In a public institution and at a utility, data is both an obligation and an asset. You have to file a return to the regulator on a specific day and format, and those same numbers could show where money leaks and where risk is building. The catch is that the data rarely lives in one place, and spreadsheets pulled together by hand before a deadline make neither a trustworthy return nor usable analytics.
In our experience, the hard part is not “producing the report” but building a repeatable, auditable process that recalculates the same numbers from the same sources every month. We will show which data and BI skills we can drop into your team, drawing on the RITS Group’s experience in Big Data and in building data platforms (including our data and AI teams within the RITS Group).
Why is data in a public institution so fragmented?
An institution rarely starts from scratch. Over the years it accumulates a layer of domain systems: billing, GIS, HR, finance and accounting, metering systems and registries, often from different vendors and across technology generations. Each has its own data model, dictionaries and its own notion of a “customer” or a “supply point”.
On top of that, reporting has for years rested on people rather than on a process: an analyst exports data from several systems, merges it in a spreadsheet, fixes it by hand and sends it off. That model stops holding up once there are more reports, deadlines are fixed, and someone asks where a particular number came from. So the first step is not a new dashboard but putting the sources in order.
What is a public sector data warehouse and what does it give you?
A public sector data warehouse is where data from many systems lands in one consistent model and one definition of the truth. We design it to answer the real reporting and management questions, not to copy every table from the sources. We usually build it in a few layers:
- a raw layer, into which we load source data unchanged, to keep a trace of the original,
- a model layer, where we clean, unify dictionaries and derive shared dimensions (time, area, customer, supply point),
- a reporting layer, with tables ready for specific returns and dashboards.
This split lets you add a new source or report without rebuilding the whole thing, and during an audit it is easy to show a number’s path from the return all the way to the source record.
How do you build pipelines for statutory reporting?
Statutory reporting plays by its own rules: what counts is not only the result but the fact that it was produced in a repeatable way. So instead of manual exports we stand up data pipelines that run on a schedule and load, transform and validate the data themselves.
We automate the full ETL/ELT cycle: pulling data from the sources, transforming it into the model, running quality checks, and generating the statements in the format the regulator requires. We rely on a proven stack (workflow orchestration, SQL transformations, logic versioned in a repository), chosen to fit what the institution already has.
On top of that we build in auditability, because the question “where did this number come from?” arrives sooner or later. We keep full data lineage from a line in the return to the source record, version the transformation logic (so a report from a quarter ago can be reproduced) and guarantee reproducible runs. An audit then means showing a documented process, not reconstructing calculations from memory.
Which management dashboards are worth giving decision-makers?
Once the data is in order, the management layer comes together quickly. We design management dashboards that answer a director’s questions rather than burying them in charts. For a utility these are sales and consumption metrics, loss levels, collections and network status; for an institution, budget execution, process load and case timeliness.
We stick to the rule that one screen supports one decision: instead of dozens of tiles we give a few metrics with context (value, trend, a reference to plan, a drill-down). We feed them from the same model layer as the returns, so the board and the regulator look at consistent numbers, not two different truths.
How do you publish open data through an API?
More and more institutions are obliged to make data available externally. Open data is not, however, dropping a CSV onto a website, but a maintained, documented and secure publication channel.
We build the publication layer so that data flows to the open-data portal or an API from the same warehouse as the returns. We handle a stable schema, metadata and versioning, anonymisation where personal-data protection requires it, and automatic publication. As a result, the open data stays current and consistent with official reporting.
How do you analyse consumption and losses in utilities?
At a utility, the most value hides in consumption and loss analytics. When metering, billing and network data land in one model, you can compare what was produced and what was billed and see the gap, that is, technical and commercial losses.
On that basis we build consumption-profile analysis, anomaly detection (unusual draw pointing to a fault or theft) and demand forecasting. Where it makes sense, we add a layer of predictive models - the AI and machine-learning skills live for real inside the RITS Group - trained on a specific network’s data rather than shipped out of the box.
How do you ensure data quality and security?
A report is worth as much as the data beneath it. We wire automated checks into the pipelines: completeness, consistency and validity rules that stop faulty data before it reaches a return.
Security in the public sector is non-negotiable. We tie access to roles, encrypt sensitive data, separate environments and log operations on personal data for GDPR compliance. We also design retention and anonymisation, so that analytical and open data do not become a back door to sensitive records.
Which skills do we provide for a data project?
Even without a case study from a specific authority, we can place people who build such systems into your team. We staff roles matched to the stage of the project:
- a data / warehouse architect who designs the model and the layers,
- a data engineer who builds and maintains the pipelines,
- a BI specialist who owns the reports and dashboards,
- an analyst or ML engineer when prediction and anomaly detection come into play.
We work in a team augmentation (body leasing) model or as a dedicated team, depending on whether we fill gaps or take over the whole area. As a nearshore partner from Poland, we join your working rhythm fast.
How do we do it at RITS?
We build on real data and BI skills inside the RITS Group, not on stories about someone else’s rollouts. We design a warehouse and data model for reporting and analytics, automate statutory reporting with full auditability, develop dashboards, open data and consumption analytics on one layer, and add AI/ML skills where they genuinely pay off. Most often we do this as custom software built by our team and, where people need topping up fast, as team augmentation.
Where to start with reporting and analytics?
Start from the single report that hurts the most: work out which sources it comes from, what its definitions are, and where it is still done by hand today. That defines the scope of the first warehouse version, the first pipelines and the roles you need. Further reports go on top of a foundation that already works. We covered the differences between engagement models in our article on IT engagement models.
Building statutory reporting or analytics in an institution or at a utility? Let’s talk - we’ll match a team and an architecture to your data.