The Real World Use of Big Data Analytics in

A Eagle Eye ViewS.Jayashree

Department of MBA,

KINGSTON ENGINEERING COLLEGE,

Vellore,India.

csjayshree29@yahoo.com

Abstract- In this paper, we explain the concept, characteristics & need of Big Data & different offerings available in the market to explore unstructured large data. This paper covers Big Data adoption trends, entry & exit criteria for the vendor and product selection, best practices, customer success story, benefits of Big Data analytics, summary and conclusion. Our analysis illustrates that the Big Data analytics is a fast-growing, influential practice and a key enabler for the social business. The insights gained from the user generated online contents and collaboration with customers is critical for success in the age of social media. The approach is premised on a platform for turbocharging business performance that converts massive volumes of data spawned by social, mobile and traditional Web-based computing into meaningful and engaging insights.

Keywords: Data adoption,collaboration, turbocharging, insights.

I. INTRODUCTION

Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. With big data analytics, data scientists and others can analyze huge volumes of data that conventional analytics[5] and business intelligence solutions can't touch. Consider this; it's possible that your organization could accumulate billions of rows of data with hundreds of millions of data combinations in multiple data stores and abundant formats. High-performance analytics is

necessary to process that much data in order to figure out what's important and what isn't.

The use of Big Data is becoming a crucial way for leading companies to outperform their peers. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value. Indeed, we found early examples of such use of data in every sector we examined. In healthcare, data pioneers are analyzing the health outcomes of pharmaceuticals when they were widely prescribed, and discovering benefits and risks that were not evident during necessarily more limited clinical trials.

Other early adopters of Big Data are using data from sensors embedded in products from children’s toys to industrial goods to determine how these products are actually used in the real world. Such knoiwledge then informs the creation of  new service offerings and the design of future products

Big Data will help to create new growth opportunities and entirely new categories of companies, such as those that aggregate and analyse industry data. Many of these will be companies that sit in the middle of large information flows where data about products and services, buyers and suppliers, consumer preferences and intent can be captured and analysed. Forward-thinking leaders across sectors should begin aggressively to build their organisations’ Big Data capabilities.

In addition to the sheer scale of Big Data, the real-time and high-frequency nature of the data are also important.

For example, ‘nowcasting,’[3] the ability to estimate metrics such as consumer confidence, immediately, something which previously could only be done retrospectively, is becoming more extensively used, adding considerable power to prediction. Similarly, the high frequency of data allows users to test theories in near real-time and to a level never before possible.

II. REACTIVE VS. PROACTIVE APPROACHES

There are four approaches to analytics, and each falls within the reactive or proactive category:

Reactive – business intelligence. In the reactive category, business intelligence (BI)[1] provides standard business reports, ad hoc reports, OLAP and even alerts and notifications based on analytics. This ad hoc analysis looks at the static past, which has its purpose in a limited number of situations.

Reactive – big data BI. When reporting pulls from huge data sets, we can say this is performing big data BI. But decisions based on these two methods are still reactionary.

Proactive – big analytics. Making forward-looking, proactive decisions requires proactive big analytics like optimization, predictive modeling, text mining, forecasting and statistical analysis. They allow you to identify trends, spot weaknesses or determine conditions for making decisions about the future. But although it's proactive, big analytics cannot be performed on big data because traditional storage environments and processing times cannot keep up.

Proactive – big data analytics. By using big data analytics you can extract only the relevant information from terabytes, petabytes and exabytes, and analyze it to transform your business decisions for the future. Becoming proactive[4] with big data analytics isn't a one-time endeavor; it is more of a culture change – a new way of gaining ground by freeing your analysts and decision makers to meet the future with sound knowledge and insight.

III. FIVE WAYS TO LEVERAGE BIG DATA1. Big Data can unlock significant value by making information transparent. There is still a significant amount of information that is not yet captured in digital form, e.g., data that are on paper, or not made easily accessible and searchable through networks. About 25 percent of the effort in some knowledge worker workgroups consists of searching for data and then transferring them to another (sometimes virtual) location. This effort represents a significant source of inefficiency.

2. As organisations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days and therefore expose variability[7] and boost performance. In fact, some leading companies are using their ability to collect and analyse big data to conduct controlled experiments to make better management decisions.

Fig 1. Big Data components

3. Big Data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services.

4. Sophisticated analytics can substantially improve decision-making, minimise risks, and unearth valuable insights that would otherwise remain hidden.

5. Big Data can be used to develop the next generation of products and services.: Manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance to avoid failures in new products.The largest repository of datas are stored in a efficient way.

IV. BIG DATA AND SUPPLY CHAIN

There has been a massive spike in supply chain data in recent times and it has become a huge challenge for enterprises to cope with ever-growing volumes of unstructured and structured data. These bits of data are compiled from a number of sources ranging from ERP systems[8] within the enterprise to the supplier's business, orders and shipment, weblogs for customer shopping patterns logistics, GPS, sensors such as RFID and Electronic On board recorders, mobile devices and social channels among others.

A Supply Chain Insights LLC survey[i] released last year found that 8% of respondents had a petabyte of data in a single database while 47% of the companies surveyed said that they expected a petabyte of data in the future. For practically all of them, Big Data was a new term in their vocabulary. But it is a term that is cropping up frequently in a number of supply chain conversations. This is because Big Data in real time can help supply chains respond to and reach customers in newer ways than before.

Fig 2. Big data management in supply chainOrganizations that create the infrastructure to capture, process, analyze and distribute the data across their supply chains will be able to adjust their capacities and inventories in real time, without missing potential business opportunities. They will be able to optimize processes and create analytical engines that help deliver accurate decisions. Even more alluring for margin-pressed businesses is the fact that Big Data from supply chain actors can help them respond with better pricing. However, given today's complex supply chains spread across the globe, the single biggest lever they can operate is to manage logistics effectively, thereby reducing costs, time-to-market and carbon footprints.

Fig 3. Life cycle of facility Management

Today's customers are difficult to predict. Forecasting their needs and preferences is risk-laden and businesses are increasingly shifting to demand-driven production models. This means it is more important than ever before to tap data sources in real time to predict market trends. Supply chains that can sense and respond to demand will help businesses integrate accurate and finely-tuned production schedules, procurement plans, staffing, distribution models, pricing structures and marketing and promotion strategy and much more.

Big Data places new demands on data manipulation and intelligence extraction, requiring an enterprise-wide cultural change towards data and analytics. The sheer volume and velocity of data being generated calls for new analytical tools and skill sets that are currently not easily accessible.

Besides, Big Data in supply chains cannot be seen in isolation. Ideally, it must be viewed as an enterprise-wide program to build Business

Intelligence (BI) in supply chains[11] . Businesses must focus on building clean and consistent data models with excellent data governance structures across functions in order to make an impact on their supply chains.

V. BIG DATA JOURNEY

1. Begin by capturing all operational data - within the enterprise, and across partners and your business eco-system that includes customers. Ensure the data is clean and not altered during its journey across systems.2. Create a centralized data repository that provides one version of the truth across the enterprise.3. Ensure that data capture and analysis is done in real time. Data latency does have an impact on the accuracy of decision making.4. Hire and train specialized data and analytical skills.5. If you can't achieve #4, ensure you have a reliable technology partner who can.

Many marketers may feel like data has always been big – and in some ways, it has. But think about the customer data businesses collected 20 years ago – point of sale transaction data, responses to direct mail campaigns, coupon redemption, etc. Then think about the customer data collected today – online purchase data, click-through rates[6]. browsing behavior, social media interactions, mobile device usage, geolocation data, etc. Comparatively speaking, there’s no comparison. And to borrow an old phrase, "You ain’t seen nothin' yet."

VI. BIG DATA MATTERS TO MARKETING

Having big data doesn’t automatically lead to better marketing – but the potential is there. Think of big data as your secret ingredient, your raw material, your essential element. It’s not the data itself that’s so important. Rather, it’s the insights derived from big data, the decisions you make and the actions you take that make all the difference.By combining big data with an integrated marketing management strategy, marketing organizations can make a substantial impact in these key areas:

Customer engagement: Big data can deliver insight into not just who your customers are, but where they are, what they want, how they want to be contacted and when.Customer retention and loyalty:Big data can help you discover what influences customer

loyalty and what keeps them coming back again and again.Marketing optimization/performance: With big data, you can determine the optimal marketing spend across multiple channels, as well as continuously optimize marketing programs through testing, measurement and analysis.

VII. THREE TYPES OF BIG DATA FOR MARKETING

Customer: The big data category most familiar to marketing may include behavioral, attitudinal and transactional metrics from such sources as marketing campaigns, points of sale, websites, customers Operational: This big data category typically includes objective metrics that measure the quality of marketing processes relating to marketing operations, resource allocation, asset management, budgetary controls,risk management,fund allocation. Financial: Typically housed in an organization’s financial systems, this big data category may include sales, revenue, profits and other objective data types that measure the financial health of the organization.

Fig 4. Trend shows us the meteoric rise in how much use of Big Data influenced marketing and sales.

VIII. FUTURE SCOPE

Big data is now a reality: The volume, variety and velocity of data coming into your organization continue to reach unprecedented levels. This phenomenal growth means that not only must you understand big data in order to decipher the information that truly counts, but you also must understand the possibilities of what you can do with big data analytics.

Big data analytics is going to be mainstream with increased adoption among every industry and forma virtuous cycle with more people wanting access to even bigger data.

However, often the requirements for big data analysis are really not well understood by the developers and business owners, thus creating an undesirable product.For organizations to not waste precious time and money and manpower[10] over these issues, there is a need to develop expertise and process of creating small scale prototypes quickly and test them to demonstrate its correctness, matching with business goals.

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