Virtualization and Cloud executives share their predictions for 2017. Read them in this 9th annual VMblog.com series exclusive.
Contributed by Avinash Lakshman, CEO and Founder of Hedvig
Cloud and Storage Technology Predictions for 2017
This past year has seen a surprising amount of change in
the storage sector. The EMC/Dell merger placed a spotlight on
the future of cloud computing and new technologies like machine learning and AI
are putting greater emphasis on metadata and in-memory
storage than ever before.
Although these seem like separate developments, they're
all steps on the path to enterprise-wide digital transformation. Digital
transformation, the widespread application of digital technologies across
society, will ultimately make on-premise datacenters obsolete as companies move
to the public cloud. But this won't happen in 2017. Many organizations today
still rely on their own data centers or onsite IT. In addition, because they
also rely on traditional storage, businesses have no easy way to embrace the
public cloud as an extension of their data centers.
As for 2017, here's what we at Hedvig are expecting:
- Monetizing
metadata will be a real business model: The nature of many
distributed systems, such as those used in large companies like Google or
Facebook, is by design to collect and store lots and lots of metadata. This
"data about the data" will become more valuable as companies analyze more of it
for insights. Organizations like Netflix have already built their success
around analyzing customer data for commonalities and many companies will
follow. Making sense of metadata, particularly metadata that has been stored
for a long time, can also lead to new customer insights as well as become the
focus of new products sold by analytics vendors.
- With
new sources of data, in-memory and temporary storage will become more
important: It can't be underemphasized that the process of
analyzing new sources of data, such as augmented and virtual reality, AI and
machine learning, is becoming critical to long-term business goals. That said,
storing the data long term is both impractical and unnecessary when the results of analysis are more important than
the data itself. Although 2017 will bring more data growth requiring permanent
storage, most new data generated will be ephemeral and be quickly discarded. As
a result, despite
exponential data growth, there won't be as much storage growth as experts might
have otherwise expected.
- Machine
Learning will become commonplace: A unique trait of
today's machine learning technology is the fact that much of it is open source.
As developers spearhead more innovation - from the bottom-up - many different products and services will include
machine learning in their platforms as a matter of course. This means that more
enterprises will adopt machine learning in 2017 without explicitly meaning to
do so because vendors are using ML to make their products more responsive and
generally smarter than before. Even existing products will soon use some form of machine learning because
the technology can be delivered easily via an update or as an extra paid
feature.
- More
enterprises will deploy multi-cloud environments: With
many companies investing more in public and private
cloud services, 2017 will see more businesses simultaneously committing to
multiple cloud providers. Fewer businesses will choose only AWS or Microsoft Azure; instead, they will
dual-source public cloud services to avoid vendor lock-in. The challenge will
be in making data services easy and productive across multiple clouds. Without
this ability, enterprise deployments will be as inefficient as they were when
businesses were still using tape.
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About the Author
Avinash Lakshman is the CEO & Founder of Hedvig. Before starting
Hedvig, Avinash built two large distributed systems: Amazon Dynamo and Apache Cassandra.
As the pioneer of NoSQL systems, Avinash is passionate about using distributed
systems to disrupt a storage space that hasn't seen any real innovation over
the last decade.
