Cloud Computing has gone mainstream. From consumers saving their photos to their online storage to businesses using powerful cloud-based algorithms to...
To the casual observer, it may seem that cloud computing has finally
gone mainstream. And to a certain extent it has. From consumers saving their
photos to their online storage account to businesses using powerful cloud-based
algorithms to unlock the secrets of their data, the cloud is everywhere. But
ubiquity does not necessarily herald progress, or power; today’s cloud industry
is one framed by a lack of understanding about how to deliver truly useful
The problem lies mainly in the assumption that scale alone is the
solution – and it is not. Size is all too often being pursued at expense of power
and functionality, two vital ingredients for any cloud platform of note. Data
is being stored, but can it also be quickly and efficiently processed, and then
mined for its hidden layers of value? This is the modern paradox of distributed
computing, but one that is easily solved by the holy trinity of cloud
computing: the rare yet extraordinarily powerful combination of big data,
computer processing, and rich algorithms.
The facts are plain: that we’re producing more data than ever - as much
in the last two years as in the rest of the entirety of human civilization in
fact; that a trillion photos were taken last
year (80% with smartphones), with billions shared and stored online; and that
within three years, a third of all this data
will pass through the cloud. As more and more of the world comes online, so the
amount of data that users generate continues to increase. But how to make sense
of this vast ocean of information? Simple storage alone is not enough.
Although businesses are now sitting on vast amounts of information, not
all data is created equal. Low-quality data is an ongoing and growing problem,
as businesses struggle to organize and collate the data they are generating.
This is also exacerbated by a general lack of structure, stemming from a wide
mix of data sources such as social, retail, transactional, machine-to-machine
etc. When real-time data is added into the mix, organizations are often left
with a cluttered, multi-scenario environment where the entire ecosystem lacks a
Power in the
To be more than just information, data needs to be processed, a massive
computational undertaking. Tools such as Hadoop have made the processing of
large-scale data in cloud environments many multiple of times easier. Open
source software such as this have rapidly reshaped the distributed computing
industry, and helped begin to make sense of the large data sets emerging from
SMEs right up to multinational organizations. New generations of similar tools
– such as NoSQL, for example – offer even more features, and can process and
store any data, even unstructured. While they lack the scalability of Hadoop,
this new generation of database is perfect for the device-heavy (and very imminent)
Internet of Things.
When combined with tools such as offline and real-time computing
platforms, suddenly a cloud environment becomes an incredibly powerful and
flexible distributed machine. This then opens the door for external parties to
take advantage of the capabilities (and cost efficiencies) associated with the
cloud, leading to the rise of Software as a Service (SaaS), Platform as a
Service (PaaS), and Infrastructure as a Service (IaaS). Of course, some
organizations choose to run some or all of their operations on an on-premise or
private cloud, but these are limited by the resources of that organization.
Thus we see the increasing popularity of hybrid cloud environments, with
businesses creating a bespoke computing environment that draws on both the
public and private cloud.
To organize and process your data is one thing, but to truly understand it
is another. Some call this the holy grail of data – the
ability to derive meaningful insights from large, seemingly benign data sets.
Many cloud scientists live by the mantra that data without insight is largely
useless, and that value will never be derived from managing data alone. Part of
realizing this goal is to move the data away from the traditional minority of data
scientists, and open it to developers and analysts who can bring the power of
the ecosystem to the table.
Supporting this evolution are increasingly powerful algorithms that feed
off the powerful computing platforms and mine big data sets for insights and
value. Traditionally developed in laboratories, the better cloud providers are
starting to integrate these tools across the platform as they seek to maximize
their efficiency. The result is an accelerated integration of data across
distributed computing platforms, and a new generation of insights from
previously unrelated data. For example, meteorological data taken on its own
has limited value, but when combined with agricultural or retail data it can suddenly
unlock significant commercial value.
Cloud computing has evolved into one of the most exciting and important
trends in business and IT today, a far cry from the early (largely
unsuccessful) days of data warehousing. From its ability to store vast amounts
of information to the innovative ways it processes it and then analyses it to
derive value, the holy trinity of cloud computing is delivering insights to
businesses around the world every second of every day. But each of these three core
elements on their own are just facets of a bigger picture. To deliver one
without the others is to fly in the face of efficiency and relegate true value
to nothing more than an afterthought.