Reporting volume has grown, decisions have not improved
There are hundreds of reports and dashboards in circulation. Few of them are tied to a decision, an owner or an action, so leadership still asks for a manual summary before a meeting.
Analytics & AI Solutions
Analytics designed around the decisions you have to make.
Organisations rarely lack reports. What they lack are data and AI solutions designed to achieve a specific outcome by informing a decision, prompting an action or automating it altogether.
This work is for executive teams, finance and operations leaders, and heads of data who want to move beyond reporting what has happened and use data and AI to improve what happens next.
You finish with agreed measures of success, insights that support key decisions and AI solutions that improve or automate specific processes, all with clear ownership and measurable outcomes.
Common challenges
What we hear before an analytics engagement starts.
These situations are common in organisations that have already invested in reporting tools. The gap is usually in decision design, definitions and adoption rather than technology.
The executive pack is assembled by hand
Senior reporting depends on someone exporting, reconciling and reformatting figures each cycle. The effort is invisible until that person is unavailable, and the numbers arrive too late to change anything.
Definitions are contested
Revenue, active customer and margin mean different things in different functions. Meetings are spent agreeing whose number is right rather than deciding what to do about it.
Dashboards are built but not adopted
Reporting was delivered to a specification rather than designed around how people actually work. Usage falls away within weeks and the spreadsheets return.
AI use cases are described in general terms
There is pressure to show AI progress, and the ideas on the list are not yet connected to a specific process, owner or measurable benefit. Nothing reaches production because nothing was scoped to.
Operational teams have no timely view of performance
Frontline and operational managers work from yesterday’s extract or a monthly report, so problems are diagnosed after they have already cost something.
Our approach
Start with the outcome, not the dataset.
We use our Unearth, Reveal, Build, Achieve engagement model, and our DIAlog framework to understand the organisational factors that determine whether analytics gets used. Adoption is part of delivery, because data only creates value when it changes what people do.
Analytics and AI solutions, defined
Analytics and AI solutions turn data into something useful, whether that is insight that informs a decision, a prediction that prompts an action, or an algorithm that automates a process. Effective solutions start with a clear outcome, use trusted and traceable data, have clear ownership and agreed measures of success, and are embedded into how the organisation works.
Stage 01
Unearth
We start from decisions rather than data. We work with the people accountable for performance to understand which decisions are hardest to make well today, what information they need, how often, and what they would do differently with it. We also establish which figures are already trusted and which are contested.
Stage 02
Reveal
We agree metric definitions and ownership, then design the data or AI solution around the decision it supports. That includes the audience, the cadence, the level of detail, the action the output should prompt and how success will be measured. Where AI or ML is appropriate, we scope the specific process and recommendations it will improve.
Stage 03
Build
We build in short cycles with the users involved, so the solution is tested against real questions rather than a specification. Reporting is engineered on governed data with documented logic, and models are validated against business measures as well as technical ones.
Stage 04
Achieve
We focus on adoption: training, embedding the output into existing operating rhythms and platforms, agreeing ownership of definitions and the review points where the model is refined or retired. Analytics that nobody uses is a cost, so adoption is part of the delivery rather than an afterthought.
Business outcomes
What changes as a result.
We describe outcomes in the terms our clients use about the work.
Reporting tied to decisions
A smaller number of outputs that each support a named decision and a named owner, instead of a library nobody trusts.
Agreed measures across functions
Shared definitions and calculation logic, so the leadership conversation moves from reconciling figures to acting on them.
Time released through automation
Manual reporting, repetitive analysis and routine processes are automated where it makes sense, freeing skilled people to focus on analysis, exceptions and higher-value work.
AI applied to a specific process
Practical use cases scoped against a real workflow, with the data requirements and success measures established before build.
Earlier visibility of performance
Operational teams see what is happening in time to act, rather than reading about it in a report next month.
Analytical capability in your team
Your analysts leave the engagement able to extend the models, maintain the definitions and build the next solution themselves.
Relevant client work
Analytics work we have delivered.
The case studies set out what the organisation needed, what we built and what it produced.
Data Understood immediately understood what we were trying to do, both in terms of the technical aspects of the data and the implications and possibilities for the business model.
The empathetic and supportive approach, quick understanding of what we were trying to achieve, and patience in supporting us through the process.
Data Understood's deep understanding of our problems and ability to communicate with non-technical teams in a very positive manner is impressive.
Where to go next
Related services, sectors and reading.
Analytics is where the value becomes visible. It depends on a platform that can be trusted and a strategy that agreed what matters, and the sector context shapes which decisions come first.
Related services
Sector context
Buying questions
Analytics and AI: questions enterprise buyers ask.
Written for the people who have to justify the investment internally, covering how reporting and applied AI work is run in practice.
Decision intelligence is the practice of designing analytics around specific recurring decisions rather than around available data. It starts by identifying the decision, who owns it, how often it is made and what information would change it, then builds the reporting, models or AI to serve that decision and measures whether the decision improved. It is a discipline for making analytics useful rather than a product category.
We map decisions to audiences. Board and executive reporting answers a small number of questions about direction and risk. Management reporting supports allocation and intervention. Operational reporting supports action within the working day. Anything that does not serve a decision at one of those levels is a candidate for retirement, which usually reduces the reporting estate rather than growing it.
A short set of measures the executive team has agreed, defined consistently and refreshed reliably. Clear comparison against plan or prior period. Enough context to interpret a movement without a second document. A visible owner for each measure. Above all, it should be the version of the truth used in the meeting, which requires the leadership team to commit to it rather than maintain a parallel spreadsheet.
Yes, Power BI is the most common tool across our client base, particularly where Microsoft Fabric or Azure underpins the data platform. We are not tied to one tool, and we would rather work in the technology your team can maintain. The value comes from the modelling, the definitions and the design, and those transfer between tools.
Choose a decision that is made repeatedly, where history exists, where the outcome is measurable and where someone can act on the prediction. Forecasting demand, prioritising outreach and predicting churn or renewal are common starting points. Predictive work fails most often when nobody agreed in advance what action a prediction would trigger.
Define the process it improves, the person accountable for that process, the data it needs and where that data comes from, the measure of success, and what happens to the existing way of working once it is live. Agree the governance and assurance requirements before build. Pilots stall when they were scoped as experiments with no route into production.
For reporting and dashboards, a first usable output within weeks is normal where the underlying data is available. Where definitions are contested or data must be engineered first, that groundwork sets the pace. AI applications take longer because validation and assurance are part of the work. We sequence delivery so something useful lands early.
Building to a list of requested fields rather than a decision. Skipping agreement on definitions. Treating adoption as training at the end. Adding new reporting without retiring the old. Choosing AI before establishing whether reliable reporting would answer the question more cheaply. And building on data that has not been engineered to be trusted.
Usually yes, with the caveat that we will be explicit about what the data can and cannot support. Analytics work often surfaces quality issues faster than a remediation programme does, because a contested figure in front of an executive team creates the will to fix the source. We agree what is handled in transformation and what needs fixing upstream.
We agree the measure before build. That might be a decision made more quickly, a manual process removed, an intervention made earlier, or a forecast that is accurate enough to plan against. We avoid measuring success by report count or dashboard views, because neither tells you whether the business acted differently.
The person accountable for the decisions the work supports, an owner for each metric definition, someone from the data or engineering team who understands the sources, and the users who will work with the output daily. Executive sponsorship matters where definitions cross functional boundaries.
Yes. We are based in Dundee and work with ambitious organisations across Scotland and the UK. This work involves onsite sessions with the teams who own the decisions alongside remote delivery, and we agree that balance with you at the outset.
Let's talk about your Data & AI priorities.
Whether you're developing a data strategy, modernising your data platform, preparing for AI or tackling a specific business challenge, start with a 30-minute conversation with Inez.