Data ≠ Value?
Bridging The Operational Gap
What an amazing event! Thanks so much to DinMo for organising this fabulous rooftop venue and organising all the catering. We had over 100 signups ranging from investors, data leaders and marketing professionals, so be sure not to miss out the next time we organise one!
Businesses have spent years, and millions, on AI, digital transformation, and data initiatives. And yet, something is not clicking. Instead of becoming more data-driven, companies are backsliding. A recent Fortune 100 survey found that fewer executives today believe their organisations have a strong data culture compared to just a few years ago.
So, what is going wrong? The missing link is collaboration. Too often, data teams and operational teams speak different languages, work in silos, and struggle to turn insights into action. Bridging this gap isn’t just important; it’s critical to unlocking real business value.
At a recent panel discussion featuring Keshvi Radia, Greg Freeman, Edward Gibbs, and our very own Candice Ren, we debated how to tackle this challenge. The conversation was candid, the frustrations real, and the solutions refreshingly practical.
Let’s explore what it really takes to turn data into decisions…
More companies today recognise that they have yet to embed analytics into their core operations.
Ed Gibbs, Data Solutions Manager at DinMo
Why Does The Gap Persist?
For most companies, building a truly data-driven culture is still a work in progress. While many organisations can describe the outcome they want, using data to make smarter decisions and power automation, getting there is another challenge entirely.
Is The Gap Growing?: The divide between data and operational teams isn’t necessarily widening; rather, the rise of AI has made the problem more visible. Ed pointed out that as AI-driven tools become more mainstream, people think they understand data better than they actually do. It’s the classic Dunning-Kruger effect: when you know a little, you feel like an expert; when you know a lot, you realise just how much more there is to learn. This growing awareness is a good thing, businesses now recognise the gap that’s always been there.
Fuzzy Requirements Lead to Fuzzy Outcomes: As Keshvi highlighted during our panel, companies need to be relentless in their pursuit of focus when it comes to data. Too often, organisations embark on data projects with vague or shifting requirements, leading to equally vague results. Without a clear understanding of the problem they are trying to solve, businesses end up with insights that might be interesting, but not necessarily useful.
Even Large Corporations Struggle: It’s not just smaller businesses facing this challenge. Greg pointed out that even large, well-resourced corporations have not fully embedded a data-driven culture. Many still rely on traditional decision-making processes, treating data as a supplementary tool rather than integrating it into their core strategy. The reality is that data adoption is not just about technology, it is about mindset and ways of working.
From Transactional to Transformational
Candice highlighted that If analytics teams become purely transactional, simply responding to requests and crunching numbers, they lose their ability to shape business strategy. Data teams need to work closely with subject matter experts to understand not just what is being asked of them, but why. What decisions are these insights informing? How do they fit into current processes? Without this alignment, operations and analytics will always be chasing different outcomes, and speaking different languages.
If a business user can’t clearly articulate the end outcome of why they need the data and the impact it will have, we do not work on that project!
Keshvi Radia, Head of Product At Balderton Capital
Making Data Actionable
Collecting data is easy. Making it useful is the hard part. Most businesses have an overwhelming amount of data at their disposal, yet operational teams still struggle to apply it effectively. The real challenge? Ensuring data is delivered quickly, trusted fully, and embedded seamlessly into decision-making.
Speed Matters—But So Does Trust: Keshvi emphasised the importance of moving fast. Data teams need to focus on delivering something tangible within weeks, not months. A Minimum Viable Product (MVP) allows business users to start seeing value quickly. From there, it can be refined and expanded. While Candice agreed with the iterative approach, she also highlighted a crucial risk: trust. Data trust is fragile, if operational teams lose confidence in the numbers, they will simply stop using them. Sometimes, investing the time upfront to get the data right is essential. Data professionals often need to unpick complex relationships to deliver meaningful insights – and it’s worth investing the time upfront to get it right.
Insights Must Be Embedded: Ed pointed out that one of the biggest reasons the gap persists is that insights often don’t slot neatly into day-to-day operations. Sales teams, customer service agents, and logistics managers need instant, actionable insights built directly into their existing workflows. Meanwhile, data teams focus on model accuracy, data integrity, and pipeline efficiency. The result? A disconnect, where business users receive dashboards and reports that don’t translate into real-world impact. Businesses need to stop treating data as something that lives in dashboards and reports and start embedding insights directly into workflows. If data is not available at the exact moment a decision needs to be made, it will not drive action.
Understand the Process: Greg raised another issue in why data teams struggle to make data actionable. Too many do not take the time to truly understand the processes they are trying to influence. It is not enough to analyse numbers in isolation, data teams need to learn the context, constraints, and priorities of the teams they support. And despite everyone talking about the importance of storytelling, few data professionals have strong storytelling skills. Being able to translate complex data into a clear, compelling narrative is just as important as the numbers themselves.
Success Stories
When considering organisations that have successfully bridged the gap between data and decision-making, a few notable examples came to mind. Each case highlighted different approaches to leveraging data for operational efficiency and business growth.
Candice shared an example at Fyxer AI, a hyper-growing client. The founding team is super focused on scaling fast and united the entire team around a clear MRR target. To help achieve this, they invested early into a robust data stack, overcoming challenges such as inconsistent MRR reporting across currencies and limited visibility into ROI across marketing channels and usage patterns. With a laser focus on what truly matters, accurate and comprehensive MRR reporting was automated within 4 weeks, with feature usage and centralised marketing performances in another 4 weeks.
Greg shared insights from Aston University, which took the time to analyse how response rates to applications affected student onboarding. Through their research, they discovered that faster responses led to significantly higher conversion rates. This data justified the investment in operational improvements, enabling the university to streamline the process so that applicants could receive a decision within days instead of months.
Ed highlighted a common challenge, the slow speed of data acting as a blocker to effective decision-making. At Ankorstore, the company recognised the need to empower operational teams directly with data and not through a small, centralised team. They invested heavily in self-serve capabilities, allowing marketing, who lacked technical expertise, to autonomously create customer segments and integrate them into their marketing platforms. This business-first approach prioritised efficiency and flexibility, ultimately building systems that served operations rather than slowing them down.
Keshvi offered a different perspective, emphasising the need to get to value quickly in her current role at Balderton Capital. Her goal is to achieve an MVP-level result within just one week! This approach has significantly increased business awareness of the value of data. By demonstrating impact early, she was able to build momentum and secure greater investment in data initiatives. Once that initial value is clear, her team can focus on building a strong data foundation and robust frameworks to support long-term success.
One of the key challenges for fast-growing businesses is the need to establish strong data foundations before they can scale effectively.
Candice Ren, Founder at 173tech
How To Get There
While the end goal is a seamless, data-driven workflow, getting there takes time. Businesses need to build the right foundation, ensuring that data flows smoothly from collection to action.
Data Foundations: Candice gave a great overview of a modern analytics pipeline in describing that data must be pulled from various sources and consolidated in a warehouse like BigQuery, Snowflake, or Redshift. Once centralised, data modelling and transformation are required to shape raw data into meaningful business intelligence. Once this foundation is in place, Reverse ETL comes into play. Rather than keeping valuable insights trapped in dashboards, Reverse ETL tools like DinMo ensures data flows back into the operational tools where decisions happen; CRMs, marketing platforms, and customer support systems.
Building the Right Team: Having the right infrastructure is only part of the equation, businesses also need the right team to ensure data becomes an integral part of decision-making. The challenge is finding a balance between speed, skillsets, and usability. Keshvi shared that she deliberately built a small, highly versatile team where everyone could work across the entire data pipeline. While this approach took longer to hire for, it meant that once the team was in place, they could develop and deploy data products much faster, without dependencies on multiple teams.
However, Candice cautioned that these kinds of generalists are hard to find. While a lean, multi-skilled team can be highly effective, not every business has the time to wait for the perfect hires. The reality is that data experts tend to excel in specific areas; data engineers, analysts, and product owners each bring unique strengths. The downside of this specialised approach is that, over time, it requires a larger team to scale operations effectively.
Ease Of Use: Beyond talent, Greg stressed the importance of making tools easy to use. No matter how powerful the insights are, if the technology is too complex for business users, adoption will suffer. If people find it frustrating or unintuitive, they will revert to old habits, making manual decisions instead of leveraging data-driven insights.
Very few of the enterprise clients we work with have a true data culture. Most are a work in progress and that’s okay.
Greg Freeman, Founder At Data literacy Academy
Bridging Cultural Gaps
To successfully bridge the gap between data and operational teams, it is crucial to address the cultural divide that often hinders collaboration. The difference in how each team views and utilises data can lead to misalignment and missed opportunities.
Technology Alone Won’t Close the Gap: Greg pointed out, much of the disconnect comes from differing priorities. Data teams focus on accuracy, scalability, and long-term trends, while operational teams prioritise speed, execution, and immediate impact. Without alignment, even the best data solutions can go unused or misinterpreted.
Where To Draw The Line: Candice emphasised that data professionals do not need to become sales, marketing, or logistics experts, but they must understand the key metrics that drive business success. A data scientist building a churn model, for example, should not only know how to predict churn but also how a customer success team actually handles retention efforts. Without this context, even the most sophisticated models risk becoming disconnected from real-world decision-making.
Data Literacy: On the flip side, operational teams need a basic understanding of how data works. Keshvi recalled a previous employer that attempted to make every employee learn SQL, an experiment that had mixed results. While operational teams do not need to write queries, they do need to understand how data is structured, where it comes from, and why certain analytics tasks take longer than others. A sales leader who understands how lead scoring is calculated, for instance, is far more likely to trust and apply it effectively.
Crucially, this is not a one-off training exercise, it is an ongoing effort. Embedding data professionals into operational teams, running joint problem-solving sessions, and fostering a culture where both sides continuously learn from each other is essential. When data and business teams operate in true partnership, insights become trusted, actionable, and seamlessly integrated into decision-making.
If you are looking to activate data within the tools your team are using and not sure where to start, why not book a call with our friendly team.
Ruben Scott, Analytics Lead at 173tech
Conclusion
Bridging the gap between data teams and operational teams is no small feat, but it is an essential step in unlocking the full potential of data-driven decision-making. From understanding each other’s priorities and workflows to ensuring the right tools and team structures are in place, creating synergy between these two groups requires deliberate effort and ongoing collaboration.
As we have seen from insights shared by Keshvi Radia, Greg Freeman, Edward Gibbs, and Candice Ren, success lies in both cultural alignment and technical execution. Data teams must develop a deeper understanding of operational needs, while operational teams need to gain a foundational understanding of data structures and processes. Only by embedding data into day-to-day workflows and ensuring it is actionable at the point of decision can businesses truly transform their operations.
It is about continuously learning, adapting, and evolving together. By fostering a culture of collaboration and understanding, businesses can turn data from a passive asset into a dynamic driver of growth.
