Enterprises to invest in more data management solutions in 2022

As data continues to grow exponentially, data management solutions and data security tools are becoming highly sought after by organizations. Despite organizations lacking enough skilled tech employees, technology has enabled most of these operations to be automated.

In fact, as most data is unstructured, the data management market, be it for cloud file storage, backup, or analytics is becoming ever so competitive with big tech vendors offering a variety of solutions to match business demands. At the same time, increased amounts of data would also see organizations having to invest more to secure them.

With the global data market expected to reach US$208.67 billion by 2028, organizations are looking to data management solutions like enterprise data platforms that are can pull data from multiple business and enterprise locations and silos to a central hub.

To understand more about this, TechHQ speaks to Krishna Subramaniam, COO at Komprise to get her views on the data management solutions market and data security in 2022. Subramaniam breaks down data management and data security into five key areas next year. They are:

• The acceleration of cloud file storage
• Modernization of unstructured data volumes
• Ending the battle of data silos
• The emergence of cloud disaster recovery
• Data analysis via cloud data lakes

Cloud file storage accelerates

First, it was cloud-native applications, then block workloads, but now it’s time for file workloads to move to the cloud. Explosive growth in unstructured file data has led to data centers bursting at the seams. Covid-19 has accelerated the shift to cloud for file workloads.

Data management solutions are also enabling smart file migrations so that hot data is placed in cloud file storage and cold data is transparently and efficiently tiered at the file level to object storage. This means that customers can use data from both the file and object tiers. Another approach many vendors are taking is to provide cloud-like economics and pricing while the infrastructure remains on-premises — HPE Greenlake and Pure as a Service are examples of this trend.

Monetizing unstructured data with AI/ML and data analytics

Unstructured data has grown exponentially in recent years; meanwhile, file digitization increased dramatically during the pandemic. These pressures are resulting in a shift. IT organizations will reorganize operations and spending from cost containment and storage efficiency to data value, data access, and data management.

The opportunity is not just how to best store unstructured data according to its age and usage for hot and cold storage, but how to leverage it for competitive or operational gain. AI and machine learning in the cloud continue to deliver more capabilities for customers and are becoming core enablers of digital transformation. Progressive enterprise IT leaders will determine how to create intelligent strategies for segmenting and extracting file data for distinct use cases and monetization.

In a hybrid, multi-cloud world, the battle against silos will end

Data silos are a barrier to visibility, to fluid customer experience, to cost management, to revenues, and more. Yet here we are today in 2021, and despite – or perhaps because of – all our innovation, most companies still have uncontrolled data silos. It’s time to stop blaming silos and look for solutions that make the silos immaterial.

Decentralized IT is unstoppable with the cloud, SaaS, shadow IT, and globalization. Instead of fighting or attempting to break the silos, bridge them. Modern IT management and data management technologies enable visibility, search, monitoring across data wherever it resides, on-premises, and in the cloud.

The mainstreaming of multi-cloud architecture gives credit to the notion that silos are sensible. They give IT the means to put data where it should best reside according to several constantly morphing parameters including cost, organizational value, compliance needs, security, and more. Silos are a representation of agile innovation as in using the best technology (whether that is infrastructure, applications, or tools) for the job.

Cloud DR emerges as a top strategy for ransomware protection

Using the cloud as a primary disaster recovery strategy will increase significantly in 2022 as it’s far less expensive and resource-intensive than creating and staffing another data center. The resilience provided by the cloud is another key benefit. Cloud file storage is preferred over object storage for hot data since it is performant and enables instant recovery for users and applications. But cloud file storage is not only more expensive since you pay by the hour, it also requires backups and DR, so the costs can add up.

Also, it does not provide the same resilience as immutable object storage for ransomware protection. Running file data at scale in the cloud and making it affordable with efficient ransomware protection is going to become a greater challenge for enterprise IT organizations and will result in more attention on moving cold or warm data to object storage to increase resilience while balancing the cost.

It’s a Faustian bargain of flexibility, scale, resilience, and low-cost storage for the unpredictable costs of cloud egress fees when you recover those files back to your data center after an outage or other incident or when cold data is accessed more frequently than you’d predicted.

Successful cloud data lake initiatives will prioritize unstructured data analysis

Data analytics is growing quickly in the cloud and with the popularity of technologies such as Snowflake, Redshift, and Databricks, it is natural to predict that the cloud will become the penultimate platform for data lakes in the future. In fact, enabling data lakes was a top goal expressed by respondents in the Komprise 2021 State of Unstructured Data Management Report.

Firstly, there is cost. Cloud-based data lakes do not require the heavy upfront infrastructure investments of on-premises data warehouses. Another advantage of cloud-based analytics and data lake solutions is rapid innovation. Fuelled by the accelerated R&D engines of the three largest cloud providers, data lakes are getting smarter and more automated, democratizing this technology for most organizations which five years ago could have never afforded the compute power nor the know-how to conduct real-time analysis of massive, distributed data sets.

Yet there remain many hurdles to overcome in making cloud data lakes successful since 60-80% of data is unstructured and does not work well (if at all) within data lakes and warehouses. The merging of unstructured data into the world of structured and semi-structured analytics tools and practices will be an area ripe for innovation.