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Business Intelligence and Big Data

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Table of Contents What is Big Data? 3 How is it adding value to organizations? 3 Importance of value chain in Big Data 4 Current Trends in Big Data across the Globe and in India 4 Growing Data: 5 Growth by Region 6 Service Providers for Big Data 8 Growth Prospects in Big Data 9 How is the Big Data useful for Industries 10 Measuring Value of Big Data 14 Key Challenges in Big Data 14 What is Big Data? According to Forbes – Big Data is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis. According to Gartner – Big Data is defined as high volume, velocity and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making. According to IBM – Big Data is more than simply a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond reach. According to Mckinsey – Big Data refers to data sets whose size is beyond the ability of typical database software tools to capture, store, manage and analyse. How is it adding value to organizations? Organizations are beginning to use Big Data to gain competitive advantage by enhancing business, predict consumer and buying behaviour and reducing cost by optimizing the operational processes. As opposed to more traditional business intelligence systems, Big Data techniques allow institutions to analyse data for patterns more quickly and at a much lower cost. More accurate analysis leads to more confident decision making which leads to greater operational efficiencies, ... ... middle of paper ... ...o analyze sales trends to keep its manufacturing efficient. Benefits of Big Data: a. To detect product defects and boost quality b. To improve supply planning c. Better detection of defects Measuring Value of Big Data Measuring the value of Big Data initiatives keeps the organizations focused and aligned with the needs of the enterprise. Without objective driven approach, organization may end up spending more than expected to achieve desired goals. Key Challenges in Big Data We can classify the challenges in three key strategic and operational areas: 1. Information Strategy a. Analyze which investments will deliver the most business value and ROI. b. By 2015, Gartner predicts that 4.4 million jobs will be created around big data. There will be shortage of skilled resources. 2. Data Analytics 3. Enterprise Information Management

In this essay, the author

  • Explains that organizations create enormous amounts of data, but lack the ability to process the data real-time, so they can't predict and respond to the changing needs of the market.
  • Explains that the exponential increase of big data ‘news headlines’ from 2012 gives ample justification for the trend.
  • Explains that data is growing at a 40% cagr and will reach 40 zetta bytes by 2020.
  • Estimates future data production will be 7 times in 2020 compared to 2014.
  • Explains emerging markets are focusing on investing in big data technologies and extracting insights to get inroads in the market.
  • Explains that more than 50% of analytics companies are it/ites based followed by consulting, research and media & advertising.
  • Describes wipro technologies' 4 key factors which drive the growth of big data.
  • Explains the increasing availability of data, including semi-structured and unstructure, through social media, blogs, etc., which can be analysed for inferences.
  • Explains that the economic recession forced companies to have closer look at the operational cost and cost optimization in all aspects of the business. big data analytics will help companies optimize cost by taking informed decisions.
  • Explains that the availability of newer and better mobile devices and other data capture devices has benefited organizations to get real-time data of consumers.
  • Explains how big data analytics is transforming industries. according to gartner, 64% of the organizations have deployed or launched a big data initiative in 2013.
  • Describes the benefits of using big data.
  • Explains how hdfc bank collects cash withdrawal patterns, credit card usage, search preferences, and customized plans for savings, insurance and home loan products.
  • Explains that retailers need to use big data analysis to increase revenue and operate more efficiently.
  • Explains how to reduce costs and increase efficiency of the supply chain by aligning order, production, and delivery schedules to consumer demand.
  • Explains that communications service providers (csps) are investing in big data to gather actionable insights.
  • Recommends developing meaningful customer segmentation for better sales and support effectiveness.
  • Explains that vodafone india generates petabytes of data from 23 different circles and uses big data to improve decision cycle times to respond to business and customer queries.
  • Explains that the company's adoption of analytics has enabled its shift to lean manufacturing, determining which products and processes should be scrapped. big data investments analyze sales trends to keep manufacturing efficient.
  • Opines that measuring the value of big data initiatives keeps organizations focused and aligned with the needs of the enterprise. without an objective driven approach, organizations may end up spending more than expected to achieve desired goals.
  • Opines that gartner predicts that 4.4 million jobs will be created around big data by 2015.
  • Explains that big data is a collection of data from traditional and digital sources inside and outside your company. according to gartner, it requires cost-effective, innovative forms of information processing for enhanced insight and decision making.
  • Explains that organizations are beginning to use big data to gain competitive advantage by enhancing business, predicting consumer and buying behaviour, and reducing cost by optimizing the operational processes.
  • Forecasts the global big data market to grow at a cagr of 34.17 percent over the period 2013-2018.
  • Explains that sas, ibm, and statsoft are the leaders in the big data implementation space. the indian analytics market is $375 million and is expected to grow to $1.5 billion by 2015.
  • Explains how big data can help automakers maximize profitability and revenue in the demanding market where customers are more educated.
  • Explains how the economic downturn, new competitive challenges and demographic shifts are all provoking many changes in the insurance business.
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    • Explains that big data is regarded as one of the most popular emerging technologies. it is not just the quantity, but the speed in which this data is being collected.
    • Explains that big data cannot be accommodated and operated by existing systems because of its massive volume.
    • Explains that the speed at which data is being captured and read contributes to the complexity of big data.
    • Explains that the big data industry is estimated to be worth between 30 billion to 200 billion dollars. the major growth drivers are smartphones, tablets, social networking, sensors, online games and mobile payments.
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    • Explains that proprietary databases are now paving the way to open source frameworks such as hadoop.
    • Explains that many corporations realizing the importance of data storage and retrieval are opting for cloud services.
    • Explains that there have been significant improvements in the disciplines of predictive analytics and data mining to support big data analytics.
    • Opines that while some people advocate big data as a potential solution, others like the human rights commission contradict this notion. data is often analyzed using statistical and mathematical models, subject to simplifications and approximations.
    • Opines that any new technology could be evaluated only with the utilization and effectiveness over the long run. it is recommended to look through all the hype and derive decisions with a sense of caution and sensitivity.
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    In this essay, the author

    • Explains that our everyday life has changed forever thanks to the ubiquitous smart phones and technology dependent information age. the government estimates that there are 250 billion dvds of electronic data generated each year by everything from underground physics experiments to retail transactions.
    • Explains that big data is a large stream of structured, semi-structured and unstructure
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    • Opines that big data has played a critical role in an array of sectors. mackenzie consulting predicts that there will be shortage of skilled data scientists by 2018 and more than 1.5 million managers who ask the right questions about data.
    • Explains that there are about 20 university programs offering graduate degrees in data science or big data analytics. the applied information technology department and the school of physics, astronomy, and computational sciences have yet to accept new students.
    • Explains that moocs offer new opportunities for areas where a skill gap needs to be met. the university of washington in collaboration with coursera offered an introduction to data science class.
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    • Cites mervis, j. agencies rally to tackle big data. marx, vivien biology: the big challenges of big data.
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    • Explains that big data is a concept that has existed for some time, but only gained proper attention in recent years. it can be used by enterprises in building new applications, improving effectiveness, lowering the costs of their applications and increasing customer loyalty.
    • Explains that the first big data challenge came in the form of the 1880 u.s. census, when the information concerning approximately 50 million people had to be gathered, classified, and reported.
    • Explains the 7 v's of big data: volume, velocity, variety, veracity, variability, visualization, and value.
    • Explains that the definition of big data is changing rapidly, so purity of the information is critical to its value. visualization makes data comprehensiveness in a manner that is easy to read and understand.
    • Distinguishes between structured and unstructured data sources that contain critical information, and explains that structured data is easiest to work with because it is already defined, indexed, making access and filtering easier.
    • Explains the processes that are included in big data, including traditional business intelligence, data mining, statistical applications, predictive analysis, and data modeling.
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    • Explains how companies like facebook, amazon, and google are relying on big data analytics as part of their primary marketing strategies and a means to serve their customers better.
    • Explains that big data analytics is able to break down and understand trends and explain the purchasing process by simply looking at what they have purchased in the past.
    • Explains that big data has many advantages, but there are obstacles that hinder its use. gathering the data is usually half the battle in the analytics game.
    • Explains that analyzing large data sets has become a key basic of competition, causing new waves of productivity, and consumer surplus. business leaders in every sector will have to deal with the implications of big data, either directly or indirectly.
    • Explains that big data will have a gigantic impact on society, the way organizations are managed and operated, government is organized, and eventually, how the global economy functions.
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  • Big Data
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    • Explains that big data is a term used to refer to extremely large and complex data sets that have grown beyond the ability to manage and analyse them with traditional data processing tools.
    • Explains that on twitter has 500 million new tweet every day, there are 1.15 billion facebook members create a giant mess of data text, files, video...
    • Explains that there are two main directions criticized for big data, one is about how people use it and the other is related to the conduct of it.
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    • Opines that big data challenges organizations and enterprises in today's digital age.
    • Explains that big data is a collection of data capacity in excess of assumed applications and traditional tools.
    • Explains that intel creates 1 petabyte of data every 11 seconds, which is equivalent to a 13 year long hd video.
    • Explains that big data is growing demand so much that software ag, oracle, ibm, microsoft, sap, emc, hp and dell has spent more than $ 15 billion for the company specializes in managing and analyzing data.
    • Explains the benefits of big data, such as cutting costs, reducing time, increasing development time and optimizing products, and supporting people to make the right decision.
    • Argues that big data needs to be contextualised in the context of social, economic and political issues.
    • Erik swan, co-founder and chief technology officer of spunk company, predicts that the change in big data is the attitude of people towards it.
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  • Job Analysis And Job Analysis: Job Description
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    In order to eradicate the fear of “big data”, there should be training programs in place that would explain this concept further. Through these trainings, it would be ideal if the link between conceptual advantages and organizational success is made, so that employees will have the mindset that this is a concept that would make data integration and management tasks more accessible and manageable. All in all, the Internet is a massive medium with an infinitive amount of information that is readily accessible at one’s leisure. If used appropriately and effectively, one would be able to maximize its full

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    • Explains that before a job is created, an analysis must be conducted to provide the employer and employees with the significant aspects that are attributed to the creation of the respective job.
    • Explains that a job description is the foundation to employee and employer success.
    • Explains the importance of having a person specification for each job because each has its own desired outcome (performance).
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  • Big Data Essay
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    • Explains that big data is a collection of data from traditional and digital sources that exceeds the processing capacity of conventional database systems.
    • Explains that unstructured data isn't organized or easily interpreted by traditional databases or data models and is usually in text format.
    • Explains that the swot analysis involves four steps: strength, weakness, opportunity, and threats. the first category examines the strengths of big data.
    • Explains the four v's of big data, namely volume, velocity, and variety, which helps understand the nature of data and the software platforms available to exploit them.
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    Big Data and the Reality of a New Digital Age Advances in new technology have come to define the modern world; super-efficient cars, computers capable of blistering speeds, and smartphones with access to any information in an instant are now a part of normal life in the 21st century. With all this new technology, new problems arise that we have no experience solving. Big Data usage has become a very controversial topic within America and around the world. Big Data is defined as, “The collection and analysis of enormous amounts of data by super computers, often in real time” (Price). The use of big data by the Government and corporations has many positive uses, but it is at the cost of our private information; at what point do the pro’s outweigh the con’s, and how does privacy play a role in this modern age of big data?

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    • Opines that the government needs to regulate big data and be transparent with their policies in order to create a solution for the issues at stake.
    • Explains how big data is being used by large corporations and the government for many practical uses, such as tracking crime, finding terrorists, and redefining shopping.
    • Explains how the nsa and police use big data to catch criminals and predict and prevent crimes. cameras can identify faces in an immense crowd within a short period of time.
    • Explains that while the government has privacy restrictions due to multiple laws, commercial use remains unregulated meaning we may see a large jump of commercial uses in the years to come.
    • Opines that modern commercialism and justice enforcement could soon be redefined through technological advances if allowed by the public.
    • Opines that big data is good, but that it must be accessed in order for it to be successful.
    • Analyzes the controversy generated by big data use by the government. the usa patriot act allows legal monitoring and spying on citizens; the electronic communication privacy act permits email access without warrant; and the proposed cyber intelligence sharing and protection act.
    • Opines that the government needs to create new policies restricting big data usage for themselves and for commercial use.
    • Opines that big data can revolutionize crime prevention and big business, but it will only cause controversy without regulation.
    • Opines that big data will have a profound effect upon the modern world, and it will affect young people more severely than older people.
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  • Big Data Case Study
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    1. Five Key Factors in Driving Big Data Project Success by Pearl Zhu This article says about five key factors that should be considered for the success of a Big Data Project. The five key factors according to the authors are 1.

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    • Describes five key factors that should be considered for the success of a big data project.
    • Opines that the organization should have the right and talented team in place to make use of the project insights. converting big data into understandable, actionable and digestible information is the major business purpose from big data project.
    • Opines that big data analytics must properly and efficiently accommodate the real state of the data. by the time a project gets going, around 30% more data will be available.
    • Explains that big data projects have a lot of pitfalls because the older users of the old systems are not very well equipped with the new concepts that are being developed.
    • Opines that talent is important, and that the processes should be collaborated effectively with the right and efficient talent available in the organization.
    • Summarizes the article's three must-dos for a big data project.
    • Explains how agile approaches reduce project faults while projects are in progress.
    • Opines that building a center of cross-pollinate expertise and competency among experts in business data scientists, and data engineers can help the big data projects.
    • Explains the seven deadly sins of big data users in the article.
    • Explains that security video has grown up with more cameras inside and out, a shift to ip, integrated video into other security and business systems, and more analytics and intelligence at the edge.
    • Explains that the life cycle of big data means evaluation of capacity planning of services provided by big data through properly timed intervals and expected cycles of growth.
    • Explains that 's research paper focuses on two major challenges faced by system operators and designers: fusing information from multiple autonomous, yet related sources and respecting external constraints.
    • Explains that big data is becoming increasingly pervasive, and enterprises should find efficient ways to derive more benefits from it.
    • Explains that bigtable is a distributed storage system for structured data, which has been designed to scale to large data sizes across servers.
    • Explains that managing the change that occurs with the arrival of big data in to the organization is an important step that should be done.
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  • B2B
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    Big Data not only involves collection of large volumes of data but also involves the aspect of harnessing relevant data and analyzing it to enable

    In this essay, the author

    • Explains that b2b marketers have more recently adopted and formed synergies with technological tools to improve the efficiency and productivity of their businesses.
    • Explains cloud computing, which utilizes computers connected through a communication network such as the internet.
    • Explains that both microsoft project (msp) and primavera are used for project management and project planning.
    • Explains marketing automation is an extension of customer relationship management (crm) and it focuses on segmentation, definition, tracking, and scheduling of marketing activities.
    • Explains that big data involves collecting large volumes of data, harnessing relevant data and analyzing it to enable analysis.
    • Explains that while both b2b and b1c have certain similarities, there are a few key differences between the two. business jargons can be used effectively, but simpler buzzwords are more appreciated.
    • Explains that expertize and efficiency might be the driving factors for a b2b purchase, whereas the purpose of a2c purchase may be entertainment.
    • Explains that businesses can connect to applications and data from any web-enabled device for doing business around the globe. cost-effectiveness is a one-time investment and improves efficiency in the long run.
    • Explains that erp software applications can be used to manage parts purchasing, tracking orders, interacting with suppliers, product planning, inventories, and providing customer service.
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  • Google's Bigdata Revolutions to Change the World
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    In today, we can see video in yutube, we can search way to go somewhere by using google map, and also we can send a mail by g-mail in our smart phone. What’s common thing in those things? Yes, they are related to google, the one of the most famous search engine in the world.There are many other things that google can provide with to us. How can google provide such many things? The answer is bigdata. Google have so much data because they are one of the most famous search engine. And they also have technology that can analyze complex data such as video , contents in text. By those things, google could go first in bigdata innovation. At first, google’s service such as free maps, buying yutube was seen as useless things to make money. however, as google can use datas on those things, those useless things are the important points in bigdata innovation. From now on, we see how google use that datas so interestingly., and practically, and how they improve their data platform and also they make people utilize big data by their ‘big query’. And what we can learn about from their innovative activity in bigdata era.

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    • Explains that google do efforts to collect more datas from the world. project loon is a research and development project that provides internet access to rural and remote areas.
    • Explains that google has a lot of data and technology that can analyze complex data such as video and text.
    • Explains how google uses bigdata to change lives of people. google's "google translate" is a statistical system that provides information about where flue be spread, and predicts latent patient.
    • Argues that google is the most innovative bigdata utilizing enterprise, and they are the best giver to help other people use big data.
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