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4 Common Misconceptions about Big Data

02-May-2018 11:54:01 / by Chris Selby-Rickards

Big data has big benefits, but many small business owners believe that it is a technology they cannot use. Here are four misconceptions fueling this belief.

 

Big data is one of the hottest technology trends — and for good reason. By analyzing big data, companies can gain insights that will help them make better business decisions. However, many small business owners believe that big data is a technology they cannot take advantage of.

Here are four misconceptions fueling this belief:

1. Businesses Need to Generate a Massive Amount of Data to Take Advantage of Big Data Analysis

A common misconception among small business owners is that they cannot take advantage of big data analysis because their businesses do not generate massive amounts of data. This is not the case.

Big data is not just about volume. According to experts, big data is data that cannot be processed by conventional systems (e.g., database systems) for one of three reasons: It is too big, it moves too fast, or it does not fit into the structures of those systems. In other words, big data can refer to:

  • A very large volume of data (e.g., historical data)
  • Data that is generated very rapidly (e.g., operational data from a shop floor)
  • Data in nontraditional forms (e.g., data from social networks)

Small businesses that do not generate massive amounts of data might have fast moving or nontraditional data that they can analyze. Even if small businesses do not generate any type of big data, they can take advantage of free big data repositories, such as Data.gov, Freebase, and AWS Public Data Sets. By cross-referencing their data with the big data in these repositories, they can uncover patterns and glean information they might not have known.

2. Big Data Projects Are Too Expensive

Another common misconception is that big data projects are too costly for small businesses to take on. When the term “big data” first came on the scene in the late 1990s, this type of data was not as prevalent. Plus, getting the data into analytics software was labor-intensive since it was often manually entered. As a result, implementing a big data project was a fairly expensive undertaking.

Times have changed. Thanks in large part to the explosion of mobile devices and the widespread use of the Internet, the digital universe — the data being created and copied annually — is doubling every two years, according to IDC analysts. The abundance of data already in digital format has made big data projects far less expensive to implement than in the past.

3. Big Data Projects Are Too Resource-Intensive

Many small business owners mistakenly think that big data projects require a lot of on-premises resources, including hardware and software. However, on-premises hardware and software are not needed. Many cloud service providers like IBM, SAS, and Microsoft offer big data solutions targeted at businesses of all sizes. Running a big data project in the cloud lets small businesses harness the scalable processing power of the cloud, without incurring huge capital outlays for local equipment.

4. Big Data Is Just for Large Companies

Contrary to what some small business owners might think, big data is just as useful for small businesses as it is for large companies. By analyzing big data, small companies can get insights on how to gain a competitive advantage, improve operations, and reduce costs. Just ask the owners of Twiddy & Company, a family-run business that manages vacation rentals in North Carolina. Big data analysis helped them turn years of operational data buried inside spreadsheets into actionable information. They used this information to increase their number of rentals by 10 percent and reduce costs by 15 percent.

Topics: metrics, analysis, data processing, data wranglers, big data, Data

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