Big Data Myths: Debunked

Big Data Myths: Debunked

Modern technologies create a near limitless avalanche of data, creating new problems for enterprises trying to gather insight. How can IT professionals harness this flow of information to fuel business growth?

The volume of information being produced by users is escalating year on year, but more significantly the trend holds the keys to growth that aren’t being capitalized on by many companies today.

By failing to deal with big data correctly, enterprises are relinquishing countless opportunities for growth. Read on for clarification on a subject that is defining corporate success today. 

What is big data?

Big data refers to quantities of information that are too big for processing via traditional computing methods. Now a whole issue in itself, this new era of supersized information has necessitated the invention of new tools, systems and methods so that it can be managed and manipulated.

Thomas H Davenport, from Big Data in Big Companies, brings the issue into clearer focus:

“It’s important to remember that the primary value from big data comes not from the data in its raw form, but from the processing and analysis of it and the insights, products and services that emerge from analysis.”

Myth 1: Big Data is just the next fad

Last October, the Rand Corporation conducted a Data Governance Survey which revealed that the participants expected data growth between 26% and 50% in the next year, with some even indicating that they expect a data growth of 200%. The median amount of data stored by survey respondents was between 20TB and 50TB, with 22% in the petabyte range.

Myth 2: Big data comes with a big price tag

Despite assumptions to the contrary, tools and software needed for the storage and analysis of big data do not have to break budgets. Recent improvements in technology, such as cloud computing and data management tools, have significantly reduced the price of storage and analysis. Furthermore, there are a number of outsourcing opportunities that can help limit costs, further increasing the accessibility of big data management.

Myth 3: Data asset management requires complex architecture 

Specialized tools for handling big data allow for improved analysis and more reliable decision-making. As a result, business risk is brought down and companies save money as systems become more efficient.

Hadoop is an open source software framework that was purpose-built to manage data sets that are far too large to be stored on one particular server. Hadoop’s distribute file system (HDFS) is the platform’s storage component; it splits high numbers of large files and split distributes them across a series of nodes. Secondly, its MapReduce component offers architecture that allows that data to be processed.

Mike Guaultieri, Principal Analyst at Forrester, explains how Hadoop is not simply “plug and play architecture.” While companies typically need a Java programmer to use Hadoop, vendors now exist offering tools that will make Hadoop easier to use.

Myth 4: Data is enough on its own

Big data isn’t a magic solution that will enable success overnight. It can of course be a great tool for learning new insights and developing strategies, however companies need to manage their expectations; data analysis takes time.

Proper data implementation requires the proper systems to be in place. According to a survey conducted by Forbes of 211 senior marketing managers, of the companies that used big data in their marketing strategies less than half of the time, only one in three were able to meet their targets. 

Utilizing big data effectively requires a solid strategy and system in place in order to be properly analyzed in a meaningful way. Simply collecting and storing it won’t have any effect. It’s only through proper analysis and decision-making that you’ll learn the right insights to further your business objectives.

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