First of all, what is Big Data? Wikipedia defines Big Data as
“… any collection of data sets so large and complex that it
becomes difficult to process them using traditional data processing
applications.”
OK, when we read this definition, most
often we don’t proceed beyond the “data sets so large” part and end up thinking that Big Data is
“really huge and vast amounts of data”. Consequently, it might lead one to
believe that all you need is monster processing power to mine, scrutinize and
bring to heel this big daddy.
In fact,
when I asked a senior executive at a multinational telecom company about what
they thought of Big Data and Analytics, they said that it was “… an exciting
field of business analysis where you got to examine gazillion bytes of complex
data and figured the usage and other characteristics of their subscribers so
that they could serve them better.” Not a bad response, however, it is only
partly correct.
So, in
order to get the most out of the Big Data, let us get our Big Data 101 primer
and go through some definitions and descriptions real quick.
Definition
of Big Data
Big Data
refers to a collection of data that is vast in its size, coming from varied
sources such as traditional customer information systems & other enterprise
level transactions, machine-generated data such as smart billing systems, call
registers, online and automated store transactions, social media streams, web
and e-commerce stores and now, you could even add data from the Internet of
Things (IoT).
The
3 V’s of Big Data
There are three
basic V’s – Volume, Velocity and Variety - that can be used to define Big Data.
(In fact, you have a few more V’s that, last I saw, added up to 6 V’s and
double what I am describing here, but more about that later). For now, the 3
V’s that characterize Big Data are:
Volume: This is the first part of the definition
from Wikipedia referenced at the top of this post and is basically the size of
the data, represented maybe not in terabytes (TB, 1012 bytes), but
perhaps more likely in petabytes (PB, 1015 bytes), exabyte (EB, 1018
bytes), zettabyte (ZB, 1021 bytes), and so on. To get an idea of the
scale of this, imagine all the data that comes in to a call data register at a
telecom company considering the millions subscribers calling each other. What volume
of data was generated in the entire year of 2000 is perhaps now being generated
every minute! Therefore, traditional databases, software programs and
analytics find it too difficult to handle data in this large scale. Newer
technologies and data tools will have to be deployed to mine, manage and
analyze.
Velocity: This is simply the speed and frequency at
which the data gets generated. We are talking about data that is generated from
the Enterprise Systems, Web & e-Commerce transactions, machines and
subscriber engagements, all coming in real-time, near real-time, batch and
periodic rates. How fast one can sample these fast flowing data to examine and derive
useful business information and intelligence.
Variety: This represents the different types of
data that come from a multiplicity of sources. They could be structured data as
in financial databases or unstructured data as in text, emails, images, audio,
video, etc., and coming in from different types of sources such as ERP, web,
social media, e-commerce transactions, etc. And then there is the Internet of
Things (IoT) which tends to combine traditional and non-traditional data that
stream-in and change at a rapid pace.
4
More V’s
Here are four
more V’s beyond the above that are being used to define Big Data:
Veracity / Validity : This signifies the dependability and
uncertainty of data. Considering the multiple sources from which data gets
generated, it is important to consider how much accuracy and trustworthiness
can be attached to the different sets of data coming in. New technology helps
us handle the quality, accuracy and abbreviated forms of data such as
emoticons, hashtags, etc., better.
Value: Many consider this as the most
important V, the holy grail of Big Data because it all boils down to how the
vast volume of a variety of data that gets blasted into our servers can be
converted to economic value. How the intrinsic information can be extracted
from it and transformed to revenue makes this a key characteristic. For
example, a telecom company can use the information extracted to analyze
subscriber and do better churn management.
Viability: This means practicability and is indicative
of what the analysis and data infrastructure can provide in real terms, over
and above just the ability to handle and store large scale, complex data. What
business rules can be generated, what attributes of data relating to purchase
cycles or purchasing history can be used to predict buying behavior, etc.
Volatility: This is about what is the window of
opportunity the company has for particular pieces or sets of data. It is
important to know the relevance and life of the data so that you can have
efficient and accurate business analytics.
Big
Data Myths
Big data is
not just confined to the field of technology, nor is it the responsibility of
the IT department. It belongs to the entire company, specifically the business
and marketing functions and helps in product development, customer engagement,
service & retention, revenue enhancement, positioning and a myriad of other
critical aspects of business.
Also, Big
Data is not a hype or fad generated by some Silicon Valley data dweebs. Big
Data technologies and tools help examine and analyze the various structured and
unstructured data to segment, understand customer behavior, get direct and
indirect feedback and make customer engagement more fulfilling to generate
value.
While it is tempting to see Big Data
as the solution to all the problems and as an answer to the question of what
next in the growth cycle, the most critical thing is not technology but a clear
strategy on how as to how you would harness this to generate insights that help
build business rules and fine tune processes to move your business forward.
Resources
Kall Ramanathan
@KallRamanathan
ValueStrat
Consulting @ValueStrat helps businesses
understand where they are currently and what they need to do to get where they
want to go. For this, we provide essential strategic plans and approaches,
called “Keys”, to enable businesses to open up competencies and clear
inefficiencies.
ValueStrat gets to the DNA of business - Desire, Need and
Ability - to help you ask some critical
questions such as discussed above. Check out http://www.valuestrat.in for more
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