Data Management


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 provides 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.  

If every organisation is sitting on a landmine of data, why are only a few actually growing and achieving their organisational goals? That comes to transforming data into useful information. Data by itself is meaningless – think numbers and records with no context.  Its immeasurable value stems from the insights that can be uncovered from it and this can only be achieved with effective data management.  


Data Management Explained

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.  

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.  

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 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. 

Types of Data Warehouses

There are three main types of data warehouses, which are: 

Enterprise Data Warehouse 

Enterprise Data Warehouses are central databases that gathers data from various sources and makes them readily available for analysis and usage across the organisation in a consolidated manner. It works by unifying data across various business functions and classifying the data according to specific subjects before giving access to the respective divisions – marketing, finance, HR etc. Data from all time periods – current and historical – can all be housed here, providing employees with a one stop shop for data analysis and business intelligence.  Antares helped deliver a seamless Enterprise Data Warehouse for GWA Group that helped them improve data accuracy and data processing.  

Operational Data Store 

Operational Data Stores (ODS) are used as an intermediary in synchronising data from unrelated and disparate systems for operational reporting. It acts as a source of data for Enterprise Data Warehouses by focusing on the operational needs of a business process and assists with decision making at the tactical and strategic levels. It is a volatile database that frequently updates and overrides existing data without keeping records of historical changes. As opposed to traditional data warehouses, an ODS captures data in real time and only performs simple queries, meaning the data is always current, up to date and lesser in volume.  

Data Mart 

Data marts are a subset of data warehouses that is oriented towards specific organisational functions, departments or subject areas. They make it easier and quicker for particular divisions to access key information without having to navigate the entirety of the data warehouse for the right data. As the information housed within individual data marts pertains to a singular department, this isolates each divisions’ use, access and manipulation of each other’s data.  

Key Components of a Data Warehouse

Data warehouses perform a variety of functions from storing data to maintaining it to ensuring it’s optimised for analysis. These are the five main components of a typical data warehouse:  

Database 

The database is the core component of a data warehouse that stores all the data from various sources by making it usable for analysis and reporting purposes.  

Extraction, transformation and loading tools (ETL) 

ETL are a three in one tool that assists with pulling and extracting data from various sources, transforming it into a suitable format and loading it onto the target database.  

Metadata 

Metadata is simply data that describes data. It governs the data warehouse architecture by providing structure in building, maintaining, handling and utilising the data warehouse.  There are two types of metadata – technical and business. Technical metadata refers to the warehouse information that can be used by data warehouse designers and administrators when executing warehouse development and management duties. Business metadata consists of information that provides users an easily comprehensible view of the information stored within the data warehouse.  

Access tools 

As end users are typically unable to interact with databases directly, access tools can help them make sense of the data and use it to fulfil their business needs. The four main tools include query and reporting tools, application development tools, data mining tools, and OLAP tools. Query and reporting tools assist users with producing reports for analysis such as spreadsheets and visualisations. Application development tools can integrate with common OLAP tools and other database systems to create custom reports for specific interpretation use. Data mining tools enable users to identify meaningful relationships, trends and patterns between datasets by sifting through large volumes of data using statistical modelling methods. OLAP tools allows analysis of data from multiple viewpoints by organising the data in a multidimension model.   

Data marts 

Data marts are designed to serve a particular function of a business and provides an access level to move the data to users. It is often used as a partition of data and is created for a specific set of users for easier and faster access.  

Choosing Where to Store Your Data Warehouse

With data growing at an astonishing rate, data warehouses have grown to become a critical necessity to the modern organisation. However, should your data warehouse be stored on-premise, in the cloud or on a combination of both to best serve your needs? The answer isn’t a simple this or that. Instead, you should be considering multiple aspects of your organisation such as your data security, data volume, support required, control and scalability. Each comes with its own benefits and drawbacks, so be sure to weigh them against your organisation’s requirements before deciding. 

On-premise 

An on-premise data warehouse, as the name suggests, involves data being collected, stored and managed on-site at your organisation. Whilst this gives your organisation complete control and visibility into your data, it also requires a pretty hefty upfront front. You’ll be responsible for purchasing all of the hardware that is required as well as having to put together and train a team of staff to manage the servers. On-premise data warehouses are a more expensive option in managing your organisational data but you may choose to use this option if you want greater compliance and security. Scaling up your on-premise data warehouse is also much more difficult and time-consuming as it consists of purchasing additional hardware and installing them.  

Whilst the cloud does offer tight data security, many organisations find it safer to store sensitive information on site to reduce chances of data leakage. Data governance and regulatory compliance are also easier to obtain with on-site data warehouses as you have complete transparency in where your data is located and making sure it adheres to company policies. On-premise data warehouses usually offer faster access to key information than cloud-based ones as they aren’t susceptible to network latency and potential wait times for server responses.     

Cloud 

Cloud-based data warehouses are often seen as a more attractive option for storing and collecting data due to their cost effectiveness and flexibility. With cloud-based data warehouses, there are minimal upfront and long-term costs as the model offers an on-demand pricing where you only pay for what you use. This gives your organisation on demand scalability and flexibility with the advantage of accessing a data warehouse solution at a low entry cost. You can scale your data up or down instantly with minimal hassle and associated costs. Aside from the cost benefits, the cloud also offers integration with other cloud services that can process multiple forms of data including semi-structured and unstructured data that on-premise warehouses are traditionally unable to. If your organisation has offices in different geographies, cloud data warehouses can also simultaneously process and serve data streams entering from disparate locations.  

However, cloud data warehouses also come with their own set of drawbacks. As the cloud is operated by a third-party service, there are increasing concerns in regard to sensitive business information being exposed to risks with data being stored remotely and managed by an external party. The repercussions can be severe if the cloud’s security is breached by cyber attacks and the sensitive information to be stolen. As such, it is recommended that organisations operating with individuals’ confidential information such as banks and the government to keep their data stored on premise rather than on the cloud.  

Hybrid cloud data warehouse architecture  

By using a mix of the two forms of data warehouses, you’ll be enjoying the best of both worlds. By choosing to store both sensitive and frequently accessed information on premise, you’ll be able to ensure complete control over the data’s security and increase the speed of access. For organisations looking to work in an agile and fast paced manner, a hybrid data warehouse architecture meets the needs of scalability, flexibility and cost effectiveness that comes with unpredictable demands. You’ll be able to access data from both data warehouses and enable applications from both sources to be integrated for accelerated business analytics.  

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.   

What is Data Governance?

Data governance is a core component of good data management and assists with defining the policies involved in maintaining and protecting data. It is critical in ensuring that the organisational data is compliant with regulatory procedures and doesn’t get misused. With data leakages becoming a common occurrence in the current climate – think the Facebook/Cambridge Analytica incident – governance is an increasingly important priority in establishing security and privacy for valuable data. Organisations now have to go beyond just managing their data effectively but must also govern the access of users to certain data.  

A data governance framework is the blueprint to constructing standards of data and is an integral part of overall data management strategy. It encompasses the policies, processes, technology and people needed to protect and make appropriate use of data assets. By working hand in hand with data management, organisations will be able to establish clear lines of accountability and technical boundaries to support transparency and integrity. 

Ensuring your data is compliant with standards and organisational policies is a crucial step in your data journey and Antares’ data strategy services can help you build trust in your data by maximising its value. Contact us on +61 2 8275 8811 to find out how we can create an integrated data strategy that drives operational efficiency and decision making.  

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.  

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