With technology constantly developing, the way businesses treat their data also changes. Businesses in every field and industry collect and use large volumes of data, making the need for effective enterprise data management more important than ever. This data assists in making decisions, improving customer experiences, and streamlining operations.Â
We will explore seven key trends that will shape the future of enterprise data management. These trends can facilitate organisations in staying ahead by leveraging their data easily and effectively.
1. AI-Powered Data Management
Artificial Intelligence is referred to as a kind of ‘smart assistant’ for data. AI automatically organises and analyses the data to assist a business in finding key patterns and trends without much manual effort.Â
For example – AÂ retail company can use AI in the study of customers’ buying habits to predict which items will be in demand in the future. In this way, the company can stock accordingly to increase its sales. AI-powered data management saves time by rendering data more useful, it converts raw information into valuable ideas.Â
2. Edge Computing
Edge Computing is another trend in enterprise data management. This computing system is the processing of information closer to its generation instead of sending it to some server located kilometres away. This becomes quite critical as more sensors and cameras are connected to the Internet.Â
For example – In a factory, certain machines can generate information about their performance. With edge computing, this information is analysed on-site, making quick adjustments to optimise efficiency. The ripple effect of having more edge devices will make edge computing more real towards the management of enterprise data.
3. Data Governance and Compliance
As the volume of data acquisition by a business increases, so does the number of rules that govern how such data is handled. Accuracy, security, and responsible usage of data come into play in Enterprise Data Management. Compliance means laws and regulations concerning data privacy and security.Â
For example – The care provider of medical care should not reveal information about a patient except to an authorised person. Any breach of such regulations attracts fines and reputational damages for the company. Shortly, we can observe tighter data governance practices that can assist businesses in becoming compliant with the evolving laws.
4. Cloud Data Management
Cloud computing enables organisations to store and manage data over the internet instead of storing it on local servers. The access of data has become easier from any location, and besides, storage can be scaled up whenever necessary.Â
For example – A simple example can be pointed out by referring to a situation where the implementation of cloud service for storing customer information is done in an organisation, and the employees can access the information while working remotely.Â
Cloud data management also backs up and secures data, making data loss rare. As more companies work online, the trend of using cloud data management will continue due to its flexibility and cost-effectiveness.
5. Data Virtualization
Data virtualization facilitates access and analysis to a business by various sources without the need for physical movement. This means data from a variety of databases can be considered and worked on as if they were all in one place.Â
For example – A marketing team wants data from the sales and customer service departments. With data virtualization, they have all the required information without actually having to merge databases. This saves time and reduces the complexity of data management, making it easy for businesses to obtain data from it.
6. Data Lakes and Data Fabrics
Data lakes and data fabrics are in trend. They are means of managing vast volumes of unstructured data like text files, images and videos. It is a repository data storage system designed to store raw data in its native format at any scale. As opposed to the data lake, the data fabric weaves all the sources of data together for central management and analytics as one.Â
For example – A media company can store its videos in a data lake while in a data fabric, the video data would be interconnected with customer preferences. This empowers the organisation to recommend material that a viewer is most likely to appreciate. As organisations collect more diverse types of information, data lakes and fabrics are going to be essential conduits to handle it all.
7. Blockchain for Data Security
The blockchain is pretty well-recognized as the power behind the rise of cryptocurrencies like Bitcoin. However, it can also be used equally effectively to block unauthorised access to sensitive information. A blockchain is a decentralised ledger that records information in a manner which is very difficult to tamper with.Â
For example – In supply chain companies, it is used to track its products right from the manufacturer through to the ultimate customer. To ensure that any data regarding the product journey is correct and tamper-proof. This level of security could be very valuable in industries like finance and healthcare, where data integrity cannot be compromised. In the years to come, blockchain will play a huge role in securing enterprise data.
Conclusion
Enterprise data management will continue to be fast-tracked into the future, more secure and innovative, either through AI, edge computing, or even blockchain.
Enterprise data management is one of the fastest-evolving domains, and new trends have been reshaping ways of storage, processing, and usage of data by businesses. AI-powered data management can facilitate seamless data analytics, edge computing can accelerate decision-making as data closer to its source.Â
With strong data governance and compliance, companies will be in a better position to operate efficiently in convoluted regulatory landscapes. Cloud data management yields flexibility and security. Data virtualization, data lakes, and data fabrics all reduce the load of handling multi-volume and multi-format data. Blockchain employs security.
Read more: 6 Best Practices To Optimize Your Database Servers
