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Cohesity 2019 Predictions: Turnover, Mass Data Fragmentation, Secondary Data, Infrastructure and ML

Industry executives and experts share their predictions for 2019.  Read them in this 11th annual series exclusive.

Contributed by Rawlinson Rivera, Chief Technology Officer, Global Field, Cohesity

Turnover, Mass Data Fragmentation, Secondary Data, Infrastructure and ML

Data has become one of the most valuable resources for all businesses of any size - but it has also one of the biggest obstacles to digital transformation. Why the paradox? It is due to a phenomenon called mass data fragmentation, that refers to the vast and growing sprawl of data scattered across different locations, trapped in infrastructure silos, and buried unseen in different point solutions. The vast majority of a company's data (up to 80%) is secondary data; it is not mission critical but provides enormous competitive benefit if it can be harnessed effectively put to work. As we close out 2018, we need to have a laser focus on the challenges and opportunities of living in a world inundated with so much data. Whether it is getting ahead of mass data fragmentation, realizing the challenges data presents to IT teams, addressing changing infrastructure models or developing strategies for machine learning, enterprises will need to adopt new approaches to data in 2019 or risk being left behind. 

1.  Turnover among IT teams will increase at companies that fail to embrace modern infrastructure management approaches. IT teams will face greater -- and often times unrealistic -- pressure to manage the growing volume of data that enterprises generate and must ensure is efficiently stored and adequately protected. Companies that don't find a way to consolidate fragmented data silos will experience IT staff turnover and burnout as data management becomes an increasingly time-consuming and tedious task. A recent survey of more than 900 IT decision makers found that nearly 40 percent of respondents believed data fragmentation would cause massive turnover on their teams, and 42 percent believed that employee satisfaction and morale would suffer (link).

2.  Companies that get ahead of mass data fragmentation will have a significant competitive advantage over those that don't. Organizations that standardize backup, disaster recovery, test/dev, and other secondary workloads on a single platform that spans on-premises data centers and public clouds will experience significant advantages over those that continue to rely on siloed infrastructure. These forward-looking companies will be able to handle the continued massive growth of data volumes, cut IT costs, and extract more value from their data, while competitors will become overwhelmed with data management tasks and compliance risks. In a recent survey, 91 percent of senior IT decision makers said that if half of the IT resources their organization spends managing secondary data were redeployed to more business-critical IT actions, it would have a positive impact on the company's revenues over a five-year period (link).

3.  Organizations will be able to easily analyze and gain insights into all of their data, ushering in a new era of compliance, discovery, and innovation: As enterprises move to consolidate data infrastructure in 2019, they will finally be empowered to analyze all of their secondary data. Secondary data makes up the vast majority of an organization's data (approximately 80 percent) and includes backups, archives, file shares, test / dev, and object stores. Traditionally, much of this has been considered "dark data" but in 2019, organizations will use SaaS-based management tools to "light up" and gain insights from that data, which can lead to environmental breakthroughs and revelations that prevent data compliance violations -- potentially saving businesses millions in fines.  

4.  New infrastructure models will empower developers to deliver better applications faster, with fewer headaches. In 2019, the application development lifecycle will be revolutionized by data platforms that give developers on-demand access to data and test/dev environments instead of requiring them to ask -- and wait -- for IT leaders to provide a copy of all relevant data sets. By removing these restrictions and delays in the test/dev process, these data platforms will empower developers to build faster and experiment with broader data sets in order to create applications that provide next-generation customer experiences.

5.  Machine learning will deliver new levels of automation and intelligence by leveraging access to secondary data sets. Machine learning has traditionally relied on a limited scope of data from primary data sets, but in 2019, the move towards a unified secondary data platform will give machine learning applications the ability to leverage enormous and diverse sets of data from across all enterprise workloads. This unprecedented access to secondary data (which typically comprises 80 percent of total enterprise data) will fuel the next generation of machine learning and automation, which can help advance autonomous vehicles, speed disease detection, and enhance the online shopping experience.


About the Author


Rawlinson Rivera is the Global Field Chief Technology Officer at Cohesity, where he is focused on defining and communicating Cohesity's product vision and strategy. With over 15 years of experience in the IT industry, Rawlinson has devoted much of his professional career to the design of enterprise architectures of physical, virtual, and cloud-based infrastructures, based on VMware, Microsoft, and other leading technologies.

Published Monday, January 21, 2019 7:22 AM by David Marshall
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