
Virtualization and Cloud executives share their predictions for 2017. Read them in this 9th annual VMblog.com series exclusive.
Contributed by Venkat Subramanian, CTO, Dataguise
5 Trends to Watch for in 2017 in Big Data, Data Security and the Cloud
You don't need a crystal ball to be
able to foretell the following five inevitable trends that we'll see
proliferate throughout the year of 2017 and beyond:
Growth in the Data-Driven Enterprise
One of the most significant and
exciting movements emerging is that enterprises across a wide range of
industries-from finance and healthcare to technology, retail, and more-are
becoming more and more data-driven. That's because the world that we live in is
increasingly data-driven. In this new reality, what separates the most
successful organizations from the pack (regardless of industry) comes down to
the effectiveness with which they leverage their data.
Two major drivers of this trend are the
ever-escalating amount of available data, alongside the rise of extremely
data-driven organizations across all sectors. And while it comes with its share
of challenges in terms of data management, regulation, and security (see
below), the growth of the data-driven enterprise benefits businesses as well,
allowing them to be more innovative and disruptive, as well as ultimately more
effective and competitive. In short, aligning big data with business strategies
helps to ensure that customers receive the most tailored service. In this new
reality, winning companies will be the ones that turn their data insights into
improvements and innovations for customers.
Increased Interest in Risk Analytics and Compliance
The new European Union privacy
regulation-the General Data Protection Regulation (GDPR)-is preparing to take
effect in 2018. GDPR and other regulatory regimes will cause many organizations
to ramp up the attention that they pay to risk analytics and compliance in the
coming year. Companies need to understand their exposure to risk and manage
their risk tolerance, particularly in the area of regulatory compliance. Smart
use of risk analytics can help companies try to predict their risk level and
create a strategy based on insights gained from data, per the growth of the
data-driven enterprise.
In the case of GDPR, it's important
to note that this may affect your business even if your company is not based in
the EU. The new regulation will apply to any entity that
offers goods or services to EU subjects, and/or monitors data related to EU
subjects. What matters is where a person (or data subject) is located whose
data is being processed. When you keep in mind the steep fines and penalties
that can be levied against organizations that fail to comply with GDPR and
other local and global regulations, it becomes clear why risk analytics need to
be on the front burner in 2017.
More Control and Balance of Data Prior to Ingestion
In light of the trends above, it
makes sense that enterprises also need to embrace more structured data planning
and management. With the explosion in data, it is no longer practical to simply
throw all the information in without a plan; companies are newly challenged in
2017 to have a plan for use in place on how to use that data, as well as how to
securely retain and archive it.
This is the year that businesses will be looking
for ways to take complete ownership of their sensitive data across all source
types, whether big data platforms, relational databases, or
structured/unstructured data repositories. The ideal data-monitoring
solution will offer a way for users to see quickly at a glance what, where, and
how sensitive data is being detected, protected, and monitored across the
enterprise via a visual dashboard. Such a dashboard allows IT administrators to
have oversight that extends to on premise and in the cloud. The goal of every
enterprise in 2017 should be to have a comprehensive, streamlined strategy to
provide sensitive data security, privacy compliance, and risk mitigation.
Increased Urgency to Address Potential Data Breaches
Fear of a data breach is the biggest
barrier that can keep companies from realizing the benefits of big data and the
cloud. We've all seen the disturbing new stories about the rise of cybercrime
across diverse industries. From ransomware hacks to insider file tampering,
organizations face a mounting urgency to address this challenge from both a
prevention and a preparation standpoint once a breach happens.
With that in mind, data-centric security becomes
imperative to protect data against breaches. Enterprises need to continue to
figure out ways to truly guard and secure their data, as opposed to just their
systems. The type of solution needed in 2017 is one that can help companies
detect where sensitive data resides, understand who is accessing it, monitor it
in real time, and protect it with encryption and masking strategies.
Increase in Machine Learning and Cognitive Computing
As enterprise intelligence continues
to evolve, machine learning and cognitive systems become more vital. Machine
learning-which is a branch of artificial intelligence-is the design of
applications or systems that can learn based on the data that's input or
output.
How do machine learning and other
forms of cognitive computing relate to big data? Again, these trends are all
linked when you recognize that the huge influx of terabytes upon terabytes of
data is difficult for even the most robust data analyst to handle unaided. Even
with a wide range of statistical tools to help information scientists dig into
data, it is no cakewalk. Machine learning gives data scientists another way to
really mine the data for patterns-and because of this fact, more industries
will take advantage of these benefits in 2017, to help them make the most of
the volumes of information that they are now charged with understanding.
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About the Author
Venkat Subramanian serves as chief technology
officer for Dataguise, a data security software vendor based in Fremont,
California.