The era of big data has arrived. Today, companies both large and small are discovering the benefits of analyzing vast pools of unstructured data for new insights and competitive advantage. That being said, there are a number of lingering misconceptions about big data that companies looking to successfully implement a big data analytics platform need to be aware of. To that end here are five common big data myths, finally debunked.
Myth #1: Big Data adoption must happen right now
With all of the promises, big data seems to hold, a popular misconception is that those companies that don’t adopt big data analytics right now will get left in the dust. As a result, many companies rush to be among the first to deploy Hadoop analytics platforms, thinking that by stockpiling vast volumes of data they will gain a competitive edge. In reality, only those businesses that know why they are collecting data and what business results they hope to achieve through analyzing all of that information will stand to gain a true business advantage by implementing Hadoop.
Myth #2: Big Data analytics platforms will replace the data warehouse
The misguided notion that big data can provide the answer to everything has given rise to the myth that big data analytics platforms are destined to replace the data warehouse. However, the reality is that the Hadoop platform was designed to complement traditional relational database management systems (RDBMS), not to render the data warehouse obsolete. As good as Hadoop is at storing, managing, and analyzing massive volumes and varieties of data, there are other tasks, such as rapidly processing structured data and running constant and predictable workloads, that data warehouses are simply better equipped to handle.
Myth #3: Big Data benefits are marred by “bad” data
Among the many deterrents that can prevent companies from embracing big data is the misconception that their data is so flawed that even sophisticated analytics platforms like Hadoop can’t fully handle it. While the reality is that enterprise data is going to be flawed more often than not, a number of data quality, management, and governance tools—along with data visualization systems—are readily available to help companies clean up their data and make it easier to analyze.
Myth #4: Big Data implementation is expensive
Another common misconception among companies is that big data comes with a big price tag. In actuality, implementing Hadoop is very affordable, owing to the fact that Hadoop uses open source software and runs on commodity servers. Virtual Hadoop platforms, such as Qubole in the cloud, offer additional savings by totally eliminating the expense of physical servers and warehouse space. Plus the ability to spin virtual servers up or down on-demand in minutes—allowing companies with fluctuating workloads to pay only for the computing power they need when they need it—makes cloud-based Hadoop even more affordable.
Hybrid systems, which integrate Qubole’s cloud-based Hadoop with traditional relational databases, are another cost-effective option for companies to consider.
Myth #5: Big Data in the cloud is not secure.
Recent high-profile data hacks have served to perpetuate the myth that storing and processing corporate information offsite is inherently not secure. However, the fact is that today’s enterprise-grade cloud providers are constantly upping security measures to protect sensitive client data. Along with employing cloud security experts, cloud vendors keep data secure by performing automatic hardware and software updates and conducting regular third-party security audits. Needless to say, companies looking to enter into contracts with cloud providers should fully acquaint themselves with the provider’s security practices in order to better ensure the safety of sensitive information.
Myths and misconceptions about big data are abundant. Companies looking to leverage large data sets for competitive advantage by implementing a big data platform must make sure they are making their decision based on fact, not fiction.