Digital Transformation

Digital Transformation Trends in Data Management

Need for efficient Data Management

Data management involves gathering, storing, and utilizing data in an efficient, secure, and cost-effective manner. To regulate, secure, and analyze data, enterprises are increasingly utilizing scalable Data Management platforms (in the cloud). In this way, they can manage all aspects of their business from one place.

Under the Covid-19 guided hybrid working paradigm, businesses are seeing the value of data-driven decision-making and increasing their investments in data transformation technology.

The concept of Data management is always evolving, and various practices being introduced to enhance about how data can drive effective decision making. To get insights on customer bases, ineffective work procedures, and security risks, “information”-containing data is used.

Data Management Trends

    • Given the abundance of data, it is simple to assume that each fact is equally valuable. For data to be genuinely helpful, it must be converted completely. The data set must be cleaned up, with duplicates eliminated and incorrect data repaired. The number of different processes needed to increase the value of data has multiplied as data volumes have grown.
    • Enterprises must pay close attention while combining data from several unrelated sources. The integration of several data points could make previously unidentifiable information into individually identifiable information, which could pose a concern. 
    • In this way, data enrichment adds value by bringing new context to existing corporate data assets. Although data enrichment presents certain difficulties, businesses that have used curated third-party data are aware of its advantages. 
    • Humans are unable to manage the huge volumes of data that enterprises must analyse daily effectively, especially given the continuous talent crisis in the whole data tech sector. The large data volumes of big data continue to be a driving force behind the application of artificial intelligence (AI) and machine learning (ML) in data management. 
    • AI is rapidly being utilised to give systems more intelligence. Thanks to machine learning and operationalized AI, businesses can now analyse massive amounts of data more quickly. Companies will invest increasingly on automated insights provided by AI to supplement their current dashboards. 
    • The service landscape is changing, even if you might contract one vendor for data transformation and another for storage. A transition to more subscription-based business models will also occur. 
    • Smaller vendors will begin acquiring the technology to develop new and interesting products as well, since the big players have the resources to make substantial changes. 
    • The cloud data warehouse has become the company’s backend system due to its size and flexibility. Businesses are producing innovative ways to combine various data components and benefit from the abundant data available on these platforms.  
    • While companies are actively converting to digital platforms and remote working, it is important to recognise how multi-cloud and hybrid cloud systems are expanding. Distributing cloud computing assets, software, and applications across many cloud platforms is possible with multi-cloud. They can also make advantage of several private and public cloud infrastructures. 
    • For many firms, multi-cloud usage has become standard. As a result, their data and applications need to be adaptable to different public cloud environments and able to communicate with private, on-premises clouds. 
    • Organizations are creating detailed Data Governance policies because of the increasing complexity of data security, data audits, and data quality.
    • A functional framework for data delivery within the enterprise, data governance is built on a set of rules, protocols, and processes. It offers a variety of advantages, such as excellent Data Quality and regulatory compliance. Data Governance prevents legal violations and enhances data quality. 
    • Data scientists and other data technology professionals are in limited supply. The use of augmented data management (ADM) alleviates this shortfall. To automatically carry out simple activities like preparation and data cleansing, it makes use of artificial intelligence and machine learning. 
    • Applications of ADM
      • Data Quality-Automatically locates, fixes, and suggests guidelines for Data Quality concerns. 
      • MDM-Determines and assesses prospective master data, creates mapping data entities automatically, and sets up a master Data Management hub. 
    • Business organizations now require higher-level data officers because successful business operations depend on effective data management. To increase efficiency and revenue, a Chief Data Officer will play a strategic role in harnessing big data trends. 


Every digital transformation journey begins with accurate master data, but for asset-intensive organizations material, product information, and vendor data are the foundation of a digital framework. Before they can begin implementing their digital transformation dreams, companies of all sizes need a plan for managing master data. To succeed in digital transformation, your business needs digital-savvy leaders. Building capabilities for the future, empowering employees to think outside the box, upgrading administrative tools, and keeping communication open between team members are all necessary. This will prevent anyone from dragging the process backwards, reducing the chances of failure.  With Partlinq by Enventure, we will collaborate with you to understand your data management needs and provide you with a solution the evolves and adapts to the rapidly changing digital world.