Sunday, September 19, 2021

Data Management Trends 2021

Data management trends in 2021 reflect the continuing digital evolution the business world has been undergoing for several years. Enterprises are responsible for managing more data than ever before — companies across every sector that work within a solid data management framework have distinct advantages over competitors. 

Our list of five prominent data management software trends reflects a growing need for large-scale, holistic approaches. Some current 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 integral component of mid-sized companies and enterprises. Data is at the heart of every process and must be governed according to numerous compliance requirements. It’s no surprise, for instance, that data science professionals have become some of the most in-demand applicants in the job market, period, not just among tech-focused roles. In 2020, the U.S. faced a shortage of more than 250,000 data scientists and data engineers, according to QuantHub. This shortage is contributing to advances in data management software that take advantage of 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 tossing gasoline on an already burning fire when it comes to enterprise adoption of cloud-based data resources. Suddenly, millions of workers needed to access company data and collaborate remotely, and cloud-based solutions were often the clear winner. 

Hybrid and multi-cloud approaches, in particular, have been key drivers of cloud data management strategies. 

Growth in the cloud infrastructure services market over 2020 has been robust, with many companies signing on with multiple cloud environments. A full 93% of enterprises are implementing multi-cloud, multiple provider strategies, while 87% are focusing on a hybrid cloud approach, where on-premises and private cloud resources are connected to public cloud repositories, according to Flexera’s “State of Cloud Report” 2020.

Why multi-cloud? In a word, diversification. Enterprises increasingly realize the financial, security, and technological benefits of spreading data resources across different cloud environments. For example, private cloud storage is a must when it comes to safeguarding proprietary data assets, but some data can safely be stored and accessed from more affordable public cloud networks. 

Software manufacturers are increasingly offering end-to-end hybrid data management platform solutions that allow companies to gain better visibility and control over dispersed data in a centralized location. IBM has been a leader in this space. The company defines modern hybrid data management platforms as those that “ensure complete accessibility, irrespective of source or format, support various deployment options, eliminate restrictions, and democratize access to data and embrace 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, primarily driven by big data concerns. The unprecedented volume of data enterprises are faced with managing is colliding with an ongoing staffing shortage across the tech industry as a whole and especially in relation to data-focused roles.  

AI and machine learning (ML) introduce highly valuable automation to manual processes that have been prone to human error. Foundational data management tasks like data identification and classification can be handled more efficiently and accurately by advanced technologies in the AI/ML space. 

Enterprises are using AI and ML solutions to support more advanced data management tasks as well, including:

  • Data cataloging
  • Metadata management
  • Data mapping
  • Anomaly detection
  • Metadata auto-discovery
  • Data governance control monitoring

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

3. Augmented data analytics

By the end of 2021, augmented data management could reduce manual data management tasks by 45%, according to Gartner. Considering the exponential growth of data volumes and a shrinking pool of data science talent, the importance of this improvement would be hard to overstate. 

When companies do manage to snag data science professionals, they want to get the most from their talents versus instructing them to work on manual tasks like data cleaning. Augmented data management solutions ingest, store, organize, and maintain data, often through AI and ML. Manually intense tasks like data preparation and data cleansing can be performed with augmented data approaches.

4. Blockchain and distributed ledger technology

Distributed ledger systems enable enterprises to maintain more secure transaction records, asset tracking, and audit trails. This technology, along with blockchain technology, stores data in a decentralized form that cannot be altered, improving the authenticity and accuracy of records related to data handling, including financial transaction data, sensitive data retrieval activity, and more. 

5. Data fabric approach

Data fabric is a newer term that encompasses the idea that disparate data is woven together from many origins. Software focused on improving enterprise data fabric includes single unified platforms that manage data disparities in on-premises and cloud environments. 

Declan Owens, digital analytics expert at Piano, a global analytics and activation platform, said that while it is seemingly within the reach of any company to collect data, it is still necessary for data to be “structured, qualitative, secure, and easily accessible internally to drive revenue and growth.”

Another prominent focus of data fabric technology is on efficiency. These programs can accelerate and streamline extract, transform, and load (ETL) processes through interconnected architectures that can connect to multiple data sources. Significant time savings can be realized with this tech, especially the time required to manually move and copy data between applications. So-called “frictionless access and sharing” is an emerging trend sure to continue gaining ground in the near future.

Be sure to keep these current trends in mind when seeking a data management software solution. Programs and platforms will quickly become obsolete if they don’t include advancements like modern AI/ML, blockchain and holistic, centralized features. 

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