What is Big Data Analytics?
Artificial Intelligence (AI), mobile, social and Internet of Things (IoT) are driving data complexity, new forms and sources of data. Big Data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
Big Data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big Data comes from sensors, devices, video/audio, networks, log files, transnational applications, web, and social media – much of it generated in real time and in a very large scale.
Analyzing Big Data allows analysts, researchers, and business users to make better and faster decisions using data that was previously inaccessible or unusable. Using advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics, and natural language processing, businesses can analyze previously untapped data sources independent or together with their existing enterprise data to gain new insights resulting in better and faster decisions.
Why Is Big Data Important?
The importance of Big Data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making. When you combine Big Data with high-powered analytics, you can accomplish business-related tasks such as:-
1.Determining root causes of failures, issues and defects in near-real time.
2.Generating coupons at the point of sale based on the customer’s buying habits.
3.Recalculating entire risk portfolios in minutes.
4.Detecting fraudulent behavior before it affects your organization.
Characteristics Of Big Data:-
(i)Volume – The name Big Data itself is related to a size which is enormous. Size of data plays very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon volume of data. Hence, ‘Volume’ is one characteristic which needs to be considered while dealing with ‘Big Data’.
(ii)Variety – The next aspect of Big Data is its variety.
Variety refers to heterogeneous sources and the nature of data, both structured and unstructured. During earlier days, spreadsheets and databases were the only sources of data considered by most of the applications. Now days, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, etc. is also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining and analyzing data.
(iii)Velocity – The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data.
Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous.
(iv)Variability – This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.
Big Data Use Cases:-
Big Data can help you address a range of business activities, from customer experience to analytics. Here are just a few. (More use cases can be found at Oracle Big Data Solutions).
Companies like Netflix and Procter & Gamble use big data to anticipate customer demand. They build predictive models for new products and services by classifying key attributes of past and current products or services and modeling the relationship between those attributes and the commercial success of the offerings. In addition, P&G uses data and analytics from focus groups, social media, test markets, and early store roll outs to plan, produce, and launch new products.
Factors that can predict mechanical failures may be deeply buried in structured data, such as the equipment year, make, and model of a machine, as well as in unstructured data that covers millions of log entries, sensor data, error messages, and engine temperature. By analyzing these indications of potential issues before the problems happen, organizations can deploy maintenance more cost effectively and maximize parts and equipment up-time.
The race for customers is on. A clearer view of customer experience is more possible now than ever before. Big Data enables you to gather data from social media, web visits, call logs, and other data sources to improve the interaction experience and maximize the value delivered. Start delivering personalized offers, reduce customer churn, and handle issues proactively.
4.Fraud and Compliance
When it comes to security, it’s not just a few rogue hackers; you’re up against entire expert teams. Security landscapes and compliance requirements are constantly evolving. Big Data helps you identify patterns in data that indicate fraud and aggregate large volumes of information to make regulatory reporting much faster.
Machine learning is a hot topic right now. And data—specifically Big Data—is one of the reasons why. We are now able to teach machines instead of program them. The availability of Big Data to train machine-learning models makes that happen.
Operational efficiency may not always make the news, but it’s an area in which Big Data is having the most impact. With Big Data, you can analyze and assess production, customer feedback and returns, and other factors to reduce outages and anticipate future demands. Big Data can also be used to improve decision-making in line with current market demand.
Big Data can help you innovate by studying in-dependencies between humans, institutions, entities, and process and then determining new ways to use those insights. Use data insights to improve decisions about financial and planning considerations. Examine trends and what customers want to deliver new products and services. Implement dynamic pricing. There are endless possibilities.