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How to Avoid the 5 Scariest Big Data Pitfalls
While almost 70 percent of all companies are using big data, less than 30 percent are getting all of the goodies that's possible out of it. A number of companies launch big data initiatives only to flounder with analysis or struggle in getting a return on the investment. How can you assure that your company comes out on the winning side of big data? Start by avoiding these five pitfalls.
While almost 70 percent of all companies are using big data, less than 30 percent are getting all of the goodies that’s possible out of it. A number of companies launch big data initiatives only to flounder with analysis or struggle in getting a return on the investment. How can you assure that your company comes out on the winning side of big data? Start by avoiding these five pitfalls.
1. Getting Wrapped Up in the “Wow” and Forgetting the Business’ Needs
Perhaps the most common reasons why big data initiatives fail is that companies get all wrapped up in the eye-popping technology and forget the basics: what does the business need to get out of all this? Big data analysis should begin with seeking answers to the questions the business has, such as why so few first-time customers return to do repeat business or which online ads are turning potential customers off. Along the way, your data will reveal other noteworthy facts to ponder, but analysis should start with answering critical questions to glean needed business intelligence.
2. Failing to Switch to a NoSQL Database
A great portion of the data needed for advanced analytics is unstructured, yet many businesses hold to the hierarchical structure of the SQL database and fail to take advantage of the NoSQL DB, which can yield so much more useful information about the relationships among your data. This doesn’t mean it’s time to scrap the SQL database, but it does mean that it’s time to develop a new way to store, analyze, and retrieve unstructured data. In fact, many of the latest big data tools provide means for managing both SQL and NoSQL databases.
3. Depending Solely on Free, Open-Source Software
The abundance of powerful big data tools that are free and open source has led to rapid adoption and lots of useful development. But the free stuff is just too limited for enterprise use. Many of the open source software packages have paid versions (such as “professional version” or “enterprise version” that offer better tools for advanced analytics. The free tools are excellent for getting started with big data, but in order to advance your initiatives, you’ll need to upgrade to paid software eventually.
4. Tossing Out the Old Legacy Systems Altogether
Big data is still in its infancy. At most, it’s perhaps an inquisitive toddler. While the potential for big data is powerful and lucrative, those old legacy databases that have served your business well for decades is still essential for day-to-day operations. Until the new NoSQL database is fully functional and capable of providing your business with everything it needs, tossing the legacy system is like throwing the baby out with the bathwater. Or, maybe throwing Grandpa out with the bathwater.
5. Overlooking Critical Aspects of Security
Big data is valuable—that’s why you’re spending tons of money on data storage, analysis, data scientists, and lots of new tools and toys. The downside to having lots of valuables is that somebody, somewhere always wants to take it from you. Regulations governing the storage, transfer, and use of data are mounting, but even aside from compliance, companies that fail to secure their data adequately face legal repercussions in addition to public relations nightmares of epic proportions. Think Target, Home Depot, Michael’s, and all of the other companies with their names scrawled across headlines like, “Put Customers’ Data at Risk,” “Failed Their Customers,” and, “Allowed Thieves Access to Customer Data.” Ouch. The perfect solution to fast data analytics that remains secure is the Full Metal Cloud. A free trial is available so you can see how easy big data can be.
With these warnings, your big data initiative will be well on its way to delivering valuable business intelligence and providing a hearty return on investment.
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