How is Big Data Different From Previous Data?

Comparing apples to orangesThe three qualities that distinguish big data from all previous information-producing methods—big, unstructured and real-time —suggest a world of possibility. Businesses are creating and applying big data solutions in unprecedented ways that not only help them maximize profits, but also redefine their relationships with their customers.

Why get so excited? Is big data really so different from the kind of large-scale data processing that came before?

Sampling versus All the Data

Until recently, businesses analyzed massive amounts of data using statistical sampling techniques. This produced subsets of data the business could then analyze to infer information and make predictions based on those results. With today’s big data toolset, companies can analyze massive volumes of data all at once.

Big data not only puts businesses in the attractive position of being able to use massive quantities of data without sampling, it renders previous information structures impotent and requires new perspectives and technologies for turning data into information.

Agility

More often than not, companies have collected data and analyzed it later. Today, data analysis can take place in real-time. Businesses who can respond in real-time have the huge advantage of agility. An agile organization can act immediately on information to expand the business value of their data, no matter the speed at which it comes into the business.

There is a chance any dataset could be “dirty” (contains data that is not accurate). This is not new; it has long been one of the issues associated with data collection. As a result, businesses have always needed to understand the importance of listening to the data—not just the data providing good information, but also data generating more problems than it is solving. With big data technologies, an agile business can evaluate and correct potentially costly errors quickly, in real-time.

Data Sources

Prior to the big data era, businesses were constrained to using only structured data sources. These sources generate data suitable for relational databases. With big data technology, a business can start tapping into the huge pool of unstructured data now becoming available: video files; audio files; images; texts; tweets; Facebook posts and other yet to be created.

Costs

Data processing on a massive scale used to be cost prohibitive for most businesses. Big data can now become the great equalizer for businesses large and small. The cost structure and growing availability of big data solutions make them accessible to a greater number of businesses. As the technology continues to develop, big data can offer a Global 5 million, not just a Fortune 100, solution.

Business Structures

Big data does not change how we need to apply information from new data sources to long-standing business issues, but it does give companies a far more finely-tuned edge for honing business intelligence, and this is the arena in which business efforts to apply the information make all the difference.

Because big data is new, because it requires business cultures to rethink how big data will change the way their organizations operate, businesses that want to benefit from big data solutions must have an organized way to integrate all areas of data usage and information application (e.g., conversion rate marketing, across channels and silos). To accomplish this through experimentation, creative development and application, businesses must have buy-in from higher level superiors and C-level officers.

New Possibilities

Big data creates new possibilities of how we process data to generate useful information. Previously, analytics was a way of discovering information that already fit into the framework of how companies processed what they collected. Big data changes everything.

Explaining how it views big data, IBM writes, “Big data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach. Until now, there was no practical way to harvest this opportunity.”

Bryan Eisenberg (10 Posts)

Bryan Eisenberg is a keynote speaker and the coauthor of the Wall Street Journal, Amazon, BusinessWeek, and New York Times bestselling books "Call to Action," "Waiting For Your Cat to Bark?," and "Always Be Testing." Bryan was been recognized by eConsultancy members as one of the top 10 User Experience Gurus, he was selected as one of the inaugural iMedia Top 25 Marketers, and a DMEF Rising Star Award winner in 2010. He is also cofounder and chairman emeritus of the Web Analytics Association now the Digital Analytics Association. Bryan serves as an advisory board member of SES Conference & Expo, the eMetrics Marketing Optimization Summit, and several venture capital backed companies. He works with his coauthor and brother Jeffrey Eisenberg. You can find them at BryanEisenberg.com.


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