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 Discover, Design, Develop, Deploy 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
Discover
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
Design
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
Develop
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
Deploy
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.
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.