Data analytics services that turn raw data into decisions.

Data analytics services cover everything between the data your platform already produces and a decision someone can act on. In practice that is four layers. Ingestion moves data out of your product database, your app and website event streams, and third-party sources such as Meta, Google, TikTok, HubSpot and Klaviyo. A warehouse - typically BigQuery or Snowflake - holds it in one place. Transformation, which we run in dbt so the logic is version-controlled, tested and reviewable rather than buried in a spreadsheet, turns raw tables into a defined metrics layer. A business intelligence dashboard then presents those metrics to the people who need them.

The part most providers skip is the metrics layer. Without an agreed definition of what an active user is, or when a subscription counts as churned, two dashboards built off the same warehouse will disagree, and the argument moves from what to do about the number to whose number is right. Defining those metrics is a business exercise, not a technical one, which is why it happens in Phase 1 Scoping & Design alongside the architecture.

Data analytics services in Australia are not sold off a rate card at PixelForce; the cost sits inside the standard engagement structure and is driven by how many sources are integrated and how much reporting is bespoke.

There is no rate card, because the cost is driven by how many sources have to be integrated, how messy they are, and how much of the reporting has to be bespoke. What we can give you is the structure, and it is the same as every other engagement we run.

Phase 1 Scoping & Design is mandatory before any build. It produces the Business Requirements Document, the metrics definitions, the architecture, a Product Requirements Document and a fixed-cost Statement of Work, so the build number is known before you commit to the build. Phase 2 Development, QA and Release is quoted against that Statement of Work, and what moves it is the number of data sources feeding the warehouse, the state they arrive in, how much historical data has to be back-filled, and how many bespoke reports and dashboards are in version one. Where a budget stretches further than the scope requires, we still advise keeping version one tightly scoped and spending the remainder on iteration once real usage tells you which reports people actually open.

After launch, Phase 3 has two options. Option 1, Warranty, Monitoring & Support, is a flat monthly fee covering critical-defect warranty, 24/7 infrastructure monitoring, business-hours incident response and a monthly Platform Health Report, with technical support capped at seven hours per month. Option 2, the Product Retainer, includes everything in Option 1 and adds a roadmap workshop, continuous sprints and quarterly business reviews, priced per four-week cycle against a committed story-point capacity, in tiers named Steady, Growth, Scale, Velocity and Momentum, with Enterprise on application. Most analytics work belongs in Option 2, because a dashboard that never changes stops being used.

Third-party costs sit outside all of that and are billed to you directly by the provider. Warehouse, ingestion and BI licensing all price on their own terms and change regularly, so check the current rates published by BigQuery, Snowflake and Power BI rather than relying on a figure quoted on an agency website.

Business intelligence and data analytics overlap, but the useful distinction is that business intelligence is mostly descriptive, telling you what happened and what is happening now, while data analytics goes further into why it happened and what is likely next.

The two terms overlap enough in marketing copy to be treated as interchangeable, but the distinction is useful when you are deciding what to buy.

Business intelligence services are mostly descriptive. They tell you what happened and what is happening now: revenue this month against last, active users by cohort, which gyms or stores or instructors are performing, where the pipeline sits. The output is a dashboard someone opens on a Monday. Data analytics goes further into why something happened and what is likely to happen next - segmentation, cohort retention analysis, churn signals, forecasting and predictive analytics.

The practical point is that both sit on the same foundation. You cannot do credible predictive work on data that has not been ingested reliably, warehoused consistently and transformed against agreed definitions. Businesses that buy the predictive layer first almost always end up rebuilding the foundation underneath it within a year. We recommend earning the second by getting the first right, and we will say so at the consultation even when the more expensive answer would have been an easier sell.

Frequently a dashboard is enough, and we will tell you that rather than sell you a warehouse you do not need. If all your reporting questions can be answered from one system - your product database, or your e-commerce platform - and the volume is modest, querying that system directly and presenting the result is cheaper, faster and easier to maintain.

A warehouse earns its place when one of three things is true. First, the answer requires joining sources that do not talk to each other, such as product usage against marketing spend against support tickets. Second, reporting queries have started to compete with the live application for resources, which shows up as the app slowing down whenever someone opens a report. Third, you need history that the source systems overwrite, so that a question asked in twelve months about what a cohort looked like at signup can still be answered.

That third point is the one businesses regret ignoring, because the fix is not retrospective. Data you did not capture is simply gone. If you are close to any of these thresholds, capturing to a warehouse early is cheap insurance even if you keep reporting off the source system for now.

Data engineering is the plumbing beneath the reporting, and it is where most of the effort in a data project genuinely goes. Concretely we build the connectors that pull from each source, the scheduling and orchestration that runs them, the schema the warehouse holds, the transformation models in dbt with their tests and dependencies, and the monitoring that tells you when a pipeline has failed before a stakeholder tells you the dashboard looks wrong.

Two engineering properties matter more than anything visual. Idempotent reprocessing means you can safely re-run a pipeline over a period without double-counting, which is what lets you fix a historical bug rather than living with it. Real-time capture at the point an event happens means the record is written when the truth is known, rather than being reconstructed later from whatever state the database happens to be in.

Both of those are exactly what we built into the EzLicence credit ledger. When EzLicence financial reports would not balance, we replaced report-time reconstruction across a dozen unrelated tables with a canonical double-entry credit ledger covering 18 event types, with real-time capture and idempotent reprocessing. Every liability report now draws from one reconcilable source, finance discrepancy complaints stopped, and outstanding credit is aged into time buckets and categorised by source for cash-flow decisions.

PixelForce does both Power BI consulting and custom dashboard development, and which one you get is decided in Phase 1 against your situation rather than assumed, because we are not resold into any BI vendor.

Both, and the choice is made in Phase 1 rather than assumed. We are not resold into any BI vendor, so the recommendation reflects your situation rather than our licensing arrangements.

An off-the-shelf BI tool such as Power BI, Looker or Metabase is usually the right answer when the audience is internal, when your team wants to build and change their own reports without a developer, and when the metrics are standard enough that a general-purpose tool expresses them well. It is faster to stand up and cheaper to maintain, and self-service is a genuine advantage. Check the vendor for current licensing, since per-user and capacity pricing both change.

A custom dashboard is the right answer when the reporting is customer-facing rather than internal, when it must live inside your existing product with your existing authentication and permissions, or when the visualisation is specific enough that a general tool fights you. Franchise and multi-site operators run into this constantly: a franchisee should see their own site and the network benchmark and nothing else, which is a permissions problem a generic BI licence handles awkwardly.

Either way the layers underneath - ingestion, warehouse, transformation, metrics - are identical, which means the presentation layer can change later without rebuilding the foundation.

How long a working dashboard takes depends far more on the state of your data than on the size of the build: weeks where the data is clean and centralised, months where it is scattered or was never captured.

It depends far more on the state of your data than on the size of the build, and we will be able to tell you which situation you are in after the consultation.

Where the data is already clean and lives in one or two systems with sane structure, a first useful dashboard is a matter of weeks. Where the data is spread across several systems that disagree with each other, or where key events were never captured in the first place, the honest answer is months, because the work is not building the dashboard - it is establishing a trustworthy number for it to display.

What we can commit to is sequencing. We do not disappear for a quarter and return with everything. Phase 1 produces the architecture and the metric definitions. The build then delivers in milestones, with the highest-value reports first, so that something is in your hands and being argued about early. Arguing about a real dashboard is the fastest way to discover that a definition everyone agreed to on paper was wrong.

For comparison on delivery pace: we shipped the AI knowledge system for EzLicence, The Handbook, in 4 weeks, and it delivers a 50 percent efficiency gain across workflows while automating 90 percent of documentation updates with 10 percent human oversight.

You own your data on every PixelForce data engagement and it stays on your own cloud infrastructure; who owns the dashboard itself depends on whether you choose the Data-as-a-Service subscription or the Custom Build pathway.

Your data is always yours and it always stays on your own infrastructure. That is not a commercial position adopted for one service line, it applies to every PixelForce engagement, including all data services. Client intellectual property is hosted on the client's own cloud account.

Ownership of the platform itself depends on which pathway you choose, and the two are genuinely different products rather than a good option and a cheap option. Data-as-a-Service is a monthly subscription: we build, run and maintain the ingestion, warehouse and dashboard, and you log in and use it. It goes live in weeks, ongoing maintenance and future roadmap releases are included, and you can change tier as your needs move. It suits businesses who want to prove the value of reporting quickly and treat it as an operating expense.

The Custom Build pathway delivers the same architecture as an owned asset for a one-off project fee. You own the ingestion, warehouse and dashboard outright, with an optional maintenance retainer afterwards. It takes months rather than weeks because it includes Scoping & Design and bespoke development, and it suits businesses that want a long-term data asset on their balance sheet. Intellectual property transfers on full payment.

Yes, PixelForce fixes financial and operational reports that do not balance, and the cause is almost never the report itself: it is a number being reconstructed at report time from tables never designed to be summed together.

Yes, and it is one of the more common reasons a business first calls us about data rather than about an app. The symptom is always the same: two reports that should agree do not, finance stops trusting the numbers, and someone starts maintaining a parallel spreadsheet that becomes the real source of truth.

The cause is almost never the report. It is that the number is being reconstructed at report time by querying several tables that were never designed to be summed together, so the answer depends on the order things happened and on edge cases nobody enumerated. Adding another report on top of that does not help.

The fix is architectural. For EzLicence we replaced report-time reconstruction across a dozen unrelated tables with a canonical double-entry credit ledger covering 18 event types, capturing in real time with idempotent reprocessing. Every liability report now draws from one reconcilable source, the finance discrepancy complaints stopped, and outstanding credit is aged into time buckets and categorised by source so it can inform cash-flow decisions rather than merely be reported. That is what balancing by construction means: the reports agree because they cannot structurally disagree.

No, you do not have to be an existing PixelForce client to use our data analytics services, and we regularly work on platforms we did not write, starting by understanding what is actually there.

No. Data services are listed as a Phase 3 add-on because that is where they most naturally arise - a platform we built goes live, it starts generating meaningful data, and the client wants to see it - but the service does not require us to have built the platform.

We regularly work on systems we did not write. Doing so starts with understanding what is actually there rather than what the documentation claims, which is the same discipline we apply to platform rescue work. Our infrastructure work has cut a client's cloud hosting costs by over 50 percent, saving more than $10,000 every month, and decreased another client's per-user cloud costs by 60 percent - both on systems inherited rather than originally built by us.

Where a platform genuinely cannot support reliable reporting without structural change, we will say so plainly rather than building an analytics layer on a foundation that will undermine it. Declining a project, or recommending against building, is a valid outcome here and we would rather deliver it at the consultation than three months in.

Choose a data analytics company on whether it starts with metric definitions rather than dashboards. Ask how an active user, a churned subscription or a completed order will be defined, who signs those definitions off, and where they are written down.

Then test four things. Ask whether the transformation logic will be version-controlled, tested and reviewable, or buried in a spreadsheet or inside a BI tool nobody can audit. Ask whether the company is resold into a particular BI vendor, because a licensing arrangement is not a recommendation. Ask what happens when two reports disagree, and listen for an architectural answer rather than a promise to check the query. Ask who owns the warehouse, the pipelines and the dashboards afterwards, and whether your data ever leaves your own cloud account.

A good answer will also tell you when you do not need a warehouse at all. If every reporting question can be answered from one system at modest volume, querying that system directly is cheaper and easier to maintain, and a provider willing to say so is telling you something a rate card never will.