AI & Data Solutions
Data, Analytics & Machine Learning
Data you can trust, dashboards people actually use, and predictive models where they are worth building.
Discuss Your ProjectIn plain terms
Before AI can help, your figures have to agree with each other. This service gets your data from several systems into one trustworthy place, puts it in dashboards people actually use, and, where it makes sense, builds models that forecast or flag things from your history.
Does this sound familiar?
- The sales figure in one report is not the sales figure in another
- Someone rebuilds the same spreadsheet every month-end
- Nobody trusts the data enough to build anything on it
- You find out about problems after they have cost money
A typical scenario
A regional logistics firm
Before
Depot managers each keep their own spreadsheet. The weekly board pack takes a day to put together and the numbers rarely match between depots. Staffing for the busy weeks is a guess based on last year.
After
Orders, deliveries and timesheets feed one set of figures overnight. A Power BI dashboard gives each manager their depot and the board the whole picture. A forecast shows expected volume for the next six weeks, with a range, and the managers staff against it.
How we deliver it
From first call to running
- 1
Find the figures that matter
We agree the ten or so measures the business really runs on, and what each one means.
You get: A data dictionary, with each measure defined once
- 2
Connect and clean
Pipelines bring the data together and flag bad records.
You get: A working data store, refreshed automatically
- 3
Build the dashboards
Power BI reports built with the people who will use them.
You get: Reports in daily use
- 4
Forecast or flag, if it is worth it
We test whether your history can predict the thing you care about, against a simple baseline.
You get: A yes or no with evidence, then a model if it is a yes
- 5
Keep it healthy
Data quality checks, and monitoring of any model.
You get: Alerts and a monthly summary
What we need from you
- Read access to the source systems
- Agreement on definitions between departments
- A few users to shape the dashboards
- For forecasting, history: ideally two or more years
Where it works best
- Figures spread across systems and spreadsheets
- Repeated manual reporting
- Repeating patterns, such as seasonal demand
Worth sorting out first
- Little history so far: begin with dashboards and keep collecting data until a forecast is possible
- Data you do not record today: start capturing it, then add it to the reporting
- Problems that are really about process: we look at the process alongside the data
How we work with you
Usually staged: a short assessment, a fixed-price first dashboard, then ongoing support. A forecasting model is only started if the assessment shows it would work.
Discuss Your ProjectTypical technology
Questions
Do we have to clean up our data before we start?
Not all of it. We pick a first use case where the data is already good enough and fix the rest as we go.
How much data does a forecast need?
It depends on the question, and a short assessment will answer it. Usually we suggest a small proof of concept with a clear measure of success.
Do we need to move our data to the cloud?
Not necessarily. Many reports can run from your existing SQL Server. The cloud helps when data comes from several places or the volumes are large.
Will Power BI replace our spreadsheets?
For reporting, usually yes. People will still use spreadsheets for odd jobs. The aim is that the numbers in them come from the same source.
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