Data and AI

Data analytics & insights

Reporting, dashboards and analysis built on data from your CRM, ERP and operational systems, with definitions everyone agrees on and numbers that reconcile.

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Overview

What analytics and insights work covers, and where Great Minds fits

Analytics turns the data your systems already collect into answers: how sales are tracking against target, which customers are at risk of leaving, where service levels are slipping, what margin each product actually makes. The visible part is dashboards and reports. The part that decides whether people trust them is underneath: a data model, agreed metric definitions and reliable feeds from the systems where the data starts.

Great Minds is a Sydney-based systems integrator, so our analytics work starts at the source systems: Salesforce, Microsoft Dynamics 365, Atlassian, commerce and marketing platforms, and your ERP. We define the metrics with the people who use them, build the data pipelines and models, and deliver reporting in the business intelligence (BI) tools you already run, such as Power BI, Tableau or Looker Studio, or in the reporting built into the platforms themselves.

Typical starting points are a monthly report assembled by hand from several exports, two dashboards showing different numbers for the same thing, a new CRM or ERP whose reporting nobody uses, or leaders asking for forecasts the current data can't support.

Hands reviewing printed financial charts

Capabilities

What an analytics engagement covers

We start from the decisions people need to make and work back to the data, so every report has an owner and a reason to exist. These are the four pieces of work most engagements include.

01Modern data platformsA data warehouse or lakehouse fed by your source systems, with clean data models, documented definitions and access controls, so every report reads from the same governed data.

02Business intelligence & dashboardsDashboards designed around the questions leaders and teams ask, showing what's happening, why, and what needs attention, with each metric traced back to its source system.

03Advanced analyticsForecasting, segmentation, cohort analysis and predictive models where the data supports them, validated against history before anyone relies on the results.

04Self-service reportingCertified datasets, templates and training so teams can answer their own questions without building conflicting versions of the same metric.

How we deliver

An analytics engagement, from questions to adoption

01Start with the decisions

Interviews with the people who use the reports: which decisions they make, how often, and what they look at today. We list the metrics behind those decisions and write a definition for each, including where its data comes from and who owns it.

02Trace and fix the data

Each metric is traced back to its source systems. Where data is missing, duplicated or defined differently across systems, we fix it at the source or in the data model, rather than hiding the gap in a dashboard calculation.

03Build the model and reports

Pipelines, the data model and the first dashboards, built in short iterations and reviewed with the people who'll use them. AI agents do much of the repetitive profiling, mapping and test-data work; our consultants own the design and sign off every change.

04Embed and hand over

Training by role, certified datasets for self-service, documentation of every metric, and a review a few weeks after launch to see which reports are used and which aren't. Unused reports are retired, not maintained.

Australian considerations

What Australian organisations need to check

Analytics often reuses personal information collected for another purpose, so check it against APP 6: using customer data to analyse behaviour is usually within what people would reasonably expect, but some combinations of data aren't. Where analysis doesn't need to identify people, de-identify it, and manage the risk of re-identification when datasets are combined. Dashboards that show individual customers or staff need the same access controls as the source systems, in line with APP 11.

If a predictive model's output is used to make, or substantially help make, decisions that significantly affect people, such as credit, eligibility or prioritising service, the Privacy Act's automated decision rules apply from 10 December 2026, and your privacy policy must disclose it. Our guide to automated decisions and the Privacy Act explains the test.

APRA-regulated entities should design reporting with data risk in mind. APRA's prudential practice guide CPG 235 sets out its expectations for managing data risk, including data quality, and reports used for risk and regulatory purposes need traceable lineage back to source.

FAQ

Data analytics questions

Which BI tool should we use?

Usually the one that fits the platforms and skills you already have. Organisations on Microsoft 365 often choose Power BI; Salesforce-heavy organisations may use Tableau or Salesforce's own analytics; Google Workspace users often start with Looker Studio. The data model and definitions matter far more than the tool.

Why do our dashboards show different numbers?

Usually because the same metric is defined differently, filtered differently or drawn from different copies of the data. Agreeing one definition per metric, with an owner, and building reports from one governed dataset fixes most of it.

Do we need a data warehouse?

Not always. If your reporting comes mainly from one platform, its built-in reporting may be enough. A warehouse makes sense when reports combine several systems, need history the source systems don't keep, or must perform at volume.

Can we use AI to analyse our data?

Yes, increasingly. AI assistants in BI tools can answer questions in plain language and summarise trends, but they give reliable answers only when the data model and definitions underneath are sound. We set those up first, then switch on AI features with the same access controls as the reports.

How long does it take to get useful dashboards?

A first set of dashboards for one team, built on one or two source systems, is a much smaller piece of work than an enterprise reporting platform. We scope the first release around a handful of decisions so people see value early, then extend it.

How is analytics different from data strategy?

A data strategy decides which data matters, who owns it and how it's governed. Analytics builds the reports and models that use it. Many engagements do a short piece of strategy work first, then deliver analytics on top.

People

Analysts, data engineers and BI developers

Our analytics consultants work with business owners to define metrics, then build the pipelines, models and dashboards behind them. They stay accountable for numbers that reconcile, with AI agents doing repetitive profiling and mapping work under their review.

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Case studies

Reporting and analytics in practice

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For a Hong Kong law firm, we built custom Salesforce dashboards and reports on caseloads, billable hours and client satisfaction. For Montgomery Investment Management, campaign performance was optimised through A/B testing and insights surfaced in Salesforce dashboards.