Big Data Material

7/27/2019 Big Data Material

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Big data is a buzzword youre probably getting sick of hearing but

thats no excuse to ignore this megatrend, as big data is drastically

affecting the future of analytics. The traditional analytical framework,

according to experts, is broken and unfit for handling large volumes and

varieties of data.

so what defines Big Data? Businesses today are digesting more data than

ever before but by leveraging this data with the appropriate analytics

strategy, they can realize some impressive competitive gains.

Unfortunately, most organizations have information stored in various

places and are either unable or unwilling to consolidate into a single

source, making analytics success a challenge.

That being said, there are lots of myths surrounding Big data - let us

take top 4 myths and discuss in this blog

1. Big Data is for large volume of data - Volume is just one key element

in defining Big Data, and it is arguably the least important of three

elements.(the other 2 are - Velocity and Verity - from Doug Laney ,

Gartner's research report, 2001 ###1). The definition of Big Data is far

broader than merely massive data store. But if you talk to any Bigdata

vendors, they make a big deal on petabyte / zeta byte scale data. SAP's

recent analysis shows more than 90 % of the companies are having volume

less than 50 Tera bytes of data.

The amount of data that Facebook and Tweeter are crunching remains the

exception, not the norm. You do not have a large volume of data get the

real economic value of implementing big data. Now, thanks to rapidly

increasing computer power (often cloud-based), open source software , and

a modern onslaught of data that could generate economic value if properly

utilized, there are an endless stream of Big Data uses and applications.

2. Big data is too expensive!

Implementing big data is a business decision not IT. This is a wonderfulquote that wraps up the most important best practice for implementing Big

Data. Analytics solutions are most successful when approached from

business perspective and not from IT/Engineering end. IT needs to get away

from model of Build it and they will come to Custom Order solutions to

business needs.

Use Agile and Iterative Approach to Implementation: Typically Big data


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projects start with a specific use-case and specific large data set. Over

the course of implementations, we have observed that organization needs

evolve as they understand the data once they touch and feel and start

harnessing its potential value. Use agile and iterative implementation

techniques that deliver quick solutions based on current needs instead of

a big bang application development. When it comes to the practicalities of

big data analytics, best practice is to start small by identifying

specific, high value opportunities, while not losing site of the big

picture. We achieve these objectives with our big data framework: Think

Big, Act Small

A Big Data implementation, including the integration of various

infrastructure components, can be a complex task that requires specialized

skills. In addition, as Big Data plays an increasingly vital role in

companies, it will become more important that the related infrastructure

have the kind of performance, security and support seen in other critical

business solutions. With these realities in mind, companies may want to

consider packaged, engineered systems that provide ready-made Big Data

platforms.

In essence, the potential value of these engineered solutions comes down

to reduced set-up times and streamlined ongoing managementfactors that

can be vitally important in some situations.

3. Big Data is for Social Media Feeds and Sentiment Analysis

Google and countless other companies are thriving at the epicenter of this

data explosion, both enabling and taking advantage of it. In many ways,

they represent models for any organization to more effectively use

information to its own advantage. Simply put, if your organization needs

to broadly analyze web traffic, IT system logs, customer sentiment, or any

other type of digital shadows being created in record volumes each day,

Big Data offers a way to do this.

4. Big data will turn an organization into a profitable analytics-driven

machine:

Big data provides precise, indisputable answers: Data science is a science,

requiring rigor, review and repeatable research. And scientificassumptions are always open to challenge. Mims points to the risk that

executives not trained in statistical or quantitative methods may be

relying on algorithmic illusions, as expressed by MIT Media Lab visiting

scholar Kate Crawford. Data is often flawed and biased.

5) Big data provides information you can bet your business on: If anything,


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growing reliance on big data analytics is creating a corporate bubble of

overconfidence. As Brian Bergstein of MIT Technology Review puts it: A

future in which such intuitive knowledge about how to deploy resources

is overruled by algorithms that can work only with hard data and cant, of

course, account for the data they dont have While it might seem obvious

that data, no matter how big, cannot perfectly represent life in all its

complexity, information technology produces so much information that it is

easy to forget just how much is missing.

6) Big data will turn an organization into a profitable analytics-driven

machine: Technology and data alone will not fix a moribund, clueless

corporate culturein anything, it will exacerbate it. Just as high-

quality film production and editing software is now available to anyone

who wants it for a few hundred dollars, dont expect to see thousands of

Steven Spielbergs to suddenly emergeit takes creativity, verve and keen

business sense to pull together a masterful production. Organizations

embracing data analytics need to be open to new approaches and ideas, and

above all, have a single-minded dedication to what their customers

want. Having the right data on them is only the beginning.

5.

Sometimes, I wonder what would happen if we changed the definition of big

data. What if, instead of focusing of the proverbial 3 Vs (velocity,

volume and variety), we tried something like this: 5 V's (velocity, volume,

variety, Viscosity and Virality). The viscosity measures the resistance

of flow of volume of data and Virality measures how quickly the data is

shared)

This definition might not be as glamorous as others, but it sure would be

closer to the reality most companies are trying to get the grip with big

data today.

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References :

###1. Doug Laney , Gartner's research report, 2001 -

http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-

Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

###2.Small and Midsize Companies Look to Make Big Gains With Big Data,

According to Recent Poll Conducted on Behalf of SAP

http://www.sap.com/corporate-en/news.epx?PressID=19188

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