We’ve talked about Big Data a lot, and for a good reason. It’s relevant not only to the organizations of today, but it also holds the keys to success for companies of the future. With that in mind, we wanted to take a look at three of the fundamental elements of a successful Big Data initiative. These three items, commonly referred to as the “3 V’s,”
are volume, velocity, and variety. Each category requires its own technology for handling data processing and storage functionality. Join us as we dive into each one and take a look at how Big Data innovation is impacting them.
Organizations are collecting more and more information, including some that may not even seem relevant to them today. Often the thought is that the electronically stored information (ESI) will have value in the future and having this vast collection of ESI will eventually prove valuable. The issue then becomes storage and requires big data innovation.
Having more data available improves the accuracy of analyses, which then improves decision making and efficiency within an organization along with cost reduction and lowered risk. Hadoop provides the technology needed to handle structured, semi-structured, and unstructured data and continually feed it to the enterprises that need it. However, this also introduces new risks, according to an article from Voltage. More data means more risk of a data breach and increases the need for big data innovation.
The speed of acquiring data must also increase to give enterprises real-time information through big data innovation. For example, streaming a person’s location through their cell phone to give them applicable information about where they are and special deals or offers from nearby businesses those stores’ likelihood of a sale. The issue then becomes how to collect this data at a faster pace while still providing the security necessary, requiring big data innovation. Hadoop is not known for its ability to provide the velocity that businesses are looking for. Therefore, another system must be in place to handle this aspect of big data. Stream processing often becomes the method used to handle large volumes of data and still analyze it in a fast time frame to make it of use to organizations.
According to Gartner, a research firm, big data analytics will play a bigger role in protecting the information. It is expected that more firms will use big data analytics to prevent and detect fraud. It allows them to recognize more threats against their organizations and to act quickly to protect themselves. The velocity of collecting this data will become even more important and require big data innovation.
The third component of big data innovation is the variety. Big data comes in three forms: structured, semi-structured, and unstructured. Because of the sheer volume of data and the numerous types, it can be difficult to join them together in one analytic. Data sources are also often separated by the use of different systems in various departments with unique processes, making it difficult to analyze the data from beginning to end.
According to an article from Gartner, as more data is collected, a phenomenon known as data variety explosion occurs. The scenario is that an organization uses an SIEM tool for data analysis. The organization then looks at what other information can be analyzed, such as email content, web browsing history, and many other pieces of information. They must then begin to look at other big data innovation systems to handle this large mass of ESI and begin their own project.
Finding the perfect systems to handle all aspects of big data innovation can be difficult even when one sees the value that it provides. The answer generally lies not in one technology but in multiple to provide the volume, velocity, and variety of information needed. Need help navigating best practices and what technologies will work best in your environment? Let us help.