Five Myths That Shroud Big Data

Companies have a wealth of information about their customers from online transaction records to social media data – the key is drawing insights from all the data regardless of its channel or source. Organizations that are able to capture and analyze the data will gain a significant advantage over competitors.

The challenge is that all data is not structured the same. Businesses are accustomed to collecting and analyzing structured data like the information in traditional year-over-year sales reports. Many businesses are now struggling with how to capture and analyze other types of data that is multi-structured, some of which includes: web logs, RFID, sensor networks, social networks, Internet text and documents, Internet search indexing, call detail records, medical records, photography archives, video archives, and large scale eCommerce.

The structure of the data and the complex interrelationships of big data types do not lend themselves to analysis with today’s traditional techniques. This presents organizations with a new task to develop an infrastructure that can easily analyze and leverage, not merely store, this emerging data along with the traditional data.

Myth #1: Big data is all about volume and the growth of data

Not exactly. Yes, big data includes large volumes of traditional business data that is growing at exponential rates, but it also includes new sources of diverse data. The varied data comes from web applications, sensor networks, social networks, genomics, video, and photographs. Big data is also complex and extremely difficult to capture, store, manage, and analyze.

But both types of data are indeed growing. According to an IDC report, the Top 10 Predictions in 2011, “Businesses are drowning in information — and still want more, creating big opportunities for big data analytics and management.” Business will have their wish fulfilled, because “The ‘digital universe’ will expand by almost 50 per cent, to almost 1.8 zettabytes (nearly 2 trillion gigabytes). To provide a reference point, experts have estimated that a single zettabyte is the equivalent of 36 million years of high definition video footage.

Myth #2: Companies should rip and replace their existing analytics systems to deal with advancing big data era

No, this isn’t necessary. Building big data analytic capabilities requires the right mix of people, processes, and technology. If a company isn’t realizing value from its existing business intelligence environment, then this issue must be addressed first, before a big data initiative is started. The real value of big data analytics is realized when the analysis of traditional business data is enriched with big data insights, creating a transparent and comprehensive view of the business. That view can create opportunities that drive superior growth.

Companies should first build a plan that includes business objectives to be achieved using big data analytics. Based on these objectives, the company should acquire hardware and software that is equal to the challenge. Deploying these solutions can provide the business intelligence in demand by front line workers, helping them make the best decision possible. With the right technology in place, business users and data scientists can quickly acquire and analyze new sources of data that contain the insights the business requires.