How Big Data Analyzes Data of Your Business Operations Differently

How Big Data Analyzes Data of Your Business Operations Differently
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by Sanjeev Kapoor 10 Dec 2015

Big data has become a catchword for the collection of large data sets that were previously difficult to process. Big data is continuing to grow because of the use of mobile devices, aerial sensory technologies, microphones, RFID readers, software logs and even social media usage. Moreover, companies themselves generate large amounts of data which can collectively be termed as big data.

The amount of consumer and client data that is generated and stored across the cloud has reached intimidating yet impressive levels. In fact, the data that companies generate are further used and processed to understand consumers, the target audience and probably even to recycle leads and prospects. Data generated by the Internet giants like Twitter, Facebook and by medium and smaller companies can also be termed as ‘big data’.

Companies have begun to understand the value of big data and the kind of analytics it can provide us with. Consequently, it has led to an entire industry which develops big data tools and platforms. Big data consists of structured, semi-structured and even unstructured data. All these kinds of data can be processed, analyzed and used for different purposes. With that in mind, we must remember that the data generated by our business operations are analyzed significantly differently.

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The Four Vs of Big Data: Volume, Variety, Veracity and Velocity

Big Data is not only varied but it is growing at a very rapid rate. Multiple industries are working together to bring data, cloud and engagement to create solutions for companies that result in more agile efficient and competitive operations. Modern information management architectures make use of the Big Data to discover fresh insights with the help of data management and information integration and governance. Let us take a look at the much-touted ‘Four Vs’ of Big Data, namely; Volume, Variety, Veracity and Velocity.

A.    Volume

Industry analysts point to the fact that almost 43 trillion gigabytes (40 Zettabytes) of data will be created by 2020. This will be a 300-fold increase from the amount of data that existed in 2005. With more than 6 billion people using cell phones and with most American companies having at least 100 Terabytes of data, there is a huge amount of data that is waiting to be analyzed, structured and used for insight.

The volume of Big Data will only increase with the multiplication of the number of channels that create data. Effective processing of this large amount of data will help companies to gain insight and achieve unparalleled scalability.

B.     Variety

An Infographic published by the IBM Big Data & Analytics Hub reveals that 161 billion gigabytes of healthcare data was generated around 2011. With an estimated 420 million wearable, wireless health monitors, the kind of data emerging from the healthcare industry alone is mindboggling.

400 million tweets a day, 30 billion pieces of content shared every month on Facebook; and with more than 4 billion hours of video being watched on YouTube every month, there is practically an insurmountable amount of data that is just waiting to be analyzed and processed.

All this data comes from varied sources like IT, sciences, social care, humanities, banking, finance and healthcare. This is the Variety aspect of the Big Data. With such a large variety of data being available to be analyzed, we can only imagine the benefits they will offer to us.

C.    Veracity

Big Data is veracious in nature. Veracity can be defined as an unwillingness to tell lies. As more data is accumulated, data becomes credible. For instance, one company may release data that is unscrupulous and another may release that is practically falsified.

However, there are thousands of other companies that work honestly to create data that is credible, reliable and valid. Statistically, this adds to the veracity of Big Data. It is increasingly being found that 33% of business leaders do not trust the information they receive.

An astonishing $3.1 trillion a year is wasted because of poor quality of data. With an increasing number of people suspecting the data that they view, it becomes important to access big Data, which is veracious in nature primarily because of the sheer volume of data that exists. The larger the data, the more veracious and truthful it is, statistically speaking. Cost-effective data and statistics will be available to companies that need it, thanks to the veracity of Big Data.

D.    Velocity

The same IBM infographic that we mentioned earlier reveals that by 2016, there will be more than 18.9 billion network connections. That implies, there will be at least 2.5 connections per person on the planet. The speed with which data will be created and the way this streaming data will be analyzed is the Velocity of Big Data.

Data will be churned out with such force and velocity that companies will be able to accelerate their own growth by making cost-effective, scalable and efficient decisions that will lead to agile operational methods. Of course, all this means, data stored in the Big Data need not be stored on local servers. Instead, the cloud can be made use of to enhance agile storage solutions and effective processing of information.

Making sense of an unstructured world

While we now understand the 4 Vs of Big Data, we must also take a look at the inherent lack of structure in certain areas of the Big Data. Like we mentioned earlier, Big Data is not only structured, but it is also semi-structured and unstructured. There are clear benefits of analyzing and implementing operations to analyze unstructured data. Traditional data processing cannot handle unstructured and semi-structured data. On the other hand, data that is unfit for traditional processing can be efficiently handled by increased system intelligence arising from the Big Data.

Let us take a look at how analyzing and processing unstructured and semi-structured data with the help of Big Data analytics will help us:

1.      Improved performance in sales

Unstructured data can be analyzed by Big Data analytics to gain important insights about markets that aren’t completely tapped or understood. It may also help us to create sales techniques that are original, innovative and very different from traditional methods derived from traditionally structured data.

2.      Increased understanding of customer needs

As the Big Data encompasses all the data that is generated all over the world from different domains, it becomes increasingly easy to understand customers from perspectives that are unique and authentic. This leads us to develop campaign methods that are catered to customer needs.

3.      Enhanced fraud monitoring

With more companies growing wary about Internet frauds and security issues arising every other time, the Big Data helps to trace and monitor fraudulent activities that are no t in sync with well-established operations. Thus, Big Data can help companies and individuals to monitor and track activities which could turn out to be fraudulent.

4.      Reinforced internal risk management function

Companies that utilize the Big Data analytics will find it easier to assess their internal risks, as compared with similar risks faced by other business entities elsewhere. Confidentiality taken care of, risk management will receive a huge boost from the Big Data. Interestingly, what may seem to be a threat to security and a cause for risk will end up becoming the solution for potential risks. Thus, companies will be able to reinforce internal risk management functions.

5.      Supported marketing initiatives and more

The Big Data helps companies to access information that is otherwise inaccessible. Unless data is structured, traditional data mining techniques simply do not assist in analyzing unstructured and semi-structured data which otherwise could have provided valuable insight regarding the behavior of consumers and customers. By implementing analytical tools that help to analyze unstructured parts of the Big Data, companies can support existing marketing initiatives and create better sales reports, leading to a better ROI.

Elimination of intuition and adoption of scientific data analysis

Big Data analytics helps businesses to eliminate intuition and depend on scientific methods for conducting business operations. In fact, one can conclude that Big Data has brought together all the pieces of information that previously existed but remained inaccessible.

It is almost like a Gestalt image that makes the big Data more important than the sum of its parts: structured, semi-structured and unstructured data. This Gestalt way of looking at data and analyzing it will help us to eliminate intuition and luck, which have always been two of the only available heuristics to understand data that were previously inaccessible or unstructured.

Progress in the field of Big Data and analytics within the field of Big Data will help companies to take a planned approach to receive a data-driven insight that doesn’t depend on intuition, but on measurable, scientific and statistically relevant analysis of all kinds of unstructured, semi-structured and structured data.


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