Most businesses are not short of reports. They are short of one agreed set of numbers, and an explanation of why those numbers moved.
The usual reason a business brings in a data analytics consultant in Australia is not that reporting is missing. It is that there are five versions of it. Sales quote one revenue figure, finance quotes another, and the difference turns out to be whether freight recharges count. Meanwhile the questions that actually decide next quarter — which sites are carrying the margin, which jobs run over and why, which customers quietly stopped ordering — have no answer at all, because the data needed to answer them lives in three systems that have never spoken.
This page is about the layer underneath the screens. Getting data out of Shopify, Cin7, Xero, MYOB, HubSpot, SimPRO, SQL Server and the spreadsheets, landing it somewhere consistent, and defining each metric once so the whole organisation means the same thing by "active customer" or "on-time delivery". If what you want is the interface on top — the screens people actually look at each morning — that is our custom dashboards work, and it is a different job. Dashboards built on a data layer nobody agrees with just spread the disagreement faster.
It is also worth separating reporting from analytics, because the words get used interchangeably and they are not the same purchase. A report tells you what happened: revenue was down four per cent last month. Analytics tells you why and what to do: it was down because two stores lost a supplier line, the gap shows up in one category, and the same line is at risk in three more. Reporting is a feed. Analytics is an argument, and it takes better-organised data to support one.
A central store your systems feed on a schedule, so nobody is exporting CSVs by hand. Usually a straightforward warehouse rather than an expensive platform — the point is one copy of the truth, not an impressive stack. Where systems need to talk to each other directly as well, that is system integration work alongside this.
Written definitions for the numbers your business argues about: what counts as revenue, when a job is complete, what "active" means for a customer, whether GST and freight are in or out. Boring work. It ends more meetings than any chart.
Plenty of the important things are not in any system yet — how long a quote sat before someone responded, why a job was rescheduled, which shifts ran short. We add lightweight capture at the point it happens so the data starts existing.
Duplicate customers, three spellings of the same supplier, stock counts that never agree between the retail POS and the stock system. We find the disagreements, quantify them, and put checks in place that flag them the day they appear rather than at year end.
Cohort and retention analysis, margin by product, site and customer, variance analysis on jobs and mining maintenance and shutdown work, leading indicators for the things you currently discover too late. Written up in plain language with the working shown.
Scheduled reporting into email, Teams or Slack, plus alerts when a number crosses a threshold you set. Most people do not want to open a tool daily. They want to be told when something has moved.
We start with the decisions, not the data. Which recurring choices are being made on incomplete information, and what would you need to see to make them with confidence? That list sets the scope, and it usually turns out to be shorter than expected.
We map every system holding relevant data, what it actually contains, how clean it is, and how we can get it out. This is also where we find that a metric you have been reporting for years is being calculated two different ways.
We agree the metric definitions in writing and settle what gets tracked, at what grain, and how often it refreshes. Daily is not always better than weekly, and hourly refreshes on data entered weekly is just expensive noise.
We build the pipelines and the store, connect the source systems, and put reconciliation checks in place so you find out when a feed breaks instead of discovering it in a board pack.
Definitions change as the business changes, and new questions appear once people can finally see the old ones answered. Monthly tuning is part of the subscription rather than a change request.
This page is the data layer and the measurement: getting data out of your systems, cleaning it, and defining what each number means. Dashboards are the interface on top of it. Some clients need only one of the two. If you already trust your data and simply cannot see it well, start with dashboards instead.
We charge a monthly subscription with a three-month minimum term, monthly billing and 30 days' notice to cancel, rather than a day rate or a fixed project fee. The amount depends on how many systems we connect and the state of the data in them. Pipelines, definitions, analysis and ongoing changes sit inside that subscription.
Expect a few hours a week for the first two or three weeks, mostly from whoever knows how the systems are used in practice. Agreeing metric definitions is the part that genuinely needs your people, and it cannot be outsourced — we can facilitate the argument, but the business has to settle it.
Not always. If everything you need sits in two systems with decent APIs, we can often connect them directly and skip the extra infrastructure. A central store earns its place once you have several sources, history that outlives what the source systems retain, or numbers that must be reconciled across systems.
No, but be realistic about sequencing. We clean the fields the agreed metrics depend on rather than attempting the whole estate, because a full data clean-up with no reporting attached tends to stall. Fixing the input systems is often part of it, which is where replacing the spreadsheets comes in.
In Australian regions — Azure Melbourne for anything we host. Your source systems stay wherever they are now. We can also build against a warehouse you already own if you would rather keep everything in your own tenancy, which some clients in regulated industries prefer.
A first conversation costs nothing and usually ends with a clearer idea of what is worth building — sometimes that answer is “not yet”, and we will say so.
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