Big data is a term that describes the large volume of data – both structured and unstructured. Big data can be analyzed for insights that lead to better decisions and strategic business moves. In this blog, let us check out the benefits of big data analytics.
Big data and big data analytics have become important research frontiers. Big data and its emerging technologies including big data analytics have been not only making big changes in the way the e-commerce and e-services operate but also making traditional data analytics and business analytics bring new big opportunities for academia and enterprises.
Big data may be new for startups and for online firms, but many large firms view it as something they have been wrestling with for a while. Some managers appreciate the innovative nature of big data, but more find it “business as usual” or part of a continuing evolution toward more data.
It’s About Variety, not Volume: Companies are focused on the variety of data, not its volume, both today and in three years. The most important goal and potential reward of Big Data initiatives is the ability to analyze diverse data sources and new data types, not managing very large data sets.
Now let’s have a look at some of the pointers which would definitely make us aware of the fact how big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
#1. Cost Reduction from Big Data Technologies
Some organizations pursuing big data believe strongly that MIPS and terabyte storage for structured data is now most cheaply delivered through big data technologies like Hadoop clusters.
One company’s cost comparison, for example, estimated that the cost of storing one terabyte for a year was $37,000 for a traditional relational database, $5,000 for a database appliance, and only $2,000 for a Hadoop cluster.
Of course, these figures are not directly comparable, in that the more traditional technologies may be somewhat more reliable and easily managed. Data security approaches, for example, are not yet fully developed in the Hadoop cluster environment.
Organizations that were focused on cost reduction made the decision to adopt big data tools primarily within the IT organization on largely technical and economic criteria.
IT groups may want to involve some of your users and sponsors in debating the data management advantages and disadvantages of this kind of storage, but that is probably the limit of the discussion needed.
#2. Time Reduction from Big Data
The second common objective of big data technologies and solutions is time reduction. Macy’s merchandise pricing optimization application provides a classic example of reducing the cycle time for complex and large-scale analytical calculations from hours or even days to minutes or seconds.
The department store chain has been able to reduce the time to optimize the pricing of its 73 million items for sale from over 27 hours to just over 1 hour. Described by some as “big data analytics,” this capability set obviously makes it possible for Macy’s to re-price items much more frequently to adapt to changing conditions in the retail marketplace.
This big data analytics application takes data out of a Hadoop cluster and puts it into other parallel computing and in-memory software architectures. Macy’s also says it achieved 70% hardware cost reductions. Kerem Tomak, VP of Analytics at Macys.com, is using similar approaches to time reduction for marketing offers to Macy’s customers.
Another key objective involving time reduction is to be able to interact with the customer in real-time, using analytics and data derived from the customer experience. If the customer has “left the building,” targeted offers and services are likely to be much less effective. This means rapid data capture, aggregation, processing, and analytics.
#3. Developing New Big Data-Based Offerings
One of the most ambitious things an organization can do with big data is to employ it in developing new product and service offerings based on data. Many of the companies that employ this approach are online firms, which have an obvious need to employ data-based products and services.
The best example may be LinkedIn, which has used big data and data scientists to develop a broad array of product offerings and features, including People You May Know, Groups You May Like, Jobs You May Be Interested In, Who’s Viewed My Profile, and several others. These offerings have brought millions of new customers to LinkedIn.
Another strong contender for the best at developing products and services based on big data is Google. This company, of course, uses big data to refine its core search and ad-serving algorithms.
Google is constantly developing new products and services that have big data algorithms for search or ad placement at the core, including Gmail, Google Plus, Google Apps, etc. Google even describes the self-driving car as a big data application. Some of these product developments pay off, and some are discontinued, but there is no more prolific creator of such offerings than Google.
#4. Supporting Internal Business Decisions
The primary purpose behind traditional, “small data” analytics was to support internal business decisions.
What offers should be presented to a customer? Which customers are most likely to stop being customers soon? How much inventory should be held in the warehouse? How should we price our products?
These types of decisions employ big data when there are new, less structured data sources that can be applied to the decision. For example, any data that can shed light on customer satisfaction is helpful, and much data from customer interactions is unstructured.
Three major banks we interviewed - Wells Fargo, Bank of America, and Discover - are also using big data to understand aspects of the customer relationship that they couldn’t previously get at. In that industry—as well as several others, including retail—the big challenge is to understand multi-channel customer relationships.
They are monitoring customer “journeys” through the tangle of websites, call centers, tellers, and other branch personnel to understand the paths that customers follow through the bank, and how those paths affect attrition or the purchase of particular financial services.
The data sources on multi-channel customer journeys are unstructured or semi-structured. They include website clicks, transaction records, bankers’ notes, and voice recordings from call centers.
The volumes are quite large—12 billion rows of data for one of the banks. All three banks are beginning to better understand common journeys, describing them with segment names, ensuring that the customer interactions are high-quality, identifying reasons for attrition, and correlating journeys with customer opportunities and problems. It’s a complex set of problems and decisions to analyze, but the potential payoff is high.
So this is how big data analytics solutions & services are impacting the Enterprise Resources. so, it could be said that big data being an Enterprise you need to take a first and the foremost step.
Being an Enterprise, if you find this information and pointers really beneficial, then you are suggested to contact the best software consulting company that can provide you the best service at the best cost.