Data Management Services

Data’s value has always been immeasurable to an organisation’s success and with the rapid changes in the current landscape, it is proving itself to be the key for businesses in sustaining a competitive edge. Whilst many organisations understand its potential, few fully utilise it to its full capabilities and even fewer have the structure to properly manage it.  

Data management services provide a framework in managing and regulating the data that is constantly being generated by your organisation. Every day, your organisation is creating huge volumes of data from various systems and processes such as social media posts, sales leads, delivery receipts and even from industrial equipment. Most of the data created is unstructured and if not stored correctly can lead to duplicates, mistakes and missing information. All of which are hindrances to driving business value.  

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Data management has progressively grown in importance over the years as businesses realise the value it brings to their operations, decision making and overall revenue. Data that is improperly formatted and located all add to the burden of inefficiency, wastage of time and data silos which can produce an array of problems. Various disciplines make up the process of data management and work together to provide a consistent framework around how raw data is processed, stored, maintained and retrieved for streamlined usage.

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Why do Businesses need Data Management?

Aside from giving structure to how data is collected, stored and used, data management also provides an organisation and its people the ability to resolve common internal pain points. Here are a few benefits of data management:

Improved Productivity

With data all in one place and in an organised manner, your employees will save valuable time in searching and understanding the right information.

Better Data Quality

Improper data management can result in compromised data quality that can affect overall usability. Data management reduces the risk of data loss occurring from data silos, inconsistent data sets, duplicate data and outdated data.

Mode of navigation

Organisations and consumers are all generating large volumes of data everyday. Data management processes provide a means to navigating the growing amounts of data by organising and storing it consistently.

Increased cost of efficiency

Data that is sitting in multiple locations contribute to both a waste of storage and unnecessary costs. When data is managed with structure, employees can find all their required information in a central location and won’t have to worry about conducting analysis using inconsistent data repeatedly.

Smarter decision making

Properly managed data can empower staff and managers to access the most accurate, recent and right information for use. Organisations can achieve better decision-making using data driven insights to support their business processes, opportunities and strategies.

Greater agility

Data management can increase the rate that decisions and key insights are made. Organisations which are able to make fast choices can react effectively to changes in the market and competitors.

Data management involves the entire lifecycle of a data item from its creation to deletion and ensures that throughout its life it undergoes a comprehensible collection of processes to maintain its integrity. Data that isn’t treated up to standard can become corrupt and unusable which can negatively affect overall business value. As data management is a diverse practice, it involves multiple fields such as data governance, data architecture, data warehousing and data security management within it. Data warehouses are commonly implemented as part of the data management process as they provide a singular platform for the storage and preparation of data for business analysis.

Master Data Management

Master Data Management, or MDM, is a powerful strategy and technology that ensures data consistency and quality across your organisation’s systems and departments. Our innovative approach, exemplified by CluedIn, automates data cleaning, integration, and preparation, enabling you to harness the transformative power of your data sooner. With MDM, your organisation can fast-track the data ingestion journey, leading to improved decision-making, increased efficiency, and regulatory compliance in complex data environments.

Discover how MDM can take your organisation to new heights.

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: 

  • 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

  • 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.

  • 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.

  • 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.

Components of Microsoft Azure Synapse Analytics

Azure Synapse Analytics is the scalable, cloud-based enterprise warehousing solution offered by Microsoft that uses the power of MPP to streamline the entire data journey from ingestion to transformation to preparation. Its integration with Power BI and Azure Machine Learning gives organisations the elasticity to process large volumes of data on demand to gain valuable insights.  

Azure Data Factory is a cloud based ETL and data integration platform that allows you to workflows that facilitate the movement of data between data stores.  By integrating the data residing both on premise and in Microsoft SQL Server, users are provided with a singular point of view into their ETL pipelines. 

Azure Blob Storage is a scalable storage service used to cache Binary Large Objects (BLOBS) allowing you to access unstructured data when needed for analytics. This low-cost storage option is perfect for media types such as texts, videos, images, and logs.  

Azure Databricks is an Apache Spark based data analytics platform optimised for Microsoft Azure that enables users to collaborate on tasks in a shared workspace.  

Azure Analysis Services is a platform as a service offering from Microsoft that includes a range of managed services that can enable organisations to consume and process data using a wide variety of technologies. Users can create tabular models and conduct data analysis using intuitive tools such as Power BI, Excel, and Tableau.   

Power BI is a business analytics tool that allows users to create dynamic visualisations and beautiful reports that can be accessed from any device at any time. It pulls data from multiple sources and transforms them into actionable insights that can be viewed as easily comprehensible graphs.   

As a Microsoft Solution Partner in Data Analytics, we have the experience to help your organisation unlock the true value of data. We can create flexible architectures such as modern data warehouses in Azure that can seamlessly ingest, store, process, and analyse data in various formats to deliver actionable insights. Contact Antares to discover how you can maximise your data assets and streamline your journey from ingestion to consumption.  

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

This is the discovery stage where we make sure to deep dive into your unique requirements by identifying your business goals and articulating the current data constraints.

Examining available resources

We assess your organisation’s current data resources including its format, location, and where it is being generated from.

Mapping out current and future state infrastruture

By analysing your current data environment, we can identify gaps and bottlenecks which can be improved for increased operational efficiency. Once the as-is architecture has been recognised, our team defines the data infrastructure that would resolve the challenges and requirements that were identified. We will model the warehouse design so that the data sources, connections, and views are clearly illustrated. Each modern data warehouse design is unique. This means that no two Azure modern data warehouse architectures are the exact same.

User adoption and data governance

Having your modern data warehouse set up is only the first step of your data journey. If your employees opt to not adopt the new technologies, processes, and standards that have been implemented, then you will not see a considerable increase in your data quality. We provide education and training to staff so that they understand how to use data processes and realise the role they play in ensuring data is held to a consistent and accurate standard.

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:

  • 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.

  • 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.

  • 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.

  • 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

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.  

Frequently asked questions

  • Data management is a process that involves acquiring, storing, managing, protecting and processing data to optimise its use and accessibility for users. It keeps data organised so that an organisation and its people can use it in a reliable and efficient manner. Data management provides a framework for data analytics by reducing ambiguity and providing structure to how data is managed. Without data management, organisations wouldn’t be able to fully unlock its true worth and uncover hidden insights to drive business value.

  • Simply put, data management is important because data is an important corporate asset. Data is very valuable and when used to its full potential can create endless opportunities and growth for organisations. Improper data management can lead to a myriad of data problems such as issues with quality, data silos and missing data which can compromise the capabilities of running data analytics and business insights. Data is the multi-dimensional foundation that assists with decision making, increasing overall revenues and profits, improving organisational processes and optimising efficiency. Inaccurate insights can subsequently affect the business decisions drawn based upon them, leading to a snowball effect of faultiness. The growing volumes and types of data means that without proper data management in place, it will become increasingly difficult to navigate the data environment and obtain a competitive edge in the current landscape. Data management resolves these data issues by providing structure to how data is managed and stored by providing an organised repository for access.

  • Data warehousing is the process of collating and managing data from various streams into a central repository for analysis and business insights. Data mining involves discovering patterns and trends in large data sets to uncover relationships, create visualisations and gain critical insights. Simply put, data warehouses are the heavy machinery that facilitates data mining to happen by providing an organisation with a pooled collection of reliable data to be used for business analysis and identifying hidden patterns. The extracted insights can be used across all the organisation’s functions such as marketing, sales and long-term strategies.

  • Data has now become a critical driver in business success with its reliability, validity, and accuracy being key elements. Here are some best practices for data management to implement:
    1. Standardise your file names to easily search and discover documents when needed
    2. Ensure your metadata is details you can quickly see information on the data making it easier to find what you need
    3. Reduce duplicate data to lessen the chance of inaccurate analysis and business decisions
    4. Use a quality data management software that integrates with your data flow to improve performance and provide clear visibility
    5. Maintain frequent monitoring of your data’s health and quality to reduce the chance of data loss

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