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.

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