Simplified Data Management
What is Simplified Data Management?
Simplified data management is a new approach to storing, accessing, preserving, analyzing, and mobilizing data. With simplified data management, businesses can eliminate the complexity and roadblocks that typically beset data and infrastructure management while unifying data operations across the data life cycle.
In the context of today’s drive toward digital transformation across industries, simplified data management has emerged as a critical business necessity, one that enables organizations to achieve new levels of innovation and agility by harnessing a cloud operational model wherever apps, data, and infrastructure live.
Why is it necessary to simplify data management?
As data has become the lifeblood of virtually every business, simplifying data management becomes essential to modernizing data operations and unlocking the value of data. Simplifying data management enables businesses to store, organize, and retrieve their data more effectively and efficiently, which is critical to accelerating digital transformation.
Data has become pivotal across enterprise operations. That may include:
- Infusing data into supply chains, distribution models, product development, manufacturing, marketing, sales, and more
- Using data to identify customer needs and incorporate customer insights into product development
- Creating an endless loop of customer input to power unique experiences that continue to evolve as enterprises gather more insight about what customers want
- Harnessing data to accelerate revenue, reimagine customer experiences, improve operational efficiency, and speed innovation
But as they seek to exploit data, enterprises must contend with fast-growing data volumes and widespread complexity. Complicated webs of fragmented data and infrastructure that span production, disaster recovery, backup, archive, test/dev, and analytics are holding back organizational transformation.
To survive and thrive, enterprises must dramatically simplify data management so that they can harness the value of data for competitive advantage.
What are some of the roadblocks to simplifying data management?
Organizations face numerous challenges in optimizing data operations and simplifying data management. These consist of:
- Lack of data access: Data access and availability are traditionally subject to repetitive manual processes managed by dedicated DBAs. Teams needing access to data wind up waiting too long for it, or use old data instead, all of which which slows down time to market.
- Data silos: Data is frequently housed in isolated systems or silos that are not linked to other resources or data. This contributes to data access issues and drives up infrastructure complexity.
- Data growth and complexity: With data growing exponentially every year, data and infrastructure sprawl can be difficult to avoid. Managing increasingly complex data environments via traditional manual processes is virtually impossible..
- Lack of data visibility: With so much data on hand, many organizations lack even a basic inventory of their data. That makes simplifying data management more difficult, and it can leave some data unprotected or out of compliance.
- Scarce resources: Ever-tightening budgets exert pressure to simplify data management and storage infrastructure, but limited resources can also make modernization initiatives harder to pursue.
What are some ways to simplify data management?
There are three keys to simplifying data management:
- Adopt data-centric policies and automation. With a continuous life cycle spanning test/dev, production, protection, and analytics, data needs to be managed holistically from creation to deletion. Apply holistic, data-centric policies and automation that collapse silos and unify workflows across the data life cycle. That means policies that manage how data is stored, accessed, protected, and mobilized — even how applications are provisioned — are data-centric and automated.
- Implement cloud-native control and operations. Instead of complex on-premises data and infrastructure management software that delivers limited visibility and requires maintainence, patching, and upgrades, opt for cloud-native control that provides edge-to-cloud management from a single plane of glass. Abstract data and infrastructure control away from physical infrastructure so teams can manage their workflows from the cloud, orchestrate them wherever data lives, and gain faster access to features through cloud services.
- Leverage AI-driven insights and intelligence. A critical component of simplified data management, AIOps deeply integrates AI into data operations. With it, organizations can rely on predictive analytics to avoid disruptions, rebalance workloads and resources as needed, and instantly provision applications across the entire fleet without any planning or calculations.
HPE and Simplified Data Management
HPE (Hewlett Packard Enterprise) offers a comprehensive range of products and services on the HPE GreenLake edge-to-cloud platform designed to radically simplify data management and accelerate business transformation, including:
- HPE Greenlake for Block Storage: Unlock agility and speed the pace of new apps, services, and initiatives with self-service, SLA-based block storage. Simplify storage management with a cloud operational experience on-premises — and run any app without compromise on enterprise-grade storage that you can order in minutes.
- HPE GreenLake for HCI: Build your self-service cloud on demand across on-prem, cloud, and edge environments with cloud-based management and self-service agility.
- HPE GreenLake for Backup and Recovery: Modernize data operations and simplify hybrid cloud data protection with a single interface to deliver snapshots for restores, recoveries, and cloud backups.
- HPE GreenLake for Disaster Recovery: Unlock the fastest recovery experience in a simple, as a service platform with unlimited scale. Enjoy industry-leading RPOs and RTOs and cloud-like functionality while slashing complexity.
- HPE InfoSight: Harness the industry’s most advanced AI for infrastructure to deliver self-managing, self-healing, self-optimizing AIOps from edge to cloud.