentary levels of data utilisation. In my experience, a significant portion still operates at the level of basic segmentation and rule-based marketing automation – a far cry from the predictive analytics and real-time personalisation achieved by market leaders and the greenfield disruptors.

This gap often stems from cultural and organisational inertia. Businesses invest in systems but, thinking that’s a job done, fail to take full advantage of their capabilities, or skip the refinement process, leaving potential growth untapped.

One of the most common barriers is the tendency to stop short of optimisation. Many companies achieve 80 per cent of their goals by making the initial tech investment but fail to push through the final 20 per cent, which often makes the difference between good and truly great outcomes. This reluctance to complete the journey stems partly from budget constraints and partly from a lack of understanding of the value of incremental improvements. As a result, businesses end up cycling through periodic rebuilds, rather than iterating and optimising existing systems to their full potential.

Steps towards greatness

Breaking through this stagnation does not require a complete overhaul. Instead, businesses can take small but decisive steps. First, they must shift their mindset from seeing data collection as an end in itself, to viewing it as a tool for creating meaningful customer interactions. This means moving beyond static metrics and broad segmentations to dynamic, real-time personalisation.

The use of artificial intelligence (AI) can be transformative in this regard. Modern AI tools can analyse customer behaviour in real time, enabling businesses to respond with tailored content, promotions, and experiences. For instance, rather than relying on broad-brush email campaigns, companies can use AI-driven insights to craft (truly) hyper-personalised messages based on individual customer journeys. Such efforts not only improve conversion rates, but also build deeper customer loyalty.

Furthermore, businesses should adopt a disciplined approach to leveraging their existing tech stacks. Most modern systems, whether they are customer relationship management (CRM) platforms or e-commerce engines, come equipped with advanced features. The challenge is to identify and activate the features that align with specific business goals. Whether it’s using AI to optimise search results, enabling dynamic promotions, or tailoring chatbot interactions, businesses can achieve significant improvements without investing in entirely new systems.

The human element

It’s important to never lose sight of the fact that data maturity is about people and culture as much as tech. Organisations need to foster a culture that values experimentation, learning, and continuous improvement. Behaviourally, this can be uncomfortable for slow-moving or cautious businesses and requires breaking down silos and encouraging cross-functional collaboration. Marketing teams, data analysts, and IT departments must work together to ensure that data insights translate into actionable strategies.

Here, leadership plays an essential role in driving the necessary cultural shift. Executives must champion the importance of data-driven decision-making and allocate resources to initiatives that might not yield immediate returns but are crucial for long-term success. Bold, decisive leadership can make the difference between stagnation and progress.

Lastly, the goal is not to necessarily mimic the capabilities of tech giants or disruptive businesses but to craft a strategy that aligns with your unique strengths and constraints. What you will find, in all probability, is that with some guidance and a shift in mindset and approach, there is much more value to extract from the investments that have already been made – it’s just good practice to ask deeper questions and test just how data mature a business is and realistically can be.

Neil Trickett is managing director, EMEA, at Apply Digital.

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