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Top Data Management Trends & Technology 2021

The trends in data management in 2021 reflect the continuous digital evolution that the business world has been going through for several years. Organizations are responsible for managing more data than ever before – companies in all sectors that operate within a solid data management framework have clear advantages over their competitors.

Our list of five prominent trends in data management software reflects the growing need for comprehensive, holistic approaches. Some recent trends are clearly linked to changes in corporate work structures following the 2020 pandemic response, including an abrupt shift to remote setups.

Data management today

Data management has become an indispensable part of medium-sized companies and corporations. Data is at the heart of every process and has to be regulated according to numerous compliance requirements. For example, it’s no surprise that data science professionals are among the most sought-after applicants in the job market, not just in technology-oriented positions. IIn 2020, the US faced a shortage of more than 250,000 data scientists and data engineers, so QuantHub. This deficiency is contributing to advances in data management software taking advantage of the emerging trends in artificial intelligence (AI) and automation.

5 trends in data management software

1. Hybrid and multi-cloud data strategies

The pandemic was like throwing gasoline on an already burning fire when it comes to enterprise adoption of cloud-based data resources. Suddenly, millions of employees had to access corporate data and collaborate remotely, and cloud-based solutions were often the clear winner.

Hybrid and multi-cloud approaches in particular were important drivers of cloud data management strategies.

Growth in Cloud Infrastructure Services Market over 2020 was robust as many companies sign up with multiple cloud environments. ON A whopping 93% of companies are implementing multi-cloud strategies with multiple vendors, while 87% are focusing on a hybrid cloud approach, where local and private cloud resources are connected to public cloud repositories, according to Flexera.State of Cloud Report ”2020.

Why multi-cloud? In a word: diversification. Companies are increasingly realizing the financial, security, and technological benefits of distributing data resources across different cloud environments. For example, private cloud storage is a must when it comes to protecting proprietary data assets, but some data can be securely stored and accessed over lower cost public cloud networks.

Software manufacturers are increasingly offering end-to-end solutions for hybrid data management platforms that enable companies to gain greater transparency and control over distributed data from a central location. IBM is a leader in this area. The company defines modern hybrid data management platforms as those that “ensure full accessibility regardless of source or format, support various delivery options, remove restrictions and democratize access to data, and harness the power of intelligent analytics with embedded machine learning”.

2. AI and ML

This data management trend is the continuation of a trend that has been emerging for several years and is mainly driven by big data concerns. The unprecedented amount of data businesses must manage collides with persistent staffing shortages across the technology industry, and particularly when it comes to data-centric roles.

AI and machine learning (ML) introduce manual processes that have been prone to human error. Basic data management tasks such as data identification and classification can be handled more efficiently and accurately through advanced technologies in the AI / ML area.

Organizations are using AI and ML solutions to support even more complex data management tasks, including:

  • Data cataloging
  • Metadata management
  • Data mapping
  • Anomaly detection
  • Automatic detection of metadata
  • Monitoring of data governance controls

Industry experts expect AI / ML develop. We can expect to see software solutions that offer intelligent, learning-based approaches, including search, discovery, and capacity planning.

3. Advanced data analysis

Extended data management could be implemented by the end of 2021 Reduce manual data management tasks by 45%, according to Gartner. Given the exponential growth in data volumes and a shrinking pool of Data science talent, the importance of this improvement is difficult to overestimate.

When companies succeed in attracting data science professionals, they want to get the most out of their talents rather than entrusting them with manual tasks like data cleansing. Augmented data management solutions capture, store, organize and manage data, often using AI and ML. Manually intensive tasks such as data preparation and data cleansing can be carried out with augmented data approaches.

4. Blockchain and Distributed Ledger Technology

Distributed ledger systems enable companies to keep more secure transaction records, asset tracking, and audit trails. This technology, along with blockchain technology, stores data in a decentralized form that cannot be changed, improving the authenticity and accuracy of records related to data processing, including financial transaction data, activities to retrieve sensitive data, and more.

5. Data fabric approach

Data fabric is a more recent term that encompasses the idea of interweaving different types of data from many sources. Software that focuses on improving corporate data structure includes single unified platforms that manage data differences in local and cloud environments.

Declan Owens, Expert in digital analysis piano, a global analysis and activation platform, said that while it is possible for any company to collect data, it is still necessary that data be “structured, qualitative, secure and easily accessible internally in order to drive sales and growth “.

Another prominent focus of data fabric technology is efficiency. These programs can speed up and streamline extraction, transformation and loading (ETL) processes through interconnected architectures that can be connected to multiple data sources. This technology can provide significant time savings, especially the time it takes to manually move and copy data between applications. The so-called “smooth access and sharing” is an emerging trend that will certainly continue to prevail in the near future.

Keep these current trends in mind when looking for a data management software solution. Programs and platforms quickly become obsolete if they do not contain further developments such as modern AI / ML, blockchain and holistic, centralized functions.

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