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Komprise 2018 Predictions: Five Data Management Predictions for 2018

VMblog Predictions 2018

Industry executives and experts share their predictions for 2018.  Read them in this 10th annual VMblog.com series exclusive.

Contributed by Krishna Subramanian, COO, Komprise

Five Data Management Predictions for 2018

As data growth continues to explode in massive scale, IT is embarking on an digital transformation from managing silos of storage to sprawling estates of data across multiple storage tiers, clouds and data-centers. In 2018, we expect enterprises to focus their data management priorities on the following goals:

1.       Machine-generated Unstructured Data Growth Surpasses User-Generated Data

Trends like Internet-of-Things (IoT) along with more sophisticated instruments in virtually every industry are creating an explosion of unstructured data growth.  Whether it is better genomics sequencers or higher fidelity media or self-driving cars or higher density engineering design automation tools, these new machines are generating 2 to 10x more data than before.  As a result, businesses are looking for data management solutions that seamlessly handle both machine and user generated unstructured data without any disruption to the applications and users.

2.       Analytics enables Intelligent Automation of Data Management Policies

Artificial-Intelligence seems to be a broad theme in 2018 across industries - and while the use of AI concepts vary pretty widely, there is no doubt that the days of dumb software that require a lot of manual IT intervention are numbered.  This is true for data management as well - by analyzing data usage and growth across storage, intelligent data management solutions can adapt to the nature of the data and its usage in how they automate policies.  When done correctly, such automation is subordinate to IT and is driven by policies that are set by humans at a high level, but can self-correct and adapt to patterns in the environment. For instance, a data management solution that moves data can adaptively move larger files before smaller files or when the network is in use by others, slow itself down, etc.  By adjusting to the nature of the data and the environment, the system requires less manual management.

3.       Data Management and Governance Becomes a Strategic Line Item, no longer Tactical Spend

Businesses are becoming increasingly data-driven, making data management and governance a key Business priority that drives strategic spend in organizations.  Storage is no longer just a tactical line item - instead, it is a cornerstone of key corporate initiatives such as Cloud, Data-Driven Enterprise, and IT Modernization. With this shift, storage infrastructure leaders are increasingly being asked by CFOs, CIOs, and CEOs to provide greater visibility and reporting into data, its usage, data management, governance, and planning. 

4.       Cloud Usage is more Nuanced, Egress Costs and Performance Implications become Larger Factors

As cloud usage is now mainstream, businesses are taking a closer look at ongoing cloud costs to ensure the savings are not just short term.   In some cases, especially if the data moved to the cloud is accessed a lot from on premise, or if the data in the cloud requires high performance access from on premise, the cloud may be more expensive with its egress costs and network latencies.  Data Management solutions that not only move data to the cloud but also provide levers to control egress costs and move data that requires performance back on premise will become more useful.

5.       Another Year of Flat Storage Budgets Makes Analytics-driven Data Management Critical

Storage budgets have remained mostly flat for the past few years, and this trend is expected to continue.  Since the cost of managing and protecting data is 4x of storage, as data footprint continues to grow while budgets remain flat, businesses are realizing they can no longer afford to treat all data equally and manage it all with one broad stroke. Instead, since 80% of the data becomes cold within months of creation, businesses are adopting analytics-driven intelligent data management solutions that store and manage data differently based on its current value. Businesses are adopting analytics-driven solutions that adapt the data management dynamically based on the current value of data.

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About the Author

Krishna Subramanian 

Krishna Subramanian is co-founder and COO of Komprise, the leader in intelligent data management across clouds. Krishna runs the sales and marketing efforts at Komprise, a 100% channel-driven enterprise.

Prior to Komprise, Krishna held executive roles at both large companies and startups for over 25 years. She was VP Marketing for the Cloud Platforms Group at Citrix where she was responsible for marketing and partner development for the Citrix Desktop and Cloud businesses. Krishna joined Citrix in May 2011 through the acquisition of Kaviza, where she served as chief operating officer, and head of marketing, sales and channels. Prior to Kaviza, Krishna led mergers and acquisitions for the Sun Microsystems cloud business that delivered over a half-billion dollars of incremental revenue. Before Sun, Krishna was the CEO and co-founder of Kovair, a venture-backed software-as-a-service CRM company that grew to become a Computerworld Top 100 Emerging Company.

Subramanian holds a Master's degree in computer science from the University of Illinois, Urbana-Champaign. She brings over 20 years of industry experience in enterprise software, virtualization and cloud computing. Twitter handle: @cloudKrishna

Published Friday, January 05, 2018 7:36 AM by David Marshall
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