Big data use was initially limited to huge organizations, such as government agencies and corporate giants that had the funds and storage systems necessary to make use of it. However, the number of businesses that are investing in big data or have plans to do so in the future is growing at a rapid clip as firms look to capitalize on actionable insight that can put them ahead of competitors.
Big data has begun to go mainstream as more companies are finding it's possible to acquire technologies such as Hadoop, according to ZDNet. Eventually, NoSQL-based databases might follow them to implement the new strategy. The source explains they are prioritizing investments in big data as they look to gain more fact-driven support for decisions and insights, drive innovation and update their processing and technology approaches to economics.
However, businesses that rush forward with their IT plans but do not perform the necessary due diligence might end up with a big pile of data that does not achieve any of the aforementioned goals. Despite the current buzz around big data analytics, there are still a number of misconceptions that could turn companies' plans into money pits rather than gold mines.
1. The amount of data they have According to a survey by Information Week, many enterprises have widespread misconceptions about the amount of data they possess. Big data is so big - and exciting - because it combines structured content companies already have in their systems with vast amounts of unstructured data being generated by the petabyte every time consumers post on social media sites or make online purchases.
However, the study found that few businesses with big data plans in place are actually going beyond the tactics they were using before by tapping content stored on servers, archives and storage arrays. Just over one-third are using data generated by mobile devices and only 11 percent are tapping their supply chain information. This could be a mistake because vendors can't expect to derive valuable nuggets of insight if they don't parse data from multiple and previously untapped sources.
2. How much data quality they need Businesses are often guilty of another common misconception surrounding the degree to which they need to check their
data quality, according to CIO. While data quality is not the most glamorous aspect of analytics, it is one that's supremely important, as it can keep companies from making embarrassing and costly mistakes.
