Guest post by Finnegan Pierson.
Big data takes into account all of the many things that need to be measured or monitored, and many organizations find it hard to manage such massive data. Additionally, the amount of data being captured, replicated, or stored is way too high, and measures should be in place to ensure that everything doesn’t blow out of proportion. It’s therefore important for organizations to come up with creative approaches that can corral and optimize their data.
A lot of organizations also think that they can do away with big data by discarding the always increasing amount of storage. They would even go ahead and buy additional storage capacity every year that can only result in inflated costs and end up forcing their IT departments to spend a lot of time on data management. Big data is always growing with an average of 50 percent every year for most companies, and you need to be conversant with different ways of achieving an organized big data.
The Scale Out Architecture
Three of the greatest challenges faced when organizing big data are: storing, processing, and managing it effectively. However, with the deployment of a scale out architecture, it should be easy to store large amounts of data. Also, there have been great strides made regarding improving the processing capabilities of purpose-built organization appliances. Additionally, it is yet to be seen how big data can be managed throughout its life cycle.
It is important for organizations to take advantage of the power of virtualization technology. This means that organizations need to virtualize the unique data set so that different applications can use the same data footprint. Additionally, the smaller data footprint can be stored on any of the vendor-independent storage. Through the reduction of the data footprint, centralizing the management of the data set, and virtualizing the reuse and storage of data, big data can be transformed into small data which can, in turn, be managed like virtual data.
Once the data footprint is reduced, organizations can improve data management in three main areas. First, less time will be required by applications to process data, and this could be streamlined through spark streaming. Also, data can be secured efficiently given that the management is centralized, although the access might be distributed. Additionally, the result of the data analysis is usually more accurate given that the copies of data are visible.
The Iterative Analytical Process
One of the ways to achieve an efficiently organized big data is if it is done with respect to a particular analytics problem. To accomplish what needs to be organized, it’s important to understand the business drivers as well as the details of the semantics and structure of the big data sets. The iterative process must be done by someone who understands the fundamentals of the descriptive statistics about the data sets. Additionally, performing other data exploration tasks as well as determining how to join multiple sets is crucial in achieving an organized big data.
Development of Big Data Models
Business processes can tend to be challenging at times, and in a situation where the iterative exercise doesn’t lead anywhere, it is usually advisable to go to the drawing board. Most of the days should be spent proposing the plans on what needs to be done with the data. Developing the plans requires individuals with in-depth business knowledge who can distinctly identify any feasible and valuable big data analysis project. The input from their analysis is important since the models developed are a blueprint on how big data should be organized. When making steps towards the development of a model that achieves an organized big data, executives need to explore more options other than the storage arrays and servers.