Sense of Big Data

Big Data: What it Means to IT Managers on the Front Lines

Big Data: yet another “game-changer” IT pros must grapple with these days.

Companies like Google and Facebook are demonstrating that a solid data management strategy can make a huge difference to a company’s bottom line. Corporations everywhere are paying attention; C-level executives are increasingly using insights gained from analyzing Big Data to make business decisions. As a result, companies are promoting IT from cost center to partner in strategic data management.

The term Big Data refers to the vast amounts of unstructured data that result from people’s interactions with the Internet, social media and mobile apps. It’s the kind of data that doesn’t fit neatly into rows and columns with clear relationships on which simple queries and reports can be based.

More and more, IT managers on the front lines are actively participating in efforts to extract meaning from the Big Data companies collect and store. Therefore, IT managers would do well to learn all they can about Big Data and what can be done to help their company mold a solid data management strategy.

Making sense of Big Data

Examples of Big Data are videos, images, transactions, web pages, email, social media content, click-stream data, search indexes, sensor data, etc. – a wide variety of raw, semi-structured and unstructured data that can’t be processed and analyzed using traditional processes and tools, like relational databases.

But the term Big Data also refers to the volume and velocity of the data generated today. IBM, in its e-book, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, explains it this way: the interconnectivity of people and things via technology generates data continuously; technology makes it possible to collect a massive amount of data; but, most of this data isn’t relational and can’t be processed by traditional database systems. Moreover, much of it needs to be analyzed in real time. According to this definition, Big Data encompasses data at rest and data in motion.

So it’s no small wonder that Big Data is so unwieldy. The challenge is to formulate the right questions to extract meaning out of terabytes, even petabytes (and some day zettabytes!) of data — data organizations feel compelled to collect and store even though its value is not always immediately known. 

For some companies, putting two and two together may be the only thing standing in the way of greatness.

Except making that connection is really hard. It’s expensive and time consuming to use traditional database tools to analyze Big Data, and it’s not always possible – there might be too much data in too many different formats. Plus, there’s a steep learning curve when it comes to Big Data – new tools require a new set of expertise.

Successfully Navigating Big Data

While analysis of Big Data has the potential to provide actionable insight that can generate financial windfalls for companies, if a compelling business case can’t be made to justify the project, it may be doomed from the start, says Jill Dyche, Vice-President of Thought Leadership at DataFlux Corporation, in a recent blog post. Dyche advises companies to think hard about the answers to these five questions when contemplating an investment in Big Data:

1.       What are the goals of the project and what does the company want Big Data to help it accomplish?

2.       What current resources can the company build on to develop a comprehensive data management strategy?

3.       How will the company avoid scope-creep?

4.       What are the criteria for success and how will progress be measured along the way?

5.       Can the company manage the structural and process changes that will inevitably result?

If the company can answer these questions to its satisfaction, then chances are developing a solid data management strategy to deal with Big Data is worth it.

QUANTUM Global Academy is famous for providing training in Big Data and Hadoop, Cloud Computing, PMP, Six Sigma, ITIL, event management, retail management and logistics & supply chain in Gurgaon, Delhi. 

We are here to help for improving company’s performance and productivity. For more information visit our website www.quantumglobal.org or can call us at 01244609530, 08527092037, for further query, please click here. 

Big data

Walmart started using big data even before the term big data became known in the industry and in 2012 they moved from an experiential 10-node Hadoop cluster to a 250-node Hadoop cluster. At the same time they developed new tools to migrate their existing data on Oracle, Netezza and Greenplum hardware to their own systems.

The objective was to consolidate 10 different websites into one website and store all incoming data in the new Hadoop cluster. Since then they have made big steps in integrating big data into the DNA of Walmart.

Social big data solutions

Many of the big data tools have been developed at the Walmart Labs, which was created after Walmart took over Kosmix in 2011. Some of the products that were developed at Walmart Labs are ‘Social Genome’, ‘ShoppyCat’ and Get on the Shelf.

The Social Genome product allows Walmart to reach customers, or friends of customers, who have mentioned something online to inform them about that exact product and include a discount. In order to do this they combine public data from the web, social data and proprietary data such as customer purchasing data and contact information.

 This has resulted in a vast, constantly changing, up-to-date knowledge base with hundreds of millions of entities and relationships. It helps Walmart to better understand the context of what their customers are saying online.

An example mentioned by Walmart Labs shows a woman tweeting regularly about movies. When she tweets “I love Salt”, Walmart is able to understand that she is talking about the movie Salt and not the condiment.

Walmart came across several technical difficulties when developing the Social Genome, among others the quantity and velocity the data pours into their Hadoop clusters. As the regular Map-Reduce/Hadoop framework was not able to cope with the amount and speed the data was coming in, they have developed their own tool called Muppet. 

This, now open-source, tool processes the data in real-time over all clusters and can perform several analysis at the same time.

The Shoppycat product that was developed by Walmart is able to recommend suitable products to Facebook users based on the hobbies and interests of their friends. It uses the Social Genome technology among others to help customers with presents for their friends. An interesting aspect of this Facebook App is that Walmart will direct the Facebook users to a different store in case the product is sold out at a nearby Walmart store.

QUANTUM Global Academy is famous for providing training in Hadoop & Big Data, ITIL, PMP, Six Sigma, event management, retail management and logistics & supply chain in Gurgaon, Delhi. We are here to help for improving company’s performance and productivity. For more information visit our website quantumglobal.org/ or can call us at 01244609530, 08527092037, or click on here.