Engaging Data

19/06/2026

There's a specific frustration that comes from making today's decisions on yesterday's data.

It's not that the data is wrong. It's that by the time it arrives — processed manually, reconciled across systems, checked before anyone will commit to it — the moment it was most useful has already passed.

For this UK food manufacturing group, the reporting cycle was a monthly exercise that consumed significant resource before producing numbers that were already ageing by the time they reached the people who needed them.

Daily automated data refreshes, feeding into Power BI dashboards with real-time visibility across all nine business units, changed that. Group-level performance became visible the same day. Decisions that used to wait on the report started being made from current data.

Real-time doesn't just mean faster. It means the insight arrives when it's still actionable.

If reporting cycles are creating a lag between what's happening and when leadership can act on it, talk to us about real-time visibility:

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18/06/2026

98.95% reduction in reporting time.

That's not a percentage from a presentation. It's what happens when 36 man-days of manual, fragmented financial reporting across nine SAP systems becomes 3 hours of automated, consolidated output.

The calculation is straightforward. The work to get there was not. Nine business units, each with their own SAP Business One instance, their own reporting logic, their own interpretation of shared financial concepts. Standardising that — building a warehouse that could hold a single version of the truth for the whole group — required understanding exactly how each unit had built what they had, and why.

We started by listening. Understanding the business before recommending the solution. The technical work followed from that understanding, not ahead of it.

The 98.95% is the result. The conversations that preceded it are why it held.

If you're spending significant time each month producing reports that should be running automatically, talk to us: https://engagingdata.co.uk/case-studies/financial-reporting-transformation.html

17/06/2026

One of the most frustrating patterns in manufacturing finance: needing a report, knowing the data exists, and still having to wait on IT to produce it.

Not because IT is slow. Because the reporting infrastructure wasn't built for business users to access directly. So every request becomes a ticket. Every variation on a standard report becomes a project. And the team that needs the insight waits while the team that can get it has other priorities.

Power BI self-service analytics, built on a centralised and properly governed data warehouse, changed that for this UK food manufacturing group. Business teams gained direct access to the financial insight they needed — filtered, trusted, available without raising a request.

IT was freed to do work that required their expertise. Finance stopped waiting. Decisions started moving.

If business teams are waiting on IT for reports they should be able to run themselves, talk to us about self-service analytics done properly:

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16/06/2026

When nine systems each produce their own version of the same financial data, the question that follows every report is the same: which number is right?

It's a question that shouldn't have to be asked. But in a fragmented reporting environment, it precedes every decision — because the confidence isn't in the data, it's in the individual who produced it and the process they followed.

For this UK food manufacturing group, nine SAP Business One systems meant nine sets of calculations and nine interpretations of shared financial concepts. No reliable mechanism to know which was definitive.

A centralised data warehouse with standardised financial logic across all business units changed the question entirely. From "which number is right" to "here's the number." One source of truth, across the whole group, refreshed daily.

When the data is trusted, the meeting moves faster. The decision gets made.

If leadership conversations are still being held up by questions about which number is correct, talk to us about building a single source of truth: https://engagingdata.co.uk/case-studies/financial-reporting-transformation.html

15/06/2026

36 man-days. Every month. Just to produce the financial report.

Not to analyse it. Not to act on it. To produce it — from nine separate SAP Business One systems, each with its own reporting logic, its own calculations, and its own version of what the numbers meant.

The finance team wasn't underperforming. They were doing exactly what the environment required of them. And the environment required too much.

Consolidating nine SAP instances into a single data warehouse, standardising the financial logic, and automating daily data refreshes changed that completely. The same report now takes 3 hours. A 98.95% reduction in the time spent producing numbers rather than using them.

The finance team now spend their time on analysis. That's what they were there for.

If financial reporting is consuming time that should be going on analysis and decision-making, talk to us about what automated reporting looks like:

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11/06/2026

Data deployments that require careful manual steps to get right are not scalable. They're a risk.

In manufacturing environments especially, where data supports operational decision-making, a fragile deployment process doesn't just slow things down — it creates exposure that compounds with every release.

As part of this global manufacturer's Data Mesh implementation, we established CI/CD integration through GitHub across both Databricks and Oracle environments. Deployments became repeatable and testable. Changes could be made with confidence rather than anxiety. The team stopped dreading releases.

That shift — from manual, high-stakes deployments to automated, well-governed pipelines — is what operationally mature data engineering looks like. Not just faster delivery. Safer delivery.

When a deployment can be trusted, the team working on it can focus on building rather than protecting what's already there.

If your deployment process requires more manual intervention than it should, talk to us about CI/CD for data environments: https://engagingdata.co.uk/case-studies/data-mesh-dataops.html

10/06/2026

Central data teams become bottlenecks without meaning to.

It's not a performance problem. It's a structural one.

When every request, every pipeline, and every data product routes through a single team, the backlog grows regardless of how capable or well-resourced that team is.

The constraint is in the model, not the people.

This global manufacturer had reached that ceiling. Analytics initiatives were slowing not because the ambition was wrong, but because the operational foundation couldn't support the volume and pace being asked of it.

Moving to a Data Mesh model — with domain ownership, automated DataOps across Databricks and Oracle, and CI/CD integration through GitHub — distributed accountability in a way the central model couldn't. Teams gained ownership of their own data products. The central function shifted from bottleneck to enabler.

The backlog cleared. Delivery accelerated. The teams doing the work felt it.

If your central data team is absorbing more than it should and delivery is suffering for it, talk to us about what domain ownership looks like in practice: https://engagingdata.co.uk/case-studies/data-mesh-dataops.html

Expert data consultancy specialising in Data Vault, warehouse automation, cloud migration, and analytics. We build trusted, scalable data platforms.

09/06/2026

One measure of a successful data engagement: the client can run everything without you when it's done.

For this global manufacturer, that was the goal from day one.

Not a platform that required ongoing reliance on an external team. An environment their engineers understood, could maintain, and could evolve as their needs changed.

Alongside building the Data Mesh and automated DataOps pipelines across Databricks, Oracle, and GitHub, we ran structured upskilling in data modelling, DataOps methodologies, and engineering workflows — embedded into the delivery, not bolted on at the end.

The handover wasn't a risk. It was the point.

When we left, the team had a platform they owned, documentation they could use, and the confidence to take it further themselves.

That's what long-term capability looks like — and it's the only version worth building.

If you're looking for a data partner who builds for your independence rather than their continued involvement, talk to us: https://engagingdata.co.uk/case-studies/data-mesh-dataops.html

Expert data consultancy specialising in Data Vault, warehouse automation, cloud migration, and analytics. We build trusted, scalable data platforms.

08/06/2026

There's a specific kind of operational frustration that comes from working in a manufacturing environment where the data exists but nothing connects.

Different teams. Different systems. Different versions of the same truth.

Analytics that should take minutes take days — because the work of pulling data together sits entirely with the people who shouldn't have to do it.

We worked with a global manufacturer in exactly this position.

The capability was there. The data was there. What was missing was the structure to make it usable — consistently, reliably, at scale.

A Data Mesh built on Databricks, Oracle, and GitHub changed that. Automated DataOps pipelines replaced manual workflows. Teams that had been dependent on a central data function gained genuine ownership of their own domains.

The difference wasn't primarily technical. It was operational. People stopped fighting the data and started using it.

If disconnected systems are slowing down analytics more than they should, talk to us about what a connected data environment looks like in practice:

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22/05/2026

The cost of data problems rarely appears in a single line item.

It’s distributed. Repeated manual work that nobody has added up. Duplicated pipelines solving the same problem twice. Delays in getting answers that slow decisions down without anyone tracking the time. Each item seems manageable.

Together, they represent a significant and largely invisible drain on the business.
The more consequential cost, though, isn’t the operational overhead. It’s the decisions that were made later than they should have been — or made with less confidence than they needed to be — because the data wasn’t reliable enough or fast enough to support them.

That cost is harder to quantify, which is why it rarely appears in a business case. But it’s the one that matters most at leadership level.

If you’re being asked to demonstrate the value of data investment — or to justify the cost of fixing the foundation — the full picture is usually more compelling than the headline figure. The operational savings are real. The strategic cost of the alternative is larger.

📌 If you’re building the case for investment in your data
foundations and need a clear picture of what it’s actually costing:

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