Data Management Services
Modern organisations generate more data than ever, from CRM records and social media interactions to IoT sensors and sales systems. But without proper management, this information often becomes fragmented, duplicated or lost.
Antares helps businesses bring order, structure, and trust to their data so they can make confident, insight-driven decisions.
The Challenge: Disconnected, Unreliable Data
Many organisations struggle with:
- Fragmented data scattered across systems, spreadsheets, and apps
- Poor data quality — duplicates, missing values, and outdated records
- Data silos between departments, blocking collaboration
- Inconsistent formats and governance, leading to errors in reporting
- Inefficient manual processes that slow productivity
- Limited insight from unstructured or incomplete data
These challenges make it difficult to make confident decisions, comply with regulations, and gain a true view of business performance.
The impact
Without Structure, Data Loses its Value
Productivity Drops
As teams waste time searching for correct information
Decision Making Suffers
Due to inaccurate or inconsistent data
Costs rise
From duplicated storage and unnecessay rework
Business risk increases
Especially in regulated industries
Opportunities are missed
Because insights remain hidden
Business Benefits of Data Governance
Imagine that whilst entering data into your systems, there was a small error which snowballs to become a disaster. Now, with data governance, that scenario likely won’t happen. You can be reassured that the data you’re using for critical decision making is reliable and has been quality controlled. With a data framework in place, you’ll be reaping plenty of benefits such as:
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Regulatory Compliance
As many organisations operate according to some form of industry regulation, data governance makes it easy to adhere to those policies and avoid potential penalties – win-win.
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Improved Efficiency
Various problems arise from poor quality data and can cost your organisation a hefty sum each year. Correcting inaccurate figures and having to manually cleanse data are all time and resource intensive tasks that can be resolved with proper data governance frameworks. Free up your employees’ time and organisational resources with the elimination of unstructured data processes.
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Data Quality
As the old saying goes – quality over quantity. Data governance gives structure to the processes and individual roles involved to maintain the integrity of data. You’ll have a clear view of the root cause of data issues and readily fix them before they become more serious.
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Security
A strong data governance framework provides levels of security to a data asset’s lifecycle which can minimise data associated risks such as theft, loss and misuse. By restricting who can access what, it helps preserve the integrity and usability of data to safeguard it against human error and unintentional leakage
The Antares Data Management Approach
We can take the guesswork out of making decisions and help you leverage the power of data to derive key insights. Our approach is simple but viable and ensures you maximise your data assets and underlying infrastructure.
Understanding your business needs
We begin with discovery workshops to understand your goals, current challenges, and data environment – ensuring our solution aligns with your strategic outcomes.
Assess & Map Your Data Landscape
We examine where your data lives, how it is formatted, who uses it and where quality issues and silos exist
Design Modern Data Architecture
We create a scalable design for: Data warehouses, Datra lakes, Data integration piplelines and security and governannce models
Implement Data Integration & Warehousing
Using the Microsoft Azure ecosystem: Azure Synapse Analytics, Azure Data Factory (ETL), Azure Databricks, Azure Blob Storage, Azure Analsysis Services and PowerBI
Master Data Management (MDM)
With solutions like CluedIn, we unify and cleanse critical business data (customers, products, employees, suppliers) to ensure accuracy across every system.
Governance, Security and User Enablement
We first establish role-based access, data standards and quality controls – then train your teams to confidently use the new platform.
What is the Difference between a Data Warehouse and Database?
A data warehouse as the name suggests houses a variety of data from multiple sources within the organisation to assist with decision making and may include a number of databases. Databases on the other hand, are an organised collection of information that is stored and managed so that it can be easily accessible when needed. The key differences between them are:
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Use case
Databases are used to manage the collation of small, atomic data in everyday operations of a business such as a customer purchasing a new chair or entering in an employee’s annual leave. The information stored in data warehouses typically span from historical to current data, making it suited for higher level analysis and insights for broader questions – e.g. conducting customer research through analysing large volumes of historical data
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Purpose
The purpose of database systems is to store and organise structured data for easy retrieval when needed. They are designed to process transactional data that are used in the day to day operations of an organisation. Whilst data warehouses also store and organise data, their main purpose is to provide a central repository for users to access the data and perform necessary analysis and reporting.
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Processing method
Database systems use Online Transactional Processing (OLTP) to process data and perform queries. As the information stored within database systems are focused on daily transactions, this processing system enables users to perform queries quickly and receive accurate information in an instant. Online Analytical Processing (OLAP) is the processing system that is used by data warehouses as they enable data analysis and reporting to be performed. It is a core component in allowing multidimensional analysis to be performed on large volumes of data at a rapid rate. OLAP can drill down into more detailed data providing a holistic view that can assist with spotting trends and identifying areas of improvement.
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Data structure
Databases use a normalised data structure to process data whereby your information is organised into columns and tables for optimal data integrity. This process removes any redundant data and ensures consistency across all of your data. However, as the process involves multiple tables, normalised databases can cause queries to be slowed down, especially for complex ones. On the other hand, data warehouses utilise a denormalised data structure which improves the performance and speed at which queries are executed. This is achieved by joining multiple tables together into one to reduce the complexity of the query. However, a small drawback from this data structure is the resulting redundant data.
Data Management and Your Organisation
There’s no doubt your organisation, whatever size or industry, can benefit from data management. While many businesses are still grappling the importance of data in achieving their organisational goals, it’s important that you unlock the full potential of your data to gain an edge in the current competitive landscape.
Antares can help your organisation better manage your data to uncover hidden insights and drive business value. We have years of expertise helping organisations from various industries in leveraging the powers of their data. By working closely with you and understanding your organisation’s needs. We can help you in designing a data warehouse that is suited to your data requirements and optimised for your users. Your volume of data is ever-growing and we want to help you scale and remain competitive with data driven decisions. Contact us at +61 2 8275 8811 for a consultation or more information.