The document discusses the current state of big data, highlighting the gap between hype and reality while emphasizing the importance of strategic planning and organizational structure for successful implementation. It addresses critical factors for success, including consumer analytics, market sentiment analysis, and the need for skilled personnel in data integration and analytics. Industries like media, banking, education, and healthcare are leading in big data projects, while others are still exploring their potential.

1. CXO INSIGHT
Leo Barella
By Leo Barella, VP, CTO- IT Strategic Services Team, Excellus BCBS
Rochester, NY based Excellus BCBS is a nonprofit independent licensee of the BlueCross BlueShield Association that
provides access to high-quality, affordable health coverage.
The state of Hype vs reality in Big Data
Undoubtedly, there is a lot of hype around
Big Data. But overall, the substance
outweighs the hype. Having said that,
there is still a fair amount of confusion,
for instance on how to get value from it,
and how to create a Big Data strategy.
On the technology level, there is a
lot of fragmentation, and a lot of very
niche technologies, that may offer
specific functionality, but do not excel
in integration. Big Data technologies,
such as Hadoop, have become accepted
pretty fast.
My general advice is to go ahead, and
invest in Big Data in your information
infrastructure, but accept there is still
some risk in technologies that are not
mature enough.
Factors playing a critical role in the
success of Big Data projects
Consumer–Personal Analytics:
Analytical mobile apps, wearable
technology, personal virtual assistants
& smart advisors to offer suggestions,
make recommendations, improve how
tasks are handled, etc.
Market–Sentiment Analysis: Who
are the influencers in the market on
social media, how are the brands being
discussed, what business trends need
to be followed that affect business
performance?
Partners/Suppliers/Distributors:
Through sensor-technology, creating
an (even) more efficient supply and
demand chain, for flow performance,
energy consumption optimization and
predictive asset maintenance.
Partners/Suppliers/Distributors:
More real-time, behaviour-based data
collection and prescriptive analytics for
benchmarking purposes.
Shareholder-Infonomics: Showing
how the information base, sharing
information with all stakeholders
and its analytical activities can be
economically valued, and contribute to
shareholder value.
Selection criteria to evaluate Big Data
outsourcing vendors
I would approach the question not from
the infrastructure and technology point
of view, but from a skills and capability
point of view.
Doing Big Data in the cloud is a
commonly accepted approach; you
would not be doing something “weird”.
Increasingly, it is also the option that
vendors of software and services offer.
The one reason to do it on-premises
would be the sensitivity of the data
being in the cloud.
Moving from cloud to outsourcing
is primarily a question of skills. If you
feel you don’t have both the technology
skills and analytical skills, it makes
sense to outsource.
Technology skills mostly in terms
of data integration, analytical skills
mostly in terms of data science and data
discovery.
The biggest challenges towards
successful deployment of Big Data
projects
If you are making Big Data an integral
part of the business strategy, it requires
an overall information management
strategy. You cannot buy Big Data off
the shelf. It requires strategic thinking
and executive buy-in.
The three pillars to start a Big Data
program are:
Vision & Strategy: This should
supplement your Information
Management strategy and requires the
definition of Vision and Values
Organization: This is not just
another capability in your arsenal, it
requires its own organization from
Strategy to Operation Support
Architecture: As described
above the Data and Technology
(Infrastructure) architecture needs to
be carefully planned given the intrinsic
elasticity of the information being
managed
Verticals to benefit the most from Big
Data and Analytics
In essence, every vertical yet Media/
Communication, Banking Services,
Education and Healthcare seem to have
the most amount of Big Data programs
while retail, insurance, transportation,
utilities and government are still testing
the waters.
However, it is very interesting to see
what use cases there are per industry.
My general advise which is applicable
beyond Big Data strategies is to compare
yourself to your peers, and even better
You cannot buy Big
Data o the shelf.
It requires strategic
thinking and
executive buy-in
to compare yourself to other industries
and see what ideas you can adapt to your
value streams.
The next “Big Thing” in Big Data
Simple. Big Data is moving away as a
theme of itself.
First, because volume and velocity
are temporary issues. The moment we
become more comfortable with the
speed and the data size, it becomes
information management as usual
again.
I didn’t mention variety here, which
will remain to be difficult for the
foreseeable future.
Second, it becomes part of
everything else. Big Data techniques
will be used in mobile, social, security,
analytics, business applications/BPM
and every other imaginable IT field.
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In My Opinion: CIO Viewpoint: CXO Insight:
Tom Wolf, SVP CIO,
MetLife
Matt Wolken, VP GM, Information
Management Products, Dell Software
T h e N a v i g a t o r f o r E n t e r p r i s e S o l u t i o n s
Anjul Bhambhri, VP-Big Data,
IBM
MAY - 2014 CIOREVIEW.COM
Company of the Month
David Peters
CEO, Emagine International
Gayle Sheppard
CEO and Chairman
C I O R e v i e w|April 2014 C I O R e v i e w|MAY 2014 C I O R e v i e w|MAY 2014