Precision is the word to focus on when it comes to cloud data management strategies for the future. Throughout this article, I shall examine the state of growth in the cloud today and share predictions for cloud data management enterprises that need to be aware of and assimilate into their growth strategies.
Cloud waste is rampant. Without detailed insights into unstructured data characteristics and storage architecture in the cloud, you might as well keep everything in your data centre. You must be analytical at every stage of the game to get the ROI you need – not just from savings on equipment and management – but to leverage new and powerful data services in the cloud. Here are some unique ways to evaluate the next steps in the cloud, save more and manage file and object data for long-term value.
Cloud NAS Will Need More Intelligent Data Migrations
File storage in the cloud is the top enterprise storage spending priority, according to the Komprise 2022 State of Unstructured Data Management Report. With better performance and lower latency than object storage and a slew of file-based enterprise applications, file storage is a popular enterprise choice. However, data lifecycle management strategies will gain mindshare as enterprises mature in the cloud and look to rein in spending overall.
Rather than moving all data to cloud file storage – the most expensive tier–organizations should migrate data in a way that leverages the diversity of cloud storage classes to cut costs significantly. One way is by identifying and tiering rarely-accessed or cold files to low-cost object classes such as AWS Glacier Instant Retrieval and Azure Blob Cool before migrating more active data to cloud NAS. As well, organizations should consider migrating data with file-object duality, so users can leverage the full cloud data services catalogue that acts on objects such as analytics, indexing and search and data workflows while still being able to access the migrated data as files, eliminating the need to rewrite existing applications or change user behaviour.
Simpler Cloud Data Management Tools Will Be in Demand as the Skill Gaps Persist
With cloud infrastructure, IT pros are still managing technology: just not physical hardware. The learning curve can be steep as cloud providers constantly revamp their offerings. Meanwhile, no cloud is the same, and complexity grows in this age of hybrid, multi-cloud deployments.
Cloud management tools help by automating key processes such as performance monitoring, configurations, provisioning, policy execution, spend analysis and optimization and reporting. Increasingly, these tools are becoming more accessible for IT generalists, allowing them to run automatic assessments of their data, network and infrastructure footprint with recommendations as to which actions to take next.
Cloud Analytics Becomes a Core Component of Unstructured Data Management
The global AI software market is expected to reach a whopping $135 billion by 2025, at a growth rate outpacing the overall software market, according to Gartner. Technavio predicts the cloud AI market will grow by over 20% in 2022. Data management strategies must follow as enterprise demand for these new tools accelerates. Unstructured data, which comprises at least 80% of all data generated, is the fuel needed to power modern ML engines. A majority (65%) of organizations in the Komprise survey indicate that they plan to or are already delivering unstructured data to their big data analytics platforms.
This trend indicates the ongoing evolution of data management from driving storage cost efficiencies to a broader mission of helping the business discover new value from enterprise data assets. To meet these new requirements, IT organizations will need capabilities to efficiently segment and classify data, enrich it through metadata tagging and facilitate automated workflows to find and move the right data sets into cloud data lakes and analytics tools.
Data Insights for Unstructured Data Management to Take Greater Priority
Advanced analytics and reporting will be the most important capability of unstructured data management solutions (53%), according to the Komprise survey. Analytics of data is imperative for making the best decisions on where data should live and when it should move elsewhere. It can answer questions like how much data I have and where it is stored, how large my files are and of what type, how old my data is, what is the cost of storing it in different places, who last accessed it and which data is “active” versus which data is “cold.”
Other top requirements for unstructured data management software include monitoring and proactive alerting of critical events such as running out of capacity, a data service becoming unresponsive, anomalous activities, automatic data tagging and global search.
Use Cases for Unstructured Data Management Will Expand Beyond Cost Savings
IT organizations typically use unstructured data management solutions to cut storage and backup costs through analytics and automation. Beyond cost savings, enterprises are most interested in data protection via secure off-site backups such as immutable object lock storage in the cloud, according to 63% in the Komprise survey, which also found preferences for searching and running analytics on unstructured data (41%), prepping data for M&A transactions (36%) and managing data deletion policies (35%).
Progressive data management strategies should encompass these three pillars: strategic cost-cutting by transparently moving older data off expensive Tier 1 storage; compliance with rules and regulations; and generating long-term value by enabling easy tagging, search and movement of specific data sets to cloud analytics platforms for native access.
