In a day how much data is generated? The number may be unfathomable for a common man, but reports estimate that every day more than 2.5 quintillion bytes of data are being generated. With this amount of data being generated, data management becomes tricky. Irrespective of the infrastructure and best solutions and platforms available organizations, DCs and cloud service providers cannot use traditional data management tools. This is why experts today recommend combining data management with AI to ensure that every data generated is treated rightly.
Today artificial intelligence has captured every path we walk on enhancing our experience and providing better outcomes. It is the same reason why data management needs the support of AI which automates a huge portion of the process that usually involved manual work. By using AI in data management businesses can analyze and process large amounts of data quickly and accurately.
By a typical definition, data management is practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum. Data management helps migrate workloads and applications across infrastructures to Cloud or a Virtual environment with minimal or no downtime for increased business agility. Provides end-to-end Heterogeneous Business Continuity & DR Solutions helping achieve RTO, RPO and retention SLAs’ in on-premise, private or hybrid cloud. Aides business intelligence solutions with best-in-class visual analytics and ease of use capability helping customers transform data into actionable insights. Finally, provides a complete end-to-end enterprise management framework from network to applications to the web and now IOT with a single pane of glass for management. The inclusion of artificial intelligence only enhances these deliverables.
How can AI help data management specifically?
There are predominantly three categories in which AI can enhance the functioning of data management tools:
Data Integration: Data integration is the core of data management. It is the process of combining data from multiple sources and consolidating the data for operational and analytical uses. Now with artificial intelligence (AI), organizations can more efficiently analyze large sets of information and share their analyses across their business. AI in data integration allows organizations to proactively respond to issues related to their data quality rather than reacting in an ad-hoc, unstructured manner.
Data Classification: This involves obtaining, extracting and structuring data from various sources and endpoints. The unstructured data is a loss to the organization, which is why classification is necessary. It can help organizations classify data at scale using automation. The major aspect to have a successful data classification is to ensure the organization has outlined a data classification process that fits its business needs. Companies can know their current position regarding the risk of the data they manage, allowing proactive management and security.
Data Security: AI-driven security tools are capable of reducing the risks and even managing many of the threats to data security. The quick and precise analysis of data by AI helps organizations in having a real-time picture of the vulnerabilities. Using AI organizations can triage security alerts and formulate an understanding of which threat is important and needs immediate attention.
As data is being generated at great speed and number, AI is the most useful tool to have. The data generated in an unstructured form needs the support of AI to be useful to the concerned teams. With data management using AI in making the process provide near-perfect results companies can utilize the data sets customized to their needs. AI and data are the future of development for every vertical and data management is the facilitator to achieving the target.