Big Data Analytics Driven Enterprise Asset Management For Asset Intensive Industries

Competing on analytics is currently one of the most essential qualities for companies looking to gain a larger market share in their given industry. This is due to the fact that the easiest way for corporations to differentiate themselves from their direct competitors is to maximize efficiency through cohesive processes and decision making. Analytics is defined as, “The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact based management to drive decisions and actions.” (Davenport, 2007, p.7) So in order for companies to compete on analytics they need to be willing to invest in the proper technologies that are able to compile all of this information and data into output that can be used

Where is this value found and how can it be justified and proven to the rest of the organization?

big data is a dynamic that seemed to appear from almost nowhere. But in reality, Big Data is not new – and it is moving into mainstream and getting a lot more attention. the growth of Big Data is being enabled by inexpensive storage, a proliferation of sensor and data capture technology, increasing connections to information via the cloud and virtualised storage infrastructure, as well as innovative software and analysis tools. It is no surprise then that business analytics as a technology area is rising on the radars of CiOs and line-of-business (lOB) executives. to validate this, as part of a recent survey of 5,722 end users in the uS market, business analytics ranked in the top five It initiatives of organisations. the key drivers for business analytics adoption remained conservative or defensive. the focus on cost control, customer retention and optimising operations is likely a reflection of the continued economic uncertainty. however,

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” – Geoffrey Moore, author and consultant.

Big Data is defined as data sets that are so large that they defy conventional applications, frameworks and methods for analyzing them. The proliferation of Big Data is attributable to the amount of data companies across all industries are capturing on transactions with suppliers, customers, distribution channels and services organizations over years of activity. Big Data, by its very nature of spanning a multitude of databases and conventional data storage platforms within organizations, becomes difficult to capture, store, search and complete analytics on. For the manager in an organization who has these conventional methods of data search, analytics and visualization available to them, Big Data can quickly become overwhelming given the limited scope of tools available and the sheer amount of data available (Jacobs, 2009). For the manager attempting to gain greater insights into their organization's processes, strategies and overall performance, Big Data can quickly becoming overwhelming. The intent of this analysis is to provide guidance to managers on how they can better manage Big Data to provide the maximum analytical insight and intelligence about their organizations.

The emergence of new technologies, applications and network systems makes it hard to run the current business models and huge data types, and thus emerged various types of analytic tools like Big Data, which make this work easier by way of proper organization of data. Big Data is all about analyzing different forms of data (Structured, Semi-structured and Un-structured) and it is not about the procedure, creation or consumption of data.

Data is important to any business, but it is of no value to the manager if all the work only involves collecting and reporting data without the ability to drive business decisions. Web tools like Yahoo web analytics and Google Analytics are good at answering the “what” questions, for example, “what happened on this page today?” or “What was the best marketing channel last year in terms of site revenue?” It cannot be denied that this information is valuable; however, successful business decisions cannot come from these analytics tools alone. They cannot offer the insights that an analytics expert can by digging through the maze of data to find meaningful information. In a word, the Web analytics tool is only useful when it is used by a certified analytics person. However, some companies fall into the trap of data. They are

Managing and analyzing big data is a huge task for all organizations of all sizes and across all industries. If a business’s plan to implement a data management tools there is a need for a more realistic way of capturing information about their customers, products, and services. Mining data is often in the terabytes and organizations need to be able to quickly analyze that data and then pull appropriate information needed to make managerial decisions. Further, with the insurgence of social media, smart devices and click-stream, data is generated daily on global networks through interactions. The use of data management technologies allow a company to interface unstructured data and structured data to gleam information that is usable for business managers to make sound business decisions, improve sales and to decrease operating costs. Big data integration and analysis has evolved for organizations to store, manage, and manipulate vast amounts of data then provide the appropriate information when it’s needed to meet business objectives.

Big Data and Analytics systems are fast emerging as one of the most critical system in an organization’s IT environment. But with such a huge amount of data, there come many performance challenges. If Big Data systems cannot be used to make or forecast critical business decisions, or provide insights into business values hidden under huge amounts of data at the right time, then these systems lose their relevance. This article talks about some of the critical performance considerations in a technology agnostic way. These should be read as generic guidelines, which can be used by any Big Data professional to ensure that the final system meets all performance requirements.

Big data analytics methods and techniques (which is the application of predictive methods, pattern recognition techniques, cluster analysis, and other quantitative and qualitative methods in big data sets) can

The importance of analytics in big and small companies could not be overestimated. Every organization uses big data in order to manage their processes and improve its business. The term of "analytics" is commonly defined as "the extensive use of data, statistical and quantitative analysis, explanatory and predictive models and fact-based management to drive predictive models" (Davenport & Harris, 2007). Analytics appears to be a useful tool, which helps to gain necessary information and provide extensive support to all departments in the enterprise. Nowadays analytical innovations play a big role on the market as they help an organization to gain a competitive advantage and become a market leader. According to the researcher Svetlana Sicular, analytics helps the companies to organize big data and divide all the information into relevant and irrelevant. In such a way organization receive an opportunity to process this data and deliver necessary information to a customer faster and more efficient. Furthermore, new tools and applications help to build up a reliable channel of communication between clients and a support team (Wagner, 2016). That is why almost all the companies are constantly trying to improve their analytical methods in order to attract more customers and investors to their business. The examples of such organization as Netflix Inc., Harrah 's Entertainment and Amazon show that analytics could help an enterprise to develop in its field and gain a strong

Data analytics has drastically changed how business operate on a day-by-day basis. It can make or break a business. It involves “exploring huge volumes of data to provide greater insight and intelligence, and doing so quickly.” (Efraim Turban. Linda Volonino. Gregory R. Wood., 2013) According to the Better Business Outcomes White Paper that was published by IBM, IBM observed that the planet was becoming more instrumented, interconnected, and intelligent about five years ago. Twenty thousand engagements later, though not it doesn’t say how many years later, IBM has gained critical knowledge of how big data analytics can improve conditions for organizations in nearly every industry. (Better business outcomes with IBM Big Data &

There is a lot of buzz around Big Data and the NOSQL movement these days and rightly so. The issues with data have essentially been two-fold: find cost effective ways to store ever increasing amounts of data and information, and find ways to mine this information to extract meaningful Business Intelligence.

Data itself is useless, until it is mined and transformed into a valuable source of knowledge discovery. Due to its conversion into useful information, data mining has become the leading source being used in many fields worldwide. “Data mining is based on complex algorithms that allow for the segmentation of data to identify patterns and trends, detect anomalies, and predict the probability of various situational outcomes.”[1] Many organizations from healthcare to multimedia and more are relaying on data and getting developed through the use of it. Regardless of how, data warehouse changed its rhythm and dimension in terms of measurements such as: variety, volume and velocity. Today, one can see the current trends of data mining in different fields such as social networks, healthcare and businesses. As data mining is giving the opportunity for those fields to get advanced, "Big Data" is also opening up new doors within itself as the new trends emerge.

Today, our society is bombarded by data driven business society. Data are facts and statistics collected together for reference and analysis. It can also be measurements, values, words, observations or even descriptions. Data has a lot of use. Companies can use data to gather information on how things work. It can even be used to differentiate things and find their similarities. Data is basically a tool for research purposes. Data became one of the prominent assets of the today’s business industry. From the traditional manual surveys and online surveys up to different types of data gathering methods, big companies are spending millions and millions of dollars just to improve their business through the use of data. Data became such