Completing year-end projects may take precedence over planning for the coming year but first let’s take a step back and think about the big picture. In times of global contraction, supply chain stressors and ongoing economic volatility, IT leaders may feel like they’re living on the edge as issues beyond their control swirl about. Yet by taking a cautionary approach to spending while also not missing opportunities to be strategic where it matters, enterprises should be able to travel the murky path ahead with confidence. Komprise executives share key trends which they foretell for unstructured data management and data storage in the coming year: implications of moving to data services, edge data management, multi-cloud caution, getting smarter about data migrations, and more.
Get ready for new, business-oriented data services metrics.
In VMblog, Komprise COO Krishna Subramanian writes about the evolution of data management metrics: “Storage teams have traditionally measured infrastructure metrics for capacity and performance such as latency, I/O operations per second (IOPS) and throughput. But given the massive data growth of unstructured data, data focused metrics are becoming paramount as enterprises move away from managing storage to managing data services in hybrid cloud infrastructure. New data management metrics look at usage indicators such as top data owners, percentage of “cold” files which haven’t been accessed in over a year, most common file size and type, and financial operations metrics such as storage costs per department, storage costs per vendor per TB, percentage of backups reduced, rate of data growth, chargeback metrics and more.”
Edge data growth will require intelligent edge processing.
Kumar Goswami, Komprise CEO, PhD Computer Science, writes about edge trends for TDWI: “Explosive data growth along with consumer adoption of disruptive digital products such as self-driving cars is pushing demand for edge storage and consequently changing data management requirements to deliver visibility into edge data. This visibility will be instrumental in managing data in place at the edge through enrichment (such as data tagging) and extraction of just the right data sets for analysis. Smarter edge data management will avoid overspending on storing extraneous data in cloud data lakes and warehouses by filtering and deleting non-valuable data at the edge first. Edge analytics tools will quickly process the data without the need to send large files back and forth to cloud or on-premises data centers, saving time and money. The right edge analytics and data management program can deliver real-time insights to improve customer experiences or detect issues quickly, such as a manufacturing defect or a ransomware breach.”
Cloud analytics becomes top of mind for unstructured data management
In Spiceworks, Krishna Subramanian discusses this major trend: “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 that the cloud AI market will grow by over 20% in 2022. 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 2022 State of Unstructured Data Management survey indicate that they plan to or are already delivering unstructured data to their big data analytics platforms. 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.
Multi-cloud strategies will be less popular unless enterprises can manage cost and complexity.
Komprise VP of Marketing Darren Cunningham predicts caution with multi-cloud deployments in an economically-uncertain 2023, as covered in ITProToday: “Many IT organizations today want the flexibility of using more than one cloud provider to balance the needs of costs and different workload requirements, as well as disaster recovery tactics such as replicating data to another cloud. Yet managing multiple clouds adds management and skills costs to ensure ROI. IT teams will need full visibility across all data assets, metrics to inform decisions, and the ability to move data between platforms and environments without excessive costs (such as cloud egress) and security risks. This will require tighter alignment and integration between storage/infrastructure and security/governance/compliance teams and tools and a storage-agnostic data management strategy.”
The storage architect/engineer will take on data services.
“We’ll see more experienced individuals in these roles move on to cloud architect and other engineering roles while IT generalists/junior cloud engineers inherit their responsibilities,” predicts Krishna Subramanian in VMblog. “This is a challenging time for IT organizations in a hybrid model as there is still significant NAS expertise needed. Either way, the IT employees managing the storage function will need new skills beyond managing storage hardware. These individuals must understand the concept of data services–including facilitating secure, reliable governance and access to data and making data searchable and available to business stakeholders for applications such as cloud-based machine learning and data lakes. The new storage architect will frequently analyze and interpret data characteristics, developing data management plans which factor in cost savings strategies and business demands to create new value from data. This individual will interact regularly with departments to create and execute ongoing data management processes and plans.”
Cloud data migration pains highlight the resurgence of enterprise IT silos.
“Large-scale data migrations to the cloud, especially petabytes of file data that historically has been stored on expensive hardware platforms, will continue to be problematic for many enterprises,” predicts Darren Cunningham in ITProToday. “Migration issues — such as slow transmissions, data loss, and errors — not only derail timelines and add costs to projects but can sour the appetite for growing cloud spending. When it comes to file data migrations to the cloud, the complexity of network configurations — routing and security — has been underestimated. There are often technical bottlenecks in the way that haven’t been investigated prior to migration. Storage and networking teams are often not on the same page, which causes perpetuation of IT silos, finger pointing, and missed deadlines. It’s critical to spend more time in upfront assessment and testing of the network to prevent data migration complexities. IT executives will need to counteract silo tendencies and instead create processes for networking, storage, and security teams to work together closely for the common goal of moving large file data workloads safely and swiftly to the cloud without errors, data loss, or risks.”
Unstructured data management expands with self-service.
“Enterprise IT departments are drowning in data requests along with their daily responsibilities,” says Kumar Goswami in TDWI. “It’s time for end users and departments to play a greater role in managing their own files and data storage. With the appropriate security guardrails in place, storage professionals will benefit by sharing data management analytics with departments. By doing so, IT teams can collaborate more closely with departments to deliver data services while meeting cost savings and governance goals. For example, users can identify data sets with certain characteristics (such as project or age) to move to cloud storage to cut costs or support research initiatives. The democratization of unstructured data management will ultimately create tighter alignment and collaboration between IT and business units, which can only benefit the enterprise for the long term.”