What are Big Data and Why Is Companies Building Data Analytics Toolkits?
What is Big Data? In the most basic sense, Big Data is the eventual solution to the problems faced by today’s businesses, which cannot readily or cheaply put together a “system” to collect, manipulate and analyze vast amounts of real-time data that has grown so much in the last few years. The term “Big Data” itself is now in use by several dozen individuals and organizations, each trying to define the boundaries and what the technology actually means.
Large-scale structured databases are the key to all of this. The growth of internet technology has created a new phase of business in which businesses can now develop, implement, analyze and utilize big data sets. Big data is also used in areas such as healthcare, government, education, retail, and telecommunications. Big data is an evolving field which seeks to make the analysis more manageable, while simultaneously increasing productivity and lowering costs. Big Data is a concept which refers to the unsupervised structured data sets whose size, volume and high level of detail make them ideal for large-scale research.
Just what is big data? By way of definition, big data means “unstructured, real-time, inclusive” data sets. In other words, it is the mass of data that is very difficult to manage and is hence very useful for making decisions. It is this capability which allow companies to create personalized customer profile websites, generate customized marketing campaigns, and analyze customer behavior, amongst other things. Companies can also use big data analytics to improve product and service design, create personalized adverts, reduce customer retuning costs, personalize the shopping experience, and even monitor employee productivity and behavior.
However, before companies and individuals can reap the benefits of big data, they must first understand and harness the power of structured and unstructured data sets. Structured data sets, as opposed to unstructured data sets, are managed and maintained in a more efficient and disciplined manner. All information is already pre-created and usually easy to manipulate and analyze. The main advantage of structured data sets is that it helps cut down on the time required for processing data and enables users to make faster and more accurate inferences and predictions. Another advantage of structured data sets is that users can make use of all available information and select relevant portions for presentation and analysis.
On the other hand, unstructured data is raw and represents unorganized or semi-organized data. Unstructured big data has the same limitations as structured, big data in terms of its usefulness and efficiency. However, users are not required to go through the tedious process of creating, maintaining, organizing and managing structured databases. This gives them the freedom to use unstructured big data more flexibly and at a higher volume.
Both types of big data storage are quite advantageous in their own ways. With unstructured data storage, it is possible to utilize real-time insights on demand from unstructured sources and access them instantly without the need to purchase costly real-time systems and applications. Also, it is possible to use unstructured storage at lower costs because it does not require expensive network connectivity or complex server architectures. On the other hand, structured storage involves more cost-effective investments in network bandwidth and servers because it requires greater investments in servers and technology.
In the future, there will be a big data market consisting of both unstructured and structured data. Businesses will want to take advantage of both storage techniques in order to gain maximum value from their analytics tools. However, businesses must decide which technique is most applicable for their unique situation.
At Netflix, we have found streaming media, in particular, to be a valuable platform to leverage our on-demand real-time analytical resources, such as streaming ingest, real-time streaming ingest, and real-time streaming ingestion along with our on-demand data ingest. In streaming media, there are two main areas of value extraction. In one area, we make use of our proprietary real-time data ingest system called Netflix backlog. In the second area, we make use of the structured web batching system called Kinesis. In both cases, we use these tools to enable us to provide great customer experiences.