Businesses generate more data than ever, but having data and being able to use it effectively are two very different things.
Information may be spread across ERP systems, finance applications, CRM platforms, operational databases, spreadsheets and cloud applications. Different departments may calculate the same metrics differently, while reporting teams spend hours manually combining information.
This is where a data consultant can help.
A data consultant works with an organisation to understand its business requirements, assess its existing data environment and design solutions that make data more reliable, accessible and useful.
This can include data strategy, integration, data warehousing, cloud platforms, data modelling, analytics and Power BI.
Importantly, good data consulting should start with the business problem, not the technology.
What Does a Data Consultant Actually Do?
The exact role varies depending on the organisation, but most data consulting engagements involve several key areas.
Understanding the Business Problem
Before selecting technology, a data consultant needs to understand what the organisation is trying to achieve.
Typical problems might include:
- Reporting takes too long
- Different reports show different numbers
- Teams rely heavily on spreadsheets
- Data is spread across multiple systems
- Power BI has become difficult to manage
- Management cannot access information quickly enough
- Existing data platforms are no longer scalable
These are business problems first and technology problems second.
Understanding the desired outcome helps determine what actually needs to be built.
Assessing the Existing Data Environment
Most organisations already have significant amounts of data.
It might exist across ERP and finance systems, CRM platforms, databases, SharePoint, Excel, APIs and existing reporting solutions.
A data consultant assesses where important information resides, how systems relate to each other and how data currently moves through the organisation.
This can identify duplicated processes, manual work, data quality problems and areas where the existing architecture has become unnecessarily complex.
Designing the Right Data Architecture
Once the requirements and existing environment are understood, an appropriate architecture can be designed.
For a smaller organisation, this may be relatively straightforward. Larger organisations may need a central data warehouse or lakehouse integrating information from many operational systems.
A typical architecture might look like:
Source Systems → Data Integration → Data Warehouse or Lakehouse → Semantic Models → Power BI and Analytics
The technology used should depend on the requirements.
Microsoft Fabric may be appropriate for organisations heavily invested in Microsoft and Power BI. Others may already use Azure, Snowflake, Databricks or another platform.
The objective isn’t to introduce technology for the sake of it. It is to create a data architecture that is reliable, maintainable and appropriate for the organisation.
Bringing Data Together
One of the most common challenges organisations face is that important information exists across multiple systems.
For example, customer information may sit in a CRM, transactions in an ERP system, operational information in another application and budgets in spreadsheets.
Individually, each system provides only part of the picture.
A data consultant can design automated processes to bring this information together, apply business rules and make it available through a central data platform.
This is where technologies such as data warehouses and lakehouses become valuable.
Instead of every report independently connecting to operational systems and reproducing business logic, the organisation can establish a reliable central source of information that supports multiple analytics requirements.
Why Data Modelling Matters
Simply moving data into a cloud platform does not solve an organisation’s data problems.
The data also needs to be structured correctly.
A well-designed data model establishes consistent relationships and definitions across areas such as customers, products, equipment, projects, finance and operations.
Instead of different reports calculating revenue, utilisation or profitability independently, those calculations can be based on shared business logic.
Good data modelling can improve:
- Reporting consistency
- Power BI performance
- Data quality
- Reusability
- Maintainability
- Confidence in reporting
This underlying architecture is often just as important as the dashboard users eventually see.
Designing the Right Data Architecture
Once the requirements and existing environment are understood, an appropriate architecture can be designed.
For a smaller organisation, this may be relatively straightforward. Larger organisations may need a central data warehouse or lakehouse integrating information from many operational systems.
A typical architecture might look like:
Source Systems → Data Integration → Data Warehouse or Lakehouse → Semantic Models → Power BI and Analytics
The technology used should depend on the requirements.
Microsoft Fabric may be appropriate for organisations heavily invested in Microsoft and Power BI. Others may already use Azure, Snowflake, Databricks or another platform.
The objective isn’t to introduce technology for the sake of it. It is to create a data architecture that is reliable, maintainable and appropriate for the organisation.
Bringing Data Together
One of the most common challenges organisations face is that important information exists across multiple systems.
For example, customer information may sit in a CRM, transactions in an ERP system, operational information in another application and budgets in spreadsheets.
Individually, each system provides only part of the picture.
A data consultant can design automated processes to bring this information together, apply business rules and make it available through a central data platform.
This is where technologies such as data warehouses and lakehouses become valuable.
Instead of every report independently connecting to operational systems and reproducing business logic, the organisation can establish a reliable central source of information that supports multiple analytics requirements.
Why Data Modelling Matters
Simply moving data into a cloud platform does not solve an organisation’s data problems.
The data also needs to be structured correctly.
A well-designed data model establishes consistent relationships and definitions across areas such as customers, products, equipment, projects, finance and operations.
Instead of different reports calculating revenue, utilisation or profitability independently, those calculations can be based on shared business logic.
Good data modelling can improve:
- Reporting consistency
- Power BI performance
- Data quality
- Reusability
- Maintainability
- Confidence in reporting
This underlying architecture is often just as important as the dashboard users eventually see.
Data Consulting and Power BI
For many organisations, Power BI is where users interact with their data.
But Power BI is only one part of the overall solution.
A simple reporting environment might look like:
Business Systems → Power BI Reports
As reporting requirements grow, a more scalable architecture may become:
Business Systems → Central Data Platform → Shared Semantic Models → Power BI Reports
This allows transformation and business logic to be managed centrally rather than recreated across multiple reports.
A data consultant therefore looks beyond the dashboard and considers the entire journey from source system to business decision.
When Does a Business Need a Data Consultant?
Not every organisation needs external data consulting.
External expertise becomes particularly useful when:
- Multiple business systems need to be integrated
- Reporting involves significant manual work
- Different departments report inconsistent numbers
- A data warehouse or lakehouse is required
- Power BI has grown without a clear architecture
- A new data platform is being considered
- Existing architecture needs modernising
- Internal teams need specialist technical expertise
- Data quality is affecting reporting and decision-making
Consultants can also provide specialist capability for a particular project without requiring an organisation to permanently expand its internal team.
Do You Need Microsoft Fabric, Azure, Snowflake or Databricks?
There is no single platform that is automatically right for every organisation.
Microsoft Fabric can be an attractive option for organisations already using Microsoft technologies and Power BI. Azure provides a broad range of cloud data services, while Snowflake and Databricks are also widely used for modern data platforms.
But the technology decision should come after the requirements are understood.
Sometimes the right solution is a new cloud data platform.
Sometimes it is improving what the organisation already has.
A good data consultant should be capable of recognising both situations.
What Does a Data Consulting Engagement Look Like?
A typical engagement begins with discovery.
The consultant works with business and technical stakeholders to understand the current environment, problems and desired outcomes.
From there, an architecture and implementation plan can be developed.
The implementation itself might involve:
- Data integration and pipelines
- Data warehouses or lakehouses
- Business rules and transformations
- Data modelling
- Power BI development
- Security and governance
- Deployment and monitoring
The solution should then be tested with business users before being deployed into production.
Importantly, organisations do not necessarily need to transform everything at once.
A focused first phase can establish the architecture while solving one high-value business problem. Additional systems and reporting requirements can then be introduced progressively.
Start With the Business Outcome
One of the most important principles in data consulting is to avoid starting with a technology.
Instead of:
We need Microsoft Fabric.
A better starting point might be:
We need reliable daily reporting across finance and operations without manually combining spreadsheets.
The technology can then be selected based on that requirement.
Likewise, organisations should avoid trying to move every system and report into a new platform immediately.
Starting with a defined business outcome can reduce risk, deliver value earlier and establish a foundation that can grow over time.
How AGER BI Approaches Data Consulting
AGER BI provides data consulting, data engineering, data warehousing, Microsoft Fabric, Azure and Power BI services to organisations across Australia.
Our approach starts by understanding the business problem and existing data environment before recommending the technology.
Depending on the requirement, an engagement may include:
- Discovery and requirements
- Data strategy and architecture
- Data integration
- Data warehouse and lakehouse development
- Microsoft Fabric and Azure
- Data modelling
- Power BI
- Performance optimisation
- Governance
- Documentation and knowledge transfer
AGER BI is a founder-led consultancy, providing clients with direct access to senior technical expertise throughout the engagement.
We also work alongside existing internal IT and data teams where appropriate, helping organisations build capability rather than creating unnecessary dependency.
Looking for a Data Consultant?
If your organisation is spending too much time manually preparing reports, struggling to combine information from different systems or considering a new data platform, the first step does not need to be a major transformation programme.
A focused assessment can identify the underlying problem, determine the appropriate architecture and establish a practical first phase.
The right solution might involve improving an existing Power BI environment, creating a data warehouse, implementing Microsoft Fabric or simply developing a clearer approach to your organisation’s data.
Contact AGER BI to discuss your data requirements.
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