Data management

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What is Data Management?

For your data to be a strategic asset to your company, it needs to be productive. But most companies’ data isn’t ready to be transformed or fed into analytics, visualization, and machine learning engines. Most enterprise data is scattered, fragmented, and unusable. The first step to making your data productive is to create a foundation for managing it.

Data management is the practice of building this foundation for your organization’s data — to make it visible and usable across your company — and encompasses the protection, consolidation, and secure access to this data.

The objective of data management is to put your data to work. Having a secure, efficient, and cost-effective data management plan is essential for competing in the digital marketplace today.

Data management formerly required organizations to deploy an assortment of legacy point products, and more recently, a low-compatibility combination of cloud and SaaS. Modern data management platforms are comprehensive, hybrid solutions for consolidating, protecting, and reusing data to fuel growth and success.

What Does Data Management Mean?

Data management is an umbrella term for protecting, consolidating, and putting enterprise data to work. And that’s wherever the data is — on-premises, cloud, or a hybrid environment.

Data management processes and use cases span from production data (managing CRM, ERP, streaming, and other primary systems) to non-latency-sensitive data.

These are examples of data management use cases for non-latency sensitive data:

  • Backup and recovery
  • Disaster recovery and mitigation
  • File and object services
  • Dev/test provisioning
  • Data governance and compliance
  • SaaS and cloud apps protection
  • Ransomware recovery
  • Long-term retention and archiving
  • Analytics

A modern data management platform can perform all of these functions. Such a platform can be deployed on-prem, consumed as SaaS, or procured as a managed service from a service provider.

What Are the Key Data Management Skills?

Individuals in charge of data management are typically IT professionals with data center and cloud expertise.

They understand how to consolidate, protect, find, restore, and prepare various data sources — including databases, virtual machines, files and objects, SaaS applications, and more — for a variety of use cases such as data science, analytics, app development and testing, and machine learning.

What Are the Methods of Data Management?

Optimal data management methods include having the following attributes as part of your data management strategy and technologies.

Backup and Recovery Audit Checklist

Attribute What it means Is this part of your existing solution/strategy
Multicloud One platform without separate servers, clouds, SaaS products, targets, and gateways Yes | No
Single UI One interface for complete visibility into all of your data sources Yes | No
Support for both traditional and modern data sources The ability to back up all of your data, no matter what type Yes | No
As a Service capabilities Choice to consume as SaaS, deploy on-prem, or procure as a managed service Yes | No
Limitless scale-out Able to easily and automatically expand capacity as your data needs grow Yes | No
Non-disruptive upgrades No rip and replace when you need to update or upgrade the solution Yes | No
Defense against ransomware Ability to detect and help defend your data, including immutability Yes | No
Reduced data footprint An efficient way to de-duplicate your data to solve mass data fragmentation Yes | No

Auditing your current backup solution to see if you’re on the right path to predictably meet your recovery is a best practice data management method.

Predictable Recovery Audit Checklist

Attribute What it means Is this part of your existing solution/strategy?
100% backup success rate Ensure all mission-critical data is protected without backup windows bleeding into the production time or backup failures Yes | No
Global actionable search Search for any VM, files. or object across workloads and location from within the recovery workflow Yes | No
Ensuring snapshots health Automatically access snapshots health, recovery status and discover known/published vulnerabilities/cyberthreats in a consistent manner without disrupting backup or recovery workflows.

Automatically alert anomalies and identify the impacted machines for quick restore.

Yes | No
Data and application consistency Support strict consistency to ensure backups are application and data consistent. Yes | No
Rapid RTO The maximum amount of time it should take to restore application functionality (15 minutes) Yes | No
Recovery at scale Restore any number of VMs, files, or objects within a few minutes Yes | No
Restore from any recovery point Flexibility to perform recovery to any point in time Yes | No
Recovery anywhere Flexibility to recover to any target, original or alternative Yes | No

What Is a Data Management System?

A data management system is a unified set of services that help companies rein in their data. A data management system provides control of space and data growth, delivers deep visibility and searchability into a company’s data estate, and helps make a company’s data usable for downstream applications such as machine learning. A robust data management system can find, protect, and index a wide variety of data types — physical servers, virtual machines, cloud archives, object storage repositories, and databases.

What Does a Data Management System Do?

The ideal data management system:

  • Consolidates data by eliminating costly data copies
  • Provides instant indexing, visibility, searchability, and metadata
  • Features a simple interface for unified management across a company’s globally
  • distributed data estate
  • Scales limitlessly
  • Builds in enterprise-grade security
  • Provides instant protection and recoverability

What Is Data Management Software

Data management software consolidates and unifies data management functions onto a single platform for data practitioners, analysts, and engineers to use.

Enterprises can use data management software to:

  • Power data services – Protect and back up data, automate availability and replication, provide file and object services, provide a data lake for data science workloads, deliver low cost clones of databases for batch analytics, and more.

What Are Some Popular Data Management Tools?

There are a wide variety of data management tools on the market, each solving a different use case challenge. For example, Oracle and SAP both offer data management, but exclusively for databases. IBM and Microsoft both deliver cloud data management through their data lakes and data warehouses.

Enterprises, however, require platforms that support their distributed, hybrid, and varied pools of data, which encompass critical functions such as data protection and availability across on-prem, cloud, as well as SaaS.

Cohesity is one such data management platform that spreads uniformly across workloads and deployment models, and offers a comprehensive set of critical and advanced data services.

Why Is Data Management Important?

Data is the lifeblood of a modern business, and making sure that it’s protected, consolidated, and usable is critical. Without robust data management practices, companies will struggle with downstream applications such as analytics and data science.

A robust data management strategy and solution empowers your organization to:

  • Rein in your data — The first step to effective data management is to consolidate, optimize, deduplicate, and de-silo your organization’s data
  • Minimize risk of data loss – You can reduce — almost nullify — the possibility that your data will get lost, stolen, compromised, or corrupted
  • Maximize efficiency – You can eliminate duplicate data, and stop your users from having to scramble to find the data they need quickly
  • Increase agility – By maintaining high-fidelity data across your organization, multiple teams — developers, business operations, and infrastructure — can build better applications, iterate faster, and eventually, serve their customers better
  • Improve decision making – With more seamless access to high-quality data, your organization will be empowered to make data-driven decisions with more confidence
  • Strengthen security – By protecting your data from attacks such as ransomware and phishing, a robust data management plan will keep it more secure
  • Enhance governance – With the enhanced visibility you get from a solid data management strategy and solution, you can better meet security and compliance mandates

Applying data management principles to your organization can be challenging because most enterprise data — backups, databases and data warehouses, file shares, object stores and data lakes, and data used in dev/test and analytics — is fragmented across different locations and silos. Even if you are deploying SaaS applications to perform all the necessary data management functions, you likely still have to oversee various, siloed point solutions creating mass data fragmentation because of the associated overhead of different service levels, license terms, and administrative interfaces.

What’s needed is a single, easy-to-use, integrated, and modern data management solution that can be deployed in your IT environment, or consumed as a service.

Legacy Data Management Solutions

Data is fragmented. IT operations such as backups, file/object services, provisioning for test/dev, and analytics are in separate infrastructure stacks that don’t share data or resources, with no central visibility or control. Data is fragmented across and within these silos.

Data is inefficient. Infrastructure silos impact both system and operational efficiency. With no sharing of data between functions, there’s no easy optimization of capacity. This leads to multiple copies being generated, taking up unnecessary space. Operational efficiency is compromised by the need to manage and coordinate multiple proprietary systems and user interfaces, each requiring specialist training.

Data is dark. This rising volume of fragmented data is “dark” — making it almost impossible to see what data you have and where it’s stored. This can raise serious compliance or security risks, and limit storage optimization. Since you don’t know what it is, and where it’s located, you can’t know what data must be kept and what can safely be deleted.

Data Management as a Service (DMaaS) Solutions

All data is consolidated and visible. Because data is consolidated onto a single platform, you eliminate silos, and can focus on extracting insights and value from your data, not on your infrastructure.

Data is efficiently managed. You minimize data duplication and share data across data management functions to optimize capacity. A single user interface means your team doesn’t waste time learning disparate point solutions or managing multiple vendor contracts and service-level agreements (SLAs). Efficient data management saves costs and streamlines operations.

Elimination of infrastructure silos illuminates data. All data management functions are performed on a single platform, reducing security and compliance risks and enabling easy access to data for faster and more insightful decisions.

Cohesity and Data Management

Today, IT organizations face unprecedented demands to not simply support business operations efficiently, but also act as a source of innovation and competitive advantage. We believe that mass data fragmentation is the most significant roadblock to digital transformation and that more effective management of data is key to enabling IT to deliver against those expectations.

Cohesity has built a unique solution based on the same architectural principles employed by cloud hyperscalers managing consumer data, but optimized for the enterprise world. The unique capabilities of the Cohesity Helios Multicloud Data Platform allow all data management functions and workloads — including backup and recovery, DR, archiving, file and object services, cloud tiering, dev/test provisioning, and data analytics — to be run and managed in a software-defined environment across any cloud, rather than in isolated silos.

All of these functions can be managed and operated within its beautiful UI or with its rich APIs, leveraging deep automation and a unified policy engine. It makes the IT team’s job that much more enjoyable and easier. Fundamentally, Cohesity helps curtail the damaging impacts of mass data fragmentation on your business and begins to get your data to work for you.

Available in customer-managed deployments, partner-managed offerings, or as a SaaS solution, Cohesity helps you take control of your data, build data resilience and compliance, and helps your IT team become more productive to your business outcomes. Cohesity is an essential piece of the data pipelines of the world’s most successful companies.


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