Our Services
Data Analytics Services
Most businesses have more data than insight. It sits in separate systems that were never designed to be read together — orders in one place, customer records in another, support history in a third, marketing spend in a fourth — and answering a question that spans two of them means somebody exporting spreadsheets and reconciling them by hand. The result is that questions get asked less often than they should, and decisions get made on the impressions of whoever is in the room.
Atisolve builds the infrastructure and reporting that makes those questions cheap to answer. That means getting data out of source systems reliably, resolving the inconsistencies between them, and presenting the result in a form people actually use. We work with businesses that have no reporting beyond their operational systems, and with businesses whose dashboards exist but are not trusted.
Why Reporting Projects Disappoint
Analytics projects fail in recognisable ways, and the causes are rarely technical sophistication. Understanding them shapes how we approach the work.
- Nobody agrees what the numbers mean. Two departments define 'active customer' differently, so their reports disagree and both are dismissed as unreliable.
- The dashboard answers questions nobody asked. Built from what was easy to measure rather than from the decisions the business actually makes.
- The data is wrong often enough to lose trust. One visible error is enough for people to stop relying on a report entirely, and trust is far harder to rebuild than to establish.
- It is too slow to be useful. A report that takes a minute to load will not be explored, and exploration is where insight actually comes from.
- It shows what happened but not what to do. Metrics without context or comparison invite interpretation rather than action.
We address these by starting from decisions rather than from data, defining metrics precisely and in writing before building anything, and treating data quality as a continuous requirement rather than a one-off cleanup.
What We Deliver
Data Integration
Reliable pipelines that bring data out of operational systems, marketing platforms and third-party services into one place where it can be queried together.
Data Warehousing
A modelled central store designed for analysis rather than transactions, so queries spanning years and systems complete in seconds rather than straining production databases.
Dashboards and Reporting
Interfaces built around the decisions people make, with the context and comparisons that turn a number into something actionable.
Self-Service Analytics
Structuring data and defining metrics so that people can answer their own questions without an analyst in the loop for every request.
Data Quality
Validation, monitoring and alerting so problems are found by the system rather than by someone noticing an implausible figure in a board pack.
Predictive Analytics
Forecasting and modelling where historical patterns genuinely support prediction — demand planning, churn risk, inventory — and honest advice where they do not.
Our Approach
Starting From Decisions
We ask what decisions the business makes regularly and what information would improve them. This is a different question from what data exists, and it produces different answers. A report that changes what somebody does is worth more than twenty that are merely interesting, and starting here prevents building the latter.
Defining Metrics Precisely
We write down exactly what each metric means — which records are included, how edge cases are treated, what time period applies, how refunds and cancellations are handled. This is tedious and it is the single most valuable step, because most disputes about numbers are actually disputes about definitions that were never made explicit.
Auditing the Sources
We examine the systems the data comes from and assess what is actually reliable. Fields that are optional in practice, records entered inconsistently, historical data that changed meaning when a process changed — these determine what can honestly be reported and are better discovered now than after a report has been published.
Building Pipelines
We build the extraction and transformation that moves data into the warehouse, with the operational qualities that matter: incremental loading rather than full refreshes where volume demands it, idempotency so a rerun does not duplicate, and alerting when a source stops delivering rather than silent staleness.
Modelling for Analysis
We model the warehouse for querying rather than mirroring source system structures. Operational schemas are designed for transactions and are usually poor for analysis. A deliberate analytical model makes queries simpler, faster and less error-prone for whoever writes them next.
Building and Refining Reporting
We build dashboards, then watch how they are used. The first version is always partly wrong — questions arise that were not anticipated, and some panels are never looked at. Reporting improves substantially through a few iterations of real use, and stagnates when it is delivered and abandoned.
Data Infrastructure in Practice
Separating Analytics From Production
Running analytical queries against production databases is common and inadvisable. Analytical queries scan large volumes and compete with the transactions your business depends on, so a heavy report can slow the application for customers. Production schemas are also optimised for writing rather than reading across time, which makes analytical queries both awkward to write and slow to execute. A separate warehouse resolves both problems and additionally allows history to be retained after operational systems have archived or overwritten it.
Handling Change in Source Systems
Source systems change without warning to downstream consumers. A field is repurposed, a category renamed, an integration updated, a product line restructured. Pipelines that assume stability break quietly, and the failure often shows up as a plausible but wrong number rather than an error. We build validation into pipelines — expected ranges, referential checks, row count comparisons against previous loads — so that unexpected change raises an alert rather than propagating into reports.
Historical Accuracy
Business reality changes, and reports that ignore this mislead. If a customer moves between segments, or a product is recategorised, or a sales territory is redrawn, should last year's figures be restated under the new arrangement or preserved as they were? Both answers are defensible for different purposes and the wrong one produces confusion when someone compares a report against one they ran previously. We make this decision explicitly per dimension rather than letting it be determined accidentally by how the pipeline happens to work.
Performance
Dashboards that take a long time to load are not explored, and exploration is where analysis becomes insight. We address this through appropriate modelling, pre-aggregation of expensive calculations, incremental processing so only new data is transformed, and warehouse-level features such as partitioning and clustering. The objective is that a person can follow a question wherever it leads without waiting, because the moment they have to wait they stop asking.
Dashboards People Actually Use
A dashboard is a user interface and suffers when treated as a data dump. The most common fault is showing everything available, which forces the reader to find the signal themselves every time. We design around a small number of questions per view, with the most important information most prominent, and detail available on demand rather than displayed by default.
Context is what turns a number into a decision. A figure alone rarely means anything — the reader needs to know whether it is better or worse than last period, how it compares to target, and whether the change is beyond normal variation. We include comparisons deliberately, and we are careful about charts that mislead: truncated axes exaggerating small changes, dual axes implying relationships that do not exist, and pie charts with too many segments to read.
Where a report is meant to prompt action, we make the action explicit. A list of accounts at risk of churning is more useful than a churn rate, because it can be worked through. Reporting that ends at description leaves the hardest step to the reader, and that step is frequently not taken.
Predictive Analytics and Its Limits
Prediction is useful where history genuinely constrains the future: demand that follows seasonal patterns, equipment that fails in observable ways, customers whose behaviour changes measurably before they leave. In these cases a model can outperform judgement consistently and at a scale a person could not match.
It is less useful where the past does not resemble the future, where the events being predicted are rare, or where the data available does not actually contain the signal. We are direct about this, because a model presented with more confidence than it deserves does more damage than no model — decisions get made on it, and the errors are systematic rather than random. Where we do build predictive models, we report their accuracy honestly and design for the case where they are wrong.
Technologies We Work With
- Warehouses — cloud data warehouses and PostgreSQL, chosen against data volume, query patterns and budget rather than by default.
- Pipelines — scheduled and event-driven ingestion in Node.js and Python, with orchestration, retries and monitoring built in rather than added later.
- Transformation — SQL-based modelling with version control, testing and documentation, so the logic behind a number is inspectable.
- Visualisation — mainstream BI tools, or custom dashboards built into your own application where reporting should sit alongside the product.
- Sources — operational databases, CRM, e-commerce and accounting platforms, advertising and analytics APIs, and flat file exchanges from systems with no API.
- Quality — automated validation, freshness checks and alerting on anomalies in volume or distribution.
Getting Started
The most productive first conversation is about the decisions you make regularly and what you currently lack to make them well. That is a better starting point than an inventory of systems, because it establishes what the work is for and prevents building reporting that is technically impressive and practically unused. Get in touch and tell us what questions you cannot currently answer.
Frequently Asked Questions
We already have dashboards nobody trusts. Where do we start?
With definitions rather than technology. In most cases the distrust traces to metrics that were never defined precisely, so different reports legitimately produce different answers to what sounds like the same question. Agreeing and documenting definitions, then rebuilding on that basis, resolves more than replacing the tool does.
Do we need a data warehouse, or can we report from our existing systems?
If your reporting needs are met within one system and performance is acceptable, you may not need one. A warehouse becomes worthwhile when questions span multiple systems, when analytical queries affect production performance, or when you need history that operational systems do not retain. We will say when it is not yet justified.
How do we handle inconsistent data across systems?
Through explicit resolution rules rather than hope. Which system is authoritative for a customer's address, how records are matched when identifiers differ, what happens when two sources disagree — these are decisions to be made and documented. The alternative is that the pipeline makes them implicitly and nobody knows what was chosen.
Can our team maintain this afterwards?
That is a reasonable objective and it shapes the build. It means favouring SQL-based transformation your analysts can read over opaque tooling, documenting the model, and keeping the architecture as simple as the requirements allow. We would rather hand over something maintainable than something impressive.
How long does it take to get useful reporting?
A focused first deliverable covering a defined set of questions from a small number of sources can be useful within weeks. Broad coverage across many systems takes longer, and the source data audit frequently uncovers work nobody anticipated. We prefer to deliver something used quickly and extend it, rather than build for months before anyone sees anything.
What ongoing work does this need?
Pipelines need monitoring because source systems change without warning to downstream consumers. Metric definitions need revisiting as the business changes, since a definition agreed two years ago may no longer describe how the company operates. Reporting needs extending as new questions arise, which they continually do once people start trusting the numbers. It is not a project that finishes, though the effort after the initial build is considerably lower and can often be absorbed by an internal team.
How current does our reporting need to be?
Less current than most people request, and the difference matters because real-time pipelines cost substantially more to build and operate. The useful question is what decision the data supports and how often that decision is made. Reporting reviewed weekly does not need to be updated every minute. We match refresh frequency to decision frequency rather than to preference.
Can we connect our reporting to spreadsheets our team already uses?
Yes, and it is often the pragmatic answer. Spreadsheets remain the tool many people are fastest in, and forcing them into a dashboard they do not want produces resistance. Exporting governed data into a spreadsheet is far better than the alternative, which is people exporting raw data and calculating their own inconsistent versions of the same metric.
Who should own the metric definitions?
The business, not the technical team. Analysts and engineers can implement a definition faithfully, but only the business can decide whether a cancelled order counts as a sale. What we provide is the discipline of making the decision explicit, writing it down and applying it consistently everywhere the metric appears.
Is our data volume too small for a warehouse to be worthwhile?
Volume is rarely the deciding factor. The case for a warehouse rests on whether questions span multiple systems, whether analytical queries affect production performance, and whether you need history that operational systems discard. A business with modest data across six disconnected systems benefits more than one with large data in a single well-structured system.
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