
Industry executives and experts share their predictions for 2019. Read them in this 11th annual VMblog.com series exclusive.
Contributed by Krishna Subramanian, COO, Komprise
How AI will Shape Data Management in 2019
Artificial
Intelligence (AI) is all the buzz these days, and vendors of all types are
touting Ai in their products. But with no standards or clear definitions of
what constitutes AI, customers are right to be skeptical that AI is just the
latest marketing buzzword. There are
three clear and tangible ways in which AI can simplify data management and
watch for vendors to evolve their uses of these AI principles in 2019.
1. Adaptive Automation
via Machine Learning
No
two customers' data sets are exactly alike, nor are their infrastructure or
user needs. So why should software operate in the same way in all
environments? By applying analytics and
machine learning, data management software can observe the unique constraints
of your environment and "learn" to work smarter by adapting its algorithms to
your needs. For example, today's modern calendars don't just remind you
fifteen minutes before any appointment, they instead let you know based on the
appointment location and traffic when you should leave. Similarly, data
management software can move and analyze data based on your network, storage
and user patterns. Based on your network
and storage load and the sizes of your data, it can figure out the best order
and times of when to move data. It can tell you based on your growth rates when
you are likely to run out of space well before it happens. Watch for this type
of adaptive automation to become more advanced in 2019.
2. Goal-based versus
Rules-based policy management
Traditionally,
data management policies have been rule based - an IT administrator programs a
set of rules on how the software should function. The problem with this
approach is that a human has to predict every single eventuality and program a
rule for it. Rules could conflict with
other rules, which create problems. This
approach is very tedious, error-prone and time consuming. But, imagine if an IT administrator could set
goals e.g. I don't want anything that hasn't been used in over a year on
expensive storage, or I want these users to get the highest performance for
their data, etc. And based on your
goals, the software figures out the appropriate way to achieve them. This type of goal-based policy management
ensures IT is in control of the outcome they desire without the manual
heavy-lifting and errors of rule-based approaches. Watch for more sophisticated goal-based
policy management that combines analytics, tagging, and goal-based execution.
3. Big Data Analytics to
Extract Data Value
A key
roadblock to adoption of big data has been the difficulty in finding the right
data sets to analyze, since data is strewn across billions of files and
different storage silos, both on premises and cloud. For instance, let's say
you want to find all files related to patients who had a particular type of
cancer, imagine if you could get a virtual data lake on demand of all this data
no matter where it lived so you can then analyze it. AI can drive efficient search and discovery
of data to help us extract value from our data more efficiently, even at today's
massive scale of data.
Finally,
one trend we believe you should never subscribe to in your data management
solutions is Autonomous or automatic management. An autonomous or self-driving system automatically
makes decisions on your behalf - and while this might be good for a
self-driving car whose goal is to get you safely from point A to point B, data
management is far more nuanced. There isn't one correct goal for all scenarios
and it is important that IT maintains control and can guide the outcomes - so
beware of products that claim "fully automated management" on your behalf!
##
About the Author
Krishna
Subramanian is co-founder and COO of Komprise, the leader in intelligent data
management across clouds whose mission is to radically simplify data management
through intelligent automation. Komprise is used by enterprises to manage data
at scale and has won numerous industry awards and recognition including Gartner
Cool Vendor in Storage Technologies, 2017 and CRN Tech Innovator 2017. Prior to
Komprise, Krishna held executive roles at both large companies and startups for
over 25 years including VP Marketing for Cloud Platforms at Citrix, Co-founder
and COO of Kaviza (acquired by Citrix), Sr. Director Corporate Development and
Cloud at Sun Microsystems, and CEO of Kovair.
Subramanian holds a Master's
degree in computer science from the University of Illinois, Urbana-Champaign.
Twitter handle: @cloudKrishna