Built for Decisions.
Optia makes your data AI-ready so you become AI-confident.
Trusted data. Clear Answers. Confident decisions.
Solutions
You know AI is changing the way we work. Knowing what to do about it and where to start is hard. Real, measurable success with AI depends on three progressive layers:
Without these, AI initiatives fail. Optia builds all three for you.
We bring together your data: syndicated market, sales and marketing or your own internal numbers, creating one governed, audited and secure environment. Your data, reconciled and reliable, with a clear trail from which every figure originates. The result? Numbers that match and reporting that runs itself. Most people undervalue this, which is why any AI will give confident, wrong answers
Data on its own doesn't know your categories, your definitions, your hierarchies or your quirks. We codify that meaning in a managed layer: the business rules, definitions and relationships that let your people - and any AI - read your data correctly.
Dashboards, ad-hoc answers and enhanced analysis. Every insight identifies real value and how to unlock it. Every recommendation is provided to improve your business, based on your governed data. All verified by a human analyst. Decisions made with confidence. Actionable insights not vanity metrics.
Not sure which of these layers fits with where you are today?
Start a conversationFoundation
Foundational data engineering
Optia's rule-driven framework harmonises and standardises the data. You get an automated, audited process that creates a clean raw data lakehouse to work with. We then work with you to build data transformation pipelines over this, producing data optimised for analysis in your BI tool of choice.
Context
Data catalogue and context layer
Our foundational data engineering processes build a governed catalogue of definitions, business rules and lineage. You get two key things: standardised metrics to work with, and pre-calculated datasets, leaving no scope for LLMs to hallucinate or imagine numbers.
Intelligence
Model Context Protocol over your data
Your harmonised data made available through MCP, so CoPilot and other AI assistants can query it directly, governed by the same permissions, lineage and named-analyst accountability as the rest of the platform.
Customers
We help consumer businesses unlock more value from complex, costly, syndicated data; this is where we have the most experience and our greatest relationships. The same engineering also serves clients in industrial, health, education, financial services consumer electronics verticals.
Partner network
What we hear
“I need time to do my actual job.”
80%
of analyst time goes on preparing data.
Just 20% remains for the work they were hired to do.
Retailer EPOS says one thing. Syndicated data says another.
The meeting starts in ten minutes and nobody knows which number is true.
10 to 15
people edit the same reporting file every month.
One overwritten formula is all it takes. Nobody notices until the numbers are presented.
One person knows how it all works.
When they leave, where do you start?
This is not an analyst problem. It is a commercial risk.
Capable teams are being asked to make major decisions using brittle, patched up spreadsheets. When syndicated data doesn’t reconcile with internal systems, a retailer feed silently drops products, or one incorrect SKU distorts an entire forecast, it is not the data that gets questioned. It is the person presenting it.
Optia catches and resolves discrepancies before they reach your category reporting, a board pack or a buyer meeting, so your teams have confidence in their numbers.
Pick the week that looks like yours.
The week
Your analysts spend the week assembling exports, reconciling definitions and rebuilding the baseline. By the time they can investigate the question, the commercial conversation has already moved on.
What changes
Start with the question.
Optia delivers one harmonised, analysis-ready baseline across retailer, syndicated and internal data. Your team investigates what changed, why it changed and what to do next, without rebuilding the data first.
The week
Was it distribution, availability, range, price or promotion? When the buyer asks, your team is still reconciling the evidence instead of presenting the opportunity.
What changes
Walk in knowing what changed and why.
Optia connects retailer performance, market context and your own, bespoke category definitions on one governed baseline. Every recommendation is supported by evidence you can trace, explain and defend.
The week
Spend sits in one file, retailer sales in another and volume uplift somewhere else. By the time the evaluation is complete, the next promotion has already been committed.
What changes
Know what worked while you can still act on it.
Optia connects spend, price, volume and margin by promotion, retailer and SKU. You can see what created genuine incremental value via A/B testing and adjust the next decision before the budget is gone.
The week
Finance, sales and category arrive with different versions of performance. Leadership time is spent reconciling the past instead of deciding what happens next.
What changes
One single source of truth.
Optia creates a governed, traceable view of performance across the business. Every team works from the same figures, every number has a source and meetings move from reconciliation to action.
One trusted baseline. Every team aligned. Better decisions, made faster.
See how the baseline is built →Approach
Most advice starts with choosing a model, buying a platform or deploying a chatbot. Or being told to use Copilot.The hardest part isn't choosing the right AI tools. It's having trustable, AI usable data available.Ask the most sophisticated model a question over fragmented, contradictory or poorly governed data and you'll receive a confident answer. It just may not be the correct one.We don't begin with AI. We begin with trust.
Every engagement follows the same principle.First, we understand how your business makes decisions.Then we bring together all your data sources: syndicated market data, retailer data and your internal systems into one governed, auditable foundation.Next, we capture the business rules, hierarchies and commercial context that give your data meaning.Then do we apply AI.The result isn't simply better reporting. It's intelligence your teams can act on with confidence.
Some organisations need predictive modelling. Others need workflow automation. Some simply need reporting they can finally trust.Our role isn't to sell you "AI". It is to recommend the right combination of data engineering, analytics, automation and Practical Intelligence for your business today, while creating foundations that allow you to adopt more advanced AI when the time is right.Sometimes AI is the answer. Sometimes it isn't. We'll tell you the difference.
Core
If you own data or IT, you're the one who has to be comfortable with how this works. Here's the straight version.
Optia runs all your data processes in a code and data versioned setup. We maintain full lineage of every change so that you don't need to deal with any surprises. Our data analysts and engineers have years of experience in dealing with data coming from myriad companies and in particular retail data providers, with whom we have data sharing partnerships. This experience allows us to build a wide range of data audits so we catch issues early.
Optia's processes are ISO27001:2022 certified. This means that we have been rigorously tested on best practices on data, infrastructure and process security. A lot of infrastructure build now happens through agents. We have skilled them to do any activity ground up, taking into account the strictest security controls.
We are big fans of conventional, top notch software. While AI Agents help us write logic and processes in an attended setup, we do not deploy them to take any autonomous un-attended decisions on your data. Every data exception is human understood, corrected and validated. Our experience has taught us where the efficiencies of AI are but equally what the risks are. Rest assured, your data is in safe hands.
Our experience has taught us where the efficiencies of AI are, but equally what the risks are. Your data is in safe hands.
The story behind Optia, in our founder's own words.
Until a couple of years ago, we were a software company, with (so we thought) a very cool self serve, no code (remember that?) data management platform. However, more often than not we were using the tool on behalf of our customers and then taking the cleaned data it provided to create automated reporting for them. We began to realise that our customers (and prospective ones!) did not really want more software forced on them. Software that promised the world but invariably failed to deliver. Software that purported to do the work of others, but which actually only gave them more work to do. They just wanted the hard work done for them. Why? Because 80% (and growing) of their time was spent trying to do this hard work when they wanted to be spending 100% of their time doing their actual jobs, doing stuff that really adds value, makes them look good, enjoy their work, get promoted, earn more…
So we launched Optia Data to help people do all that and more. We started as a data services company, with a focus on CPG/FMCG as this is where we had the most knowhow. In actual fact, as you have seen elsewhere on this site, we serve customers from many different industries all over the world. It turns out that wherever there is a lot of data, people who have to deal with it are in pain. We are here to help those people, whatever their business.
They just wanted the hard work done for them.
In light of the growing impact of AI, we repositioned ourselves to help our customers use not just data, but AI to their advantage. But to see AI as a productivity tool and not a threat. Helping people to work out where to start. AI is certainly 'promising the world' at the moment. But while recent findings suggest that in the majority of cases it is failing to deliver, it is our job to help you win with it, as the real winners will be those who use AI properly. Luckily, that requires all the skillsets that we have been developing over the last 15 years. Because, believe it or not, AI (in its Machine Learning guise) has been around for a long time. Today, anyone can use AI to spin something up quickly, but it is those who have the depth of understanding, learned over years, who can make it reliable and scalable on an ongoing basis. Today anyone can ask AI a question, but only those who have invested in creating that data foundation and context can expect an accurate answer.
It is daunting for a lot of people. But it needn't be. And it probably won't be in time. And it certainly won't be if you start now. Allow us to help, human to human. If you trust us to help you trust your own data, maybe you'll trust us to help you as this space continues to evolve.
Allow us to help, human to human.
We're a specialist data company with deep expertise and partnerships in consumer, but with a growing reputation in other fields. My team covers all the bases. Data and Software engineering expertise gained over decades from the best technical universities and Fortune 500 companies. Bleeding edge innovation skills honed in the labs of the most forward thinking global management consultancies and CPG behemoths. Analyst skillsets influenced by years working in and for CPGs and digital startups alike. Customer success, empathy and an innate understanding of what someone needs that can only have been honed on the playing fields of England.
We have spent years in the detail of syndicated datasets, doing the unglamorous work that makes commercial numbers reliable for big household names. We are human, we are diverse, we are located all around the world and have the ability to listen to the needs of our customers and then work out and implement the best possible solution available today. Every project we take on is either slightly different to the previous, or very different. All this coupled with backgrounds building and maintaining data platforms and softwares and natural curiosity that is seeing us test and push the boundaries of the latest technologies, mean that our customers want us not just to solve the problem at hand, but to join them wherever they are on their data journey.
We have always known that the data to insights journey is fraught with issues of 'garbage in garbage out'. The challenge conversational AI poses is that it may make even garbage look interesting and correct. Conversational AI must be fed data that is correct by design and also presented in a way that means the LLMs have a miniscule chance of hallucinating or cooking up something random. This requires strong data foundations and data presentation skills. At Optia, we have understood these challenges and are continuously on a mission to improve our capabilities around these emerging challenges.
Specifically, we strongly believe in having a grown up (human) in the room. As such every output we deliver has a person behind it: a named analyst who has checked the number and will stand by it. In a market rushing to automate the human away, we think the human is the point. Trust is earned, not claimed.
In fact, when you work with Optia, you always have a real person to talk to and to meet; someone who is accountable. And with Optia's leadership team in five countries and across three continents, we are always available and ready to share our combined three digit years of experience in working in data.
LondonBarcelonaNurembergAbu DhabiBangaloreChicagoNew York
Fifteen engagements, from a single dashboard to a five-year platform. Every one started with numbers somebody did not trust, and ended with a view they could act on.
Showing 15 of 15 case studies
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How Arla replaced a 20-day reporting process with an always-ready view of market, brand, category and retailer performance.
20 days to 10 minutesMonthly reporting cycle
Arla operates across markets ranging from the UK and the Nordics to Turkey, the UAE and Saudi Arabia. Each market supplied Nielsen data using its own product and category hierarchies, reporting calendars, currencies, and brand and SKU naming conventions.
This made it difficult for brand, insight and category teams to answer straightforward questions. Which brands were gaining share? Was growth coming from price, volume or distribution? How did private label performance compare by market?
Producing a consolidated view required around 20 days of manual preparation every month. The final output was a static report that was already becoming outdated by the time it reached decision-makers. Considerable effort went into assembling and reconciling the numbers, leaving less time to interpret what they meant and decide what to do next.
Optia created a single, consistent model for Nielsen reporting across all 16 markets. Every month, 60+ source files are automatically processed, checked and mapped into a common structure covering more than 366 brands and every relevant SKU.
Differences in currencies, reporting periods, product classifications and naming conventions are resolved before the information reaches users. The Power BI environment sits inside Arla's own systems rather than on infrastructure held by Optia. It gives Arla's teams a consistent view of value, volume and unit-price performance, brand and private-label market share, distribution, sales momentum, new product performance, and retailer and market-level results.
Automation alone was not enough. Optia introduced validation and quality controls that identify unusual movements, missing information and inconsistencies before they affect reporting. These checks have surfaced a 130% growth spike in the Danish data traced to SKU-level reporting errors and fed back to NIQ for correction, masked brand data in Switzerland requiring a bespoke method to reconcile to dataset totals, and Lira-denominated Turkish sales that overwhelm EMEA comparisons, now reported in converted currency both with and without Turkey. Rather than letting these problems pass into reports, Optia works with Arla and its data providers to investigate and correct them at source.
Arla reports that the monthly reporting cycle has been reduced from around 20 working days to around 10 minutes. The platform now supports around 50 users and is opened more than 100 times on an average working day.
Across the engagement, Arla has benefited from faster identification of brand and category opportunities, consistent comparison across markets, earlier visibility of pricing, share and distribution changes, less manual data preparation, and greater confidence in the numbers used by commercial teams and leadership.
The platform has continued to evolve since launch. Optia holds regular operational reviews, updates classifications as products and categories change, and adapts the model as the business grows, which is why it remains widely used five years later.
“Partnering with Optia has completely transformed how we manage data. Their automated reporting and real-time insights have not only improved our operational efficiency but also accelerated decision-making and positioned us for scalable growth.”
International Category and Commercial Excellence Lead, Arla Foods
“Nielsen data across 16 markets is a very complex yet structured dataset, so a great candidate to extract value with the power of Optia Data.”
Arla Foods
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How Australian Vintage uncovered £26 million of misclassified category sales, recovered hundreds of missing products, and turned a manual reporting process into an accessible source of commercial insight.
£26 millionMisclassified category sales identified
The UK and Ireland represent Australian Vintage's largest market region, making accurate category reporting essential for leadership updates, customer conversations and brand planning. Each month, the category team had to consolidate and validate market data manually before it could be used.
Two issues demonstrated the commercial risk. A recurring classification problem within the no and low alcohol category placed a significant volume of sales under an unidentified varietal and private label, misstating approximately £26 million of category performance. This was a restatement of how sales were reported, not revenue recovered. In a separate delivery, around 793 SKUs disappeared from the reported market following changes to product classifications. The products had not stopped selling; the reported view simply no longer included them correctly.
Without detailed checks, these issues could have affected board reporting, category conclusions and the interpretation of brand and competitor performance.
Optia built a reporting environment covering total category performance, top brands and manufacturers, no and low alcohol, country of origin, brand and sub-brand, varietal, wine colour, retailer performance, and value and volume market share across four-week, twelve-week, financial year-to-date and moving annual total views. Ireland is reported separately in local currency for the regional team.
Optia introduced validation controls that compare each new delivery against expected product counts, category totals, classifications and historical patterns. When something changes unexpectedly, the system flags it for investigation before publication. This process identified both the varietal classification issue and the disappearance of around 793 SKUs. Optia documented the evidence, worked with the source provider to establish what had happened, and secured corrected historical data.
The environment was also designed to be visual, intuitive and easy to navigate, so colleagues who don't work with market data every day could move from a high-level category view into the detail behind a particular brand, SKU, retailer or varietal without help. Training and rollout support helped the wider team use the dashboard confidently.
Australian Vintage now has a more reliable and efficient way to prepare category information for leadership and commercial decision-making: approximately £26 million of misclassified category sales corrected in the reported view, around 793 missing SKUs recovered, and full historical reporting rebuilt. Australian Vintage reports monthly validation effort falling from days to hours.
On one occasion, the team was able to validate, correct and deliver the required reporting over a weekend in time for a Monday board meeting.
“People are really unfamiliar with data. Having something visual and easy to use is beneficial for the people who are not working with data all the time. At least now I can show people in our company, just click on this tab. It is very easy.”
Category Insights Manager, Australian Vintage
“Optia have done all they can, been very responsive and supportive over the last couple of weeks. Please pass on my thanks to them.”
Senior Category Insight Manager, Australian Vintage
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How Golden Acre Foods created a consistent view of retailer and category performance, rebuilt two years of history, and gave its commercial team clearer evidence for range and growth decisions.
30% less timeSpent preparing commercial reports
Golden Acre Foods is a £130 million UK supplier of Halal, Polish and international food products and a certified B Corp, working with Tesco, Sainsbury's, Asda and Morrisons across a diverse and growing portfolio. To manage that portfolio, teams needed to understand which products were growing, how ranges performed by retailer, and whether changes reflected genuine trends.
Thousands of product records had incomplete SKU information. Existing classifications didn't consistently distinguish Halal, Polish and other international ranges. The team also lacked sufficient historical depth to tell whether a category was growing sustainably or simply moving from one week to the next. Rows covering stores with no active distribution were inflating the reported base, and were removed. For a lean business without a dedicated data engineering function, maintaining this internally would have required time and resources better focused elsewhere.
Optia took responsibility for processing and harmonising Golden Acre's weekly data across Tesco, Sainsbury's, Asda and Morrisons, delivered every Wednesday through a repeatable process designed around the commercial team's reporting timetable.
Working with Golden Acre, Optia built and maintained a practical classification structure separating Halal, Polish and other international food products, and resolved around 2,700 incomplete SKU records. Optia also processed, standardised and loaded 104 weeks of historical data, giving Golden Acre two complete years of comparable performance for the first time.
When a five-week gap appeared in the Asda data, Optia identified the missing periods, documented the issue, and coordinated the correction and reload with the source provider rather than leaving an unexplained break in the reporting.
Golden Acre now receives a consistent weekly view of performance across four major retailers: two full years of category and retailer history, weekly data delivered reliably every Wednesday, and around 2,700 incomplete SKU records resolved. Golden Acre reports that report preparation time has fallen by around 30%.
As Golden Acre places new products into test and expands its ranges, the team can now evaluate performance using consistent historical retailer evidence rather than manually assembling the picture each week.
“There is definitely a need for us to work with someone like Optia, who understands how to manipulate huge amounts of data into small, useful pieces of information.”
Commercial Manager, Golden Acre Foods
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How Samworth Brothers replaced a fragmented monthly reporting process with a consistent view of category, retailer and competitor performance across the group.
Around one weekReclaimed from every monthly cycle
Samworth Brothers, the family-owned food group behind brands including Ginsters and Soreen, operates across meals, sausages, savoury pastry, food to go, sliced cooked meats and cake. Each operating company reported its own results using its own calculations, definitions and process, and bringing those submissions together for the group executive board required a substantial monthly Excel exercise consuming around a week of each cycle.
Analysts estimated that around 80% of their time was spent gathering, cleaning and preparing data rather than interpreting it, limiting the time available to answer the commercial questions that mattered: which categories were gaining or losing momentum, how the group compared to the wider market, and which competitors were driving growth. A later data quality review also identified reporting errors that had previously gone unnoticed.
Following a competitive tender, Optia brought together five external sources (NielsenIQ EPOS, Kantar, Dunnhumby, Sakana and Nectar) with internal category submissions to create a single, maintained view of the GB market. The model standardises definitions and calculations across all six categories, allowing like-for-like comparison. During the transition, users could switch between the new standardised calculations and the previous methodology to build confidence in the new approach.
The resulting Power BI dashboard covers total GB market performance, individual category analysis, retailer performance, and competitor benchmarking against 16 strategically relevant names including Greencore, Cranswick, Pilgrim's, Compleat Food Group, Pukka Pies and Finnebrogue. It is delivered into Samworth's own Power BI tenant with SharePoint ingestion, so the group keeps control of access, governance and distribution.
As part of implementation, Optia carried out a structured data quality review across source files, calculations and outputs, identifying several previously undetected reporting errors, which were investigated, corrected and incorporated into the new standardised process.
Samworth Brothers now has one board-ready view across six operating categories: a single reporting model replacing manual Excel consolidation, consistent calculations across all six categories, and category, retailer and competitor performance visible together. Samworth Brothers reports reclaiming around a week from each monthly cycle. The first working version was delivered and demonstrated around three months after the initial discovery discussion.
“We were going to go and create another chart, which seems crazy in today's world. The dashboard is brilliant, this is exactly the kind of thing we needed. Seriously, thank you so much for the effort and everything that you put into this, and using your brains and your knowledge and your experience to create it.”
Group Head of Category, Samworth Brothers
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How Vit Hit turned complex market data into a self-serve view of sales, promotion, distribution and retailer performance.
3 yearsOf weekly history for trend analysis
Vit Hit competes across several parts of the soft drinks market, spanning mineral water, carbonates and ambient juice drinks. Previous reporting gave the team a high-level view but limited ability to investigate what was driving the numbers.
Was sales growth coming from stronger distribution or better rate of sale? Which retailers were performing best? Was promotional activity generating incremental volume? The information existed, but turning it into answers required more preparation and analysis than the sales team could realistically undertake themselves.
Optia created a consistent market model covering three relevant soft drinks categories across three levels of the Great Britain market, tracking five measures: value and unit sales, price, promotional performance and weighted distribution. Three years of weekly history were loaded to provide the context needed to distinguish short-term movements from longer-term trends.
One of the most important additions was the ability to view distribution and promotional performance alongside sales, helping surface whether changes were being driven by wider or narrower distribution, promotional activity, price, or stronger underlying sales performance. The dashboard was designed to be explored rather than simply presented, with filters by retailer, time period, format, brand and market, and Optia trained the team to use it directly.
Vit Hit gained a consistent view of its performance across three categories and three market levels, with sales, promotion, pricing and distribution visible together: three years of comparable weekly history, visibility of distribution alongside sales and promotion, and a self-serve dashboard the sales team could use to investigate its own commercial questions. The monthly refresh became a regular source of insight, giving users a clearer view of what had changed and where further action might be required.
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How International Beverage is turning a manually assembled monthly leadership pack into an automated reporting process spanning market, brand and internal sales data.
156 weeksOf market history loaded, up from 13
Work in progress
Watch this space.
First live reporting cycle targeted for September 2026.
International Beverage, the spirits business behind Old Pulteney, Balblair, Speyburn and Caorunn, relied on one insight manager to build its monthly leadership reporting by hand. Each cycle involved collecting data from multiple sources, refreshing Excel templates and pasting static outputs into PowerPoint.
The process depended heavily on his knowledge of where the data came from and how the final report needed to be assembled. Market data, internal shipments, distributor depletions, brand health information and annual market measures all arrived at different frequencies and followed different reporting calendars, meaning much of the consolidation still happened manually behind the scenes.
Optia is creating a single reporting model bringing together six external and internal data feeds, with initial scope covering market data for the UK and US, distributor depletion information from China, internal shipment data, and IWSR and Kantar sources. The underlying model also resolves differences in reporting calendars, since market data may follow a 13-period structure while International Beverage reports monthly against an October financial year. The leadership reporting framework covers 20 markets in total; the UK, the US and China are the initial scope being built first, not the whole framework.
Rather than replacing the existing leadership pack, the same governed model will serve two outputs: an interactive Power BI environment and the existing monthly PowerPoint, both generated from the same numbers. Optia has also loaded 156 weeks of historical data, up from the 13 weeks previously available, giving the insight team a much stronger basis for interpreting seasonality and momentum.
“I am basically the power user and I am essentially the person who is doing all this manual work. Really my time should be spent doing more insight work rather than data analysis.”
Category and Consumer Insight Manager, International Beverage
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How Samyang is replacing fragmented monthly reporting with one view of sell-out, sell-through and sell-in across Europe.
5 European marketsInto one consistent commercial model
Work in progress
Watch this space.
First dashboards targeted within six weeks of sign-off.
Samyang Foods has expanded rapidly across Europe, driven by the growth of the Buldak brand, managed from Amsterdam across the UK, Germany, France, the Netherlands and Poland. Each market came with different data structures, retailer definitions and product classifications, and three different views of the business (retail market data, distributor files, and internal SAP shipments) were being managed separately.
When those numbers disagreed, the team had no simple way to explain why, which mattered commercially: rapid growth had already created reported stock pressure, while major retail relationships in the Netherlands and Germany were reported as becoming harder to manage. The insight team was spending roughly two days per market preparing data before analysis could begin.
Optia is creating a common data model across all five countries, bringing together sell-out, sell-through and sell-in, combining retail audit data with internal SAP shipments and distributor reporting under a consistent product structure so a product can be compared the same way from Warsaw to Manchester. QA complaints and trade marketing activity are also in scope. The model is delivered through Power BI.
The most important change is the ability to compare the three layers side by side, giving insight and supply chain teams a shared evidence base to investigate whether a gap is caused by inventory build, distributor reporting, retail coverage, supply constraints, or a genuine change in demand. The new model will also allow distributor-declared performance to be compared with independently measured retail coverage. It is designed to make the difference between the three layers explainable, not to make distributor-reported and retail-measured numbers agree: syndicated retail data covers only the measured part of the market.
The first dashboards are targeted within six weeks of sign-off. Once operational, the model is designed to give Samyang one consistent view across five European markets, sell-out, sell-through and sell-in visible together, manual preparation time returned to the insight team, and a stronger basis for range, supply and market planning as the Buldak business continues to expand.
“You have some great charts about companies spending 80% of the time cleaning the data and 20% using the actual numbers. Actually you can change this to 95 to 5. Probably our case. And I am not joking.”
Team Leader, Strategic Planning, Samyang
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How Bacardi moved from manually combining market channels to automating management reporting across eight business systems.
40+ hours a monthOf manual reporting removed
Bacardi's commercial and management teams were working with valuable data across multiple parts of the business, but that information was rarely available in one consistent view. On-trade data from bars and restaurants and off-trade data from retailers arrived separately and followed different structures, and producing a total trade view required manual reconciliation that wasn't always completed, so teams worked from a partial picture.
Separately, commercial, finance, CRM, retailer, distributor, HR, supply chain and forecasting data were spread across systems maintained through a complex set of interlinked Excel files, with each reporting cycle requiring repeated preparation and reconciliation.
Optia created a consistent model bringing on-trade and off-trade together into a single total trade view, then automated the monthly reporting cycle so the two sources no longer needed manual combination. Commercial users could access and adapt the resulting analysis themselves.
More than 40 hours of monthly reporting effort removed, report generation reduced from days to minutes, and greater control for commercial teams without routine IT dependency. Bacardi records approximately $40,000 in annual cost avoided, an estimate rather than a measured financial result, reported in US dollars.
Optia consolidated data from eight areas of the business (CRM, finance, market data, retailer data, distributor reporting, HR, supply chain and forecasting) into one automated reporting model, replacing a high-maintenance interlinked spreadsheet process. Forecasting was connected to the same underlying data model rather than treated as a separate manual exercise.
One dashboard replacing the previous spreadsheet process, automated KPI reporting across commercial and operational sources, and manual preparation removed from the recurring reporting cycle.
The first project asked how Bacardi was performing across the total market. The second asked how the wider business was performing across its key KPIs. In both cases, the answer required information from multiple sources to be cleaned, connected and presented consistently, replacing manual consolidation with repeatable data processes.
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
How QTS replaced an expert-built spreadsheet process with a constraint-based maintenance plan for a 45MW hyperscale data centre, then compressed twelve months of work into nine and a half when the build programme changed.
12 months into 9.5After the build programme changed
QTS needed to build the planned preventive maintenance schedule for a new 45MW hyperscale data centre containing thousands of assets across twelve equipment classes and three separate construction handover phases. Every maintenance date depended on a network of operational constraints: redundant systems couldn't be taken offline together, cooling equipment had seasonal restrictions, and some assets shared physical spaces or needed maintenance in a defined sequence.
The existing plan, built manually by experienced, time-poor in-house individuals, was inconsistent and flawed. The problem was the thousands of interconnected decisions adding pressure and mismatches in scheduling: there was no simple way to demonstrate every rule had been respected. The plan depended on the sustained concentration of one experienced individual who also had a new site to bring into operation.
Optia converted QTS's maintenance requirements into a constraint-based scheduling model. The model understands which systems provide redundancy for one another, accounts for relationships between generators, UPS equipment and transfer systems, and encodes seasonal restrictions and shared service spaces directly, so it cannot produce a valid schedule unless the constraints are satisfied.
Once critical rules are satisfied, the model optimises how work is distributed so maintenance doesn't cluster into unmanageable peaks. Daily service volumes are capped and work is sequenced across the building to cut unnecessary movement of engineers and specialist equipment. The final deliverables included the maintenance schedule, a monthly planning view and a validation file, with Excel deliberately retained as the primary output to match how site engineers and leadership needed to work.
When the site handover programme moved, the maintenance workload originally planned across twelve months had to fit into approximately nine and a half. Optia reconfigured the model around the revised dates and returned a new plan within five days, preserving the agreed redundancy, sequencing and operational constraints.
QTS received a fully deconflicted annual maintenance calendar covering the 45MW site, giving a controlled basis for committing dates to engineers and vendors. When the construction programme changed, the same approach compressed twelve months into nine and a half and produced the revised schedule within five days. The European implementation has since become a reference point for the wider organisation, with interest from the US team in applying the same model.
“I couldn't have done this confidently whilst doing all the other work that I've needed to do. You have been able to take that off my plate and you provided a PPM planner, which is exactly what I asked for. So it is everything.”
Operational Readiness Director, QTS
Tell us what is slowing your commercial decisions down and we will show you how we would approach it, including a free assessment.
Education
How Inspired Learning Group turned fragmented finance, occupancy, admissions and payroll data into role-specific reporting across around 25 schools and nurseries.
Around 25 settingsInto one reporting environment
Inspired Learning Group has grown through acquisition, bringing together schools and nurseries with different operating models, systems and reporting rhythms. Much of the traditional financial reporting wasn't designed around the decisions individual heads could actually make: a full group P&L might contain hundreds of lines, but only a small proportion related to the levers available to them.
The information needed to manage those levers was spread across five different systems: finance in NetSuite, school occupancy and discounts in iSAMS, nursery data in Famly, admissions in Digistorm, and payroll separately again. Reporting cadence also varied, since nursery occupancy can change meaningfully week to week while school reporting follows a more monthly rhythm. Some source systems held only the current state, so when a pupil joined, left or moved stage the previous position could disappear unless it had already been captured.
Optia connected the five core systems into one reporting environment covering finance, occupancy, admissions and people, then structured the reporting around who needed to make the decision. Individual heads and nursery managers see their own setting; leadership can move through nursery, prep, all-through school and group-level views. That is five levels in all, from the individual setting up to the group.
For each setting, the reporting connects occupancy against budget and capacity, revenue and staffing performance alongside staff cost as a percentage of revenue and agency cost as a percentage of staff cost, admissions from enquiry through to deposit, and HR views tracking vacancies, starters, leavers and absence. Nursery files are prepared weekly in time for Monday marketing meetings, while schools operate to a more monthly rhythm, with a review workspace used before publication and access controlled so heads see their own setting.
The former Finance Director reports that the dashboards now form the basis of monthly school-by-school discussions. Heads and nursery managers receive information focused on the parts of the business they can influence, while the group can compare occupancy, admissions conversion, revenue and payroll performance across settings using consistent definitions, allowing leadership to see which settings are performing well and investigate what others can learn from them.
“If we get those wrong, the dashboards are almost meaningless because they’re being referenced against a meaningless target and therefore any decision points that come out of it are rendered slightly obsolete because the data is already out of date.”
CFO, Inspired Learning Group
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How Campari UK brought on-trade and off-trade performance together in one commercial view while preserving the differences between the two channels.
4 data providersReconciled into one Total Trade view
For spirits brands, on-trade and off-trade performance are closely connected, but there was no ready-made Total Trade dataset for Campari. The two channels came from different sources, followed different structures, and measured some KPIs in fundamentally different ways: distribution meant weighted distribution in retail but a physical outlet count in the on-trade.
Category definitions also differed between sources, particularly in low and no alcohol, and the two channels could arrive on different release schedules, meaning a combined number might look plausible while actually comparing different periods. Internally, the reporting split reflected the data split, with no single view across the full market. One analyst owned on-trade, another owned off-trade, and the outputs were produced manually in Excel. Off-trade carries a large share of volume while on-trade builds visibility, trial and brand equity, so neither channel answers the question on its own.
Optia built the Total Trade model in the reporting layer rather than assuming the sources could simply be joined together, aligning category definitions and normalising naming while preserving genuine differences. Selecting off-trade distribution returns the appropriate weighted distribution measure, while selecting on-trade returns the physical outlet count, so the dashboard never averages or combines measures that don't mean the same thing.
Optia also built controls around period alignment: if the underlying periods didn't match, a Total Trade figure simply wasn't published, prioritising a correct answer over an apparently complete one. The finished reporting environment spans Total Trade market and supplier views, brand scorecards for Campari, Aperol, Courvoisier and Wray & Nephew, and consumer funnel measures.
In November 2025, Optia completed a full knowledge transfer to Campari's application support team, and the monthly refresh is now run internally.
Campari now has a single reporting environment covering on-trade and off-trade performance: one Total Trade view across both channels, four data providers reconciled into a common model, manual Excel reporting replaced by a structured process, and the monthly refresh transferred to Campari's own support team. Brand, category and insight teams can now examine how the two sides of the market interact rather than reviewing them as separate stories.
“We are really pleased with the final dashboard and grateful for Optia's efforts. I'm excited to share this with our wider teams and stakeholders.”
Category and Insights Controller, Campari UK
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How Starbucks EMEA moved from fragmented monthly reports to a consistent multi-market view, then explored how AI could turn those numbers into commentary, answers and recommendations.
16 marketsInto one reporting model
Starbucks EMEA needed to understand performance across 16 markets, but the existing process produced 33 separate monthly reports covering countries, formats and the EMEA total. The information was available but difficult to compare, interrogate and trend consistently, and historical gaps made the picture harder to interpret. Analysts still had to work through the reporting manually to explain what had changed and why. The stated objective was not to replace dashboards or analysts.
Optia consolidated reporting across 16 countries into a single model, creating consistent views for individual markets, the wider EMEA region and key commercial cuts. Thirteen missing reporting periods were reconstructed, restoring historical continuity, and static monthly reporting was replaced with a more flexible, explorable environment.
A stronger foundation for understanding market, brand and category performance across the region, and the structured data foundation required for the next question: could the reporting explain itself?
Optia built an intelligence layer designed to read the underlying reporting and generate a first draft of monthly commentary automatically, with predefined prompts covering market performance, trend analysis and competitive movement. Distinct agent perspectives were created to reflect that a brand manager, a strategic leader and a data specialist need different things from the same numbers, covering brand positioning and activation, market priorities and investment, and statistical or structural significance, and the work explored a conversational way of interacting with the data, where a question could return a number, a written explanation or a generated visual.
The intelligence layer was tested against real market extracts, which proved the potential of automated commentary and role-specific interpretation, but also confirmed that where underlying market files were structured differently, AI couldn't make those inconsistencies disappear. The data still needed to be aligned first.
Starbucks EMEA moved from fragmented monthly reporting towards a more integrated approach to commercial insight: a consistent view across 16 markets, restored historical periods, and a clearer specification for the intelligence capabilities that mattered most, including role-specific perspectives and automated market commentary.
“A really real step forward versus what I saw before Christmas. Definitely moving in the right direction.”
Insights and Analytics, EMEA Channel Development, Starbucks EMEA
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A project delivered by the team now operating as Optia, bringing eleven adhesives and stationery categories into one commercial view, from total market performance down to individual SKU and retailer.
11 categoriesInto one consistent reporting model
Henkel competes across eleven adhesives and stationery categories, from stationery tapes and glue sticks to sealants, construction adhesives, wood glue, PU foams and humidity absorbers. Competitive sets, pack formats and retailer ranges all vary, and the same underlying product can appear as a single unit, multipack, refill or variant with different descriptions depending on retailer and source data.
More importantly, performance can change at a much lower level than the category total. A competitor might gain distribution on a particular SKU, or private label might gain share in a specific sub-category while the overall market looks stable, long before it becomes visible in the headline number.
The project consolidated market data across all eleven categories into a consistent hierarchy covering category, sub-category, pack structure, brand and individual SKU, resolving product mapping differences across retailers before the information reached the reporting layer.
The Power BI environment let users move progressively from total category performance through brand, sub-category, retailer and individual product results, with a dedicated retailer view across the three major grocers and competitor reporting showing Henkel brands (Loctite, Sellotape, Pritt, UniBond, No More Nails and Solvite) alongside private label and competitors including Bostik, Gorilla, Scotch, 151 and Securefix. Measures included unit and value sales, volume, average price per volume, weighted distribution, stores selling and household penetration across two years of weekly history. Measures are available across moving annual total, year-to-date, 52-week and shorter rolling periods.
The project created one consolidated source of UK market data across eleven categories, with a consistent view from total category down to individual SKU, competitor and private label performance alongside Henkel's own brands, and two years of weekly history for understanding trends. The environment shows where share is moving, what is driving it, and where the next commercial action might be required.
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How Optia supports the client data management and risk assessment processes behind a major bank's KYC operation, with auditability, resilience and continuity built into the delivery.
Zero Priority 1 incidentsDuring peak regulatory windows
Client details are withheld under a confidentiality agreement. The numbers and the work are real.
A global bank operating across multiple APAC jurisdictions has to manage more than one version of regulatory reality, since local requirements differ and individual KYC hubs can develop their own representation of the same client. The challenge isn't simply bringing information together; the bank also needs to demonstrate how a result was produced, which data was used, what rules were applied, and who interacted with the process. Peak regulatory windows make that requirement even more important, since the platform has to remain stable while volumes and scrutiny are at their highest.
Optia operates as a delivery and support partner within the wider programme, maintaining the data pipelines and global client inventory feeds that underpin the workflow, together with the date and business-rule logic required for regulatory processing. There is deliberately no dashboard layer; the value sits in making sure the right data reaches the right process with enough traceability to understand how the result was produced.
Optia implemented an automated daily activity feed delivered through secure file transfer, allowing user interactions with the platform to be monitored continuously, alongside stronger identity mapping, access controls and pre-peak system health checks. Optia also migrated the platform from PostgreSQL to a new analytical engine alongside a front-end upgrade, without interrupting the regulatory reporting cycle.
The programme has created a more centralised approach to managing client information across the regional KYC operation: a consistent client data management process across multiple jurisdictions, automated daily user activity monitoring, stable support through high-volume regulatory windows, and platform migration delivered without disrupting the regulatory cycle. Success here is measured by what doesn't happen: no critical outage, no broken regulatory cycle, no Priority 1 incident during the peak window.
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How Oracare Group created a consistent monthly view across a multi-country practice estate without changing how individual practices operated.
3 practice brandsInto one reporting model
Oracare Group operated three separate dental practice brands across multiple countries, each generating patient, treatment and revenue information in its own structure and following its own local processes. Producing a consolidated view meant bringing those sources together manually every month, and a revenue figure from one practice was only useful alongside another if both had been prepared using the same definitions.
The group didn't need a completely new reporting philosophy. It needed the information it already recognised to arrive consistently and mean the same thing across every setting.
The project consolidated data from across the practice estate into one prepared source, standardising different structures across brands and countries before they reached the reporting layer, so group-level figures could reconcile back to the underlying practices without requiring every local operation to change how it worked.
Rather than replacing the group's existing reports with something entirely new, the project replicated the reporting leadership already knew, then extended and delivered it through a shared dashboard as part of a regular monthly cycle, improving the consistency of the information underneath. Standardising in the preparation layer keeps change away from the practices, at the cost of concentrating ongoing maintenance in that layer.
Individual practices could continue operating locally, while the group gained a common view across brands and countries: a consolidated source of practice data across three brands and multiple countries, existing management reporting replicated and extended within a shared dashboard, and a consistent monthly reporting cycle. That made it easier to compare performance across the estate and understand how individual practices contributed to the group result.
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