Table of Contents

What is data management?

Data management can be simply defined as the administrative process of acquiring, storing, validating, organising, protecting, and maintaining the data, which was created and/or collected by the organisation. Data management processes typically consist of multiple functions to ensure that the data is accurate, available, and accessible.

As more companies taking advantage of Big Data to improve their knowledge about the market, their customers, or even about themselves, they must find a way to effectively handle the ever-increasing data. This is where Data Management solutions and platforms come into play. These platforms and solutions can vastly increase the efficiency and effectiveness while reducing the time needed for data processing, validation, etc.  

Data generated and used by enterprises daily is typically categorised into three types: 

1. Transactional

This data supports the ongoing operations of the business, for example, time, place, price, discount, payment methods. Transactional data is typically stored and updated within enterprise systems to describe data and to automate key processes of sales, customer service, order management, purchasing, and many more.

2. Analytical

These are the numerical values, metrics, and measurements that provide business intelligence and support the decision-making process. Analytical data is stored in Online Analytical Processing (OLAP) repositories, such as data warehouses and data lakes.

3. Master

Master data describes the core objects in business operations, transactions, and analytics such as customers, suppliers, employees, inventories, and products. Master data is usually stored in different systems and shared by multiple users or groups across the organisation.

The enormous growth of data amidst the technological boom requires enterprises to put in 200% effort to centralise, organise, control, and make them accessible to anyone who needs data. This happens to be the ultimate goal of enterprise data management.

The importance of data management and its benefits

Data is the business' most important asset, especially in today's highly dynamic business environment. A recent study shows that around 90% of companies that lost their data centre for 10 days or more due to a catastrophic failure in the system filed for bankruptcy within a year after the incident. For smaller businesses that are unfortunate enough to have the same problem, almost half of them filed for bankruptcy in the same amount of time.

The sheer volume of enterprise data today, sadly, are still handled manually, such as through conversations, spreadsheets, and emails. However, many enterprises do not acknowledge this is a serious issue. The lack of knowledge, skills, resources, and even staffing often results in improper enterprise data management.

Improper data management can lead to a variety of issues, such as incompatible data, inconsistent data sets, lost data, and other data quality problems. These issues can significantly reduce a firm's ability to do Business Intelligence (BI) and data analytics and can also lead to inaccurate findings. All of which will hurt a business deeply.

If done properly, data management will bring to the firm countless perks and opportunities. Here are some of the many benefits that data management can bring to you and your business.

  • Increase productivity: As data is more accessible, available, and understandable, employees' productivity will increase.
  • Lower cost: As historical data is stored, sorted, and easily accessible, Data management will make sure that employees will not do the same work or conducting the same research and analysis that has been done before, which makes your firm more cost-effective.
  • Reduce data loss: With proper data management, you will have a lower risk of losing information and data. Moreover, it also ensures that vital information is backed up and can be easily retrieved when needed.
  • Lower security risk: Nobody wants to have their sensitive data leaks and falls into the wrong hands. Good data management will certainly increase the protection of your data, and thus, reduce the risk of cyber-attacks.
  • More accurate decisions and predictions: As businesses rely heavily on data to make marketing plans, trends analysis, and assessments, a strong Data Management plan and system will make sure that the data is accurate, accessible, and available, which would positively affect the decisions and predictions of the firm.
  • Increase flexibility: A well-executed data management system can greatly decrease your company's response time to changes in the business environment, thus, increase the company's flexibility and adaptability to changes.

Data management best practices

Businesses continuously collect data. They need to find a way to handle these very valuable resources to avoid the conundrum of "garbage in, garbage out". And data management is the thing that takes care of these data. Here are key components of a good data management system.

Data governance

This includes policies, guidelines, and processes created by the organisation to protect the integrity, quality, and security of the data. It also helps firms to comply with governmental regulations such as CECL and GDPR.

Data security

Data security consists of strategies and measures that a business sets up to protect its valuable data throughout the data lifecycle.

Data integration

This process includes collecting and unifying all types of data from a variety of sources and repositories for meaningful usage.

Master data management

The function helps the organisation to eliminate silos, avoid data entry errors caused by manual inputs as well as making sure of the validity and timeliness of data.

Metadata management

Metadata is the "data about other data"; it gives contexts to the data's contents. Metadata management can help ensure the creation, storage, integration, and control of these metadata are according to the predetermined framework across the organisation.

Data quality management

This component is quite straight-forward, it makes sure that the data acquired by the business is of the best quality possible.

Simplify data management process with data lakes

A data lake is the concept centres on landing all analysable data sets of any kind in raw or only lightly processed form into the easily expandable scale-out infrastructure to ensure that the fidelity of the data is preserved.

Before data lakes, data warehouses were viewed as a revolution to enterprise data management. However, to make it to the warehouse, all data must be processed – a procedure that is not only time-consuming but also laborious and challenging.

Data lakes are created to store historical and micro-transactional data – what in the past was not sustainable in data warehouses due to volumes, complexity, storage costs, latency, or granularity requirements. This level of detail in data offers rich insights, but deducting meaning from it is prone to error and misinterpretation

According to an Aberdeen survey, organisations that implemented data lakes outperform their peers by 9% in revenue growth by identifying and acting upon new growth opportunities faster using new data sources and analytics.

The ability to garner practically all data provides endless opportunities for businesses. Data lakes have many uses and play a key role in providing solutions to many different business problems. With the right business intelligence tools, businesses can conduct experimental analysis before its value or purpose is defined and moved to a data warehouse.

Data management tools

As the need for a good data management system and platform soars around the globe, there are a large number of offers from various enterprises to help with the situation. These platforms and systems are built differently and offer different key functions, it is wise to consider your company's workflow, framework, and needs to find the one that fits your firm most.

Your data issues can be elevated with the right data management solution, and dataBelt could be the solution that you have been looking for.

DataBelt is an all-in-one solution leveraging modern technologies, namely machine learning and robotic processing, to support companies to overcome challenges associated with data governance.

Its open API architecture and data crawler support the indexing, classifying and storing both structured and unstructured data in a secure data lake. You can always rest assured your valuable assets are well protected, organised, maintained, and readily available upon request.

DataBelt enables you to understand every piece of data you currently possess, including but not limited to documents, images, videos, and even sound files. What's more, the solution also helps you to grasp the relationship between each data, the jurisdiction it belongs to, what can it be used for, its level of privacy/ sensitivity and many more.

On top of that, dataBelt is deployable both on-premise and in the cloud.

Here a few additional noteworthy Data Management systems and platforms:

Oracle Data management suite

  • Can deliver authoritative, consolidated, and consistent master data that organisations can use for analytics and operations.
  • Provide real-time analysis
  • Enhance data governance, quality, validation, access controls
  • Enable compliance with organisational and standard policies
  • Support large databases, multiple programming languages, data replications, and migrations,
  • Support cross-functional collaboration and repeatable business processes

IBM Infosphere Master Data Management Server

  • Scalable on-premise and cloud ETL platform
  • Data identification, cleansing, automation, governance, reporting, monitoring, analytics, and quality control
  • Transform data into an easy-to-understand and use format.
  • Enable near-real-time data integration across different cloud and on-premise systems and services.
  • Align data assets, business processes, and strategies to improve productivity



  • Easy data access, in-depth analysis, and reporting
  • High-level data security
  • Good reporting, dashboard features, and insights
  • Easy to integrate with other tools to provide actionable insights
  • Alert users when there are issues such as ETL failures, low sales, fraudulent transactions, etc.

SAP Data management

  • Use a single point to access all data across multiple platforms
  • Good metadata management tools
  • Lower cost of ownership


Dell Boomi

  • Connect all the data sources and applications across hybrid IT infrastructure
  • Synchronise data via a central data hub
  • Automate most processes while providing flexible workflows and business logic
  • Support interoperability between internal and third-party systems.
  • Enhanced data stewardship with the ability to alert IT teams when there are data entry and duplication issues

Microsoft Master Data Service

  • Data can be organised in models, updated by creating rules, and it can include access controls
  • Enables users to develop MDM solutions that are built on top of an SQL Server database technology for back-end processing
  • Provides service-oriented architecture endpoints using Windows Communication Foundation (WCF)
  • It can implement a hub architecture using MDS to create centralised and synchronised data sources to reduce data redundancies across systems

Google Cloud

  • Big data analytic platform
  • Various tools for could-based data management
  • Sever as a workflow manager to tie components together such as BigQuery for tabular data storage, Cloud BigTable for NoSQL database-style storage, Cloud Pub and Cloud Data Transfer for data intake, ML Engine for advanced analysis via machine learning and artificial intelligence, Data Studio for GUI-based analysis and dashboard construction, Cloud Datalab for code-based data science, and connections to BI tools such as Tableau, Looker and Chartio.


  • Offers an easy connection to various data sources
  • Interactive dashboards that help with data exploration
  • Provides easy access to visualisations
  • Support for data governance; secure and scalable collaboration, sharing of dashboards, data, and insights
  • Self-service analytics capability.
  • Quick and easy to deploy, scalable as needed



Data analytics - The next step of data management

Apart from manufacturing businesses, the hospitality and real estate industries can also take advantage of Data Analytics through hotel revenue management systems and OLAP technology. With this assistant, hoteliers are able to easily compare prices (to competitors), maintain the whole system, and access to smart data presentations, therefore, enhance the overall operational performance. In the meantime, Data Analytics helps to create more value and online visibility, make better decisions and encourage individual customers, which boosts the efficiency for real estate enterprises.

To Chief Finance Officers (CFOs), it is better to have full control of the financial data in corporate reports, so they would love an assessing data system that can both analyse and protect the crucial information. Many vendors have integrated the security feature into their products to keep the competitive advantages (like Infor Sunsytems Cloud). Not to stop there, even the non-financial data should not be overlooked as it has certain effects on the overall result.

Let's keep in touch


Interested in learning more?

Subscribe to TRG Blog to always keep up-to-date on the latest news, trends, and events surrounding Business Intelligence and Data Analytics.

To subscribe, simply fill out the form on your right hand side!

TRG provided great service and solutions for our recruitment and performance management process. TRG consultants are equipped with very good knowledge and have been very supportive.

 CapitaLand Vietnam

Nguyen Tuan Long CapitaLand Vietnam

[TRG consultants] are devoted to supporting our HR team for the best results by thoroughly introducing the solutions, step by step guiding how to use the assessments, simplifying the guideline for users, and on-the-job training to optimise the TRG Talent products.
 FMCG Viet

HR Manager FMCG Viet