Big Data Analytics
Big Data Analytics involves the process of examining large and complex data sets to uncover patterns, correlations, and insights. It utilizes advanced algorithms and tools to extract valuable information from massive volumes of data, enabling organizations to make data-driven decisions and predictions. This field encompasses various techniques such as data mining, machine learning, and predictive analytics to derive meaningful insights from big data.
8 Key excerpts on "Big Data Analytics"
eBook - ePubBig Data Analytics Methods
Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing
- Peter Ghavami(Author)
- 2019(Publication Date)
- De Gruyter(Publisher)
...Big Data Analytics approaches do not mandate data to be clean and normalized. In fact, they make no assumption about data normalization. Data analytics may analyze many varieties of data to provide views into patterns and insights that are not humanly possible. Analytics methods are dynamic and provide dynamic and adaptive dashboards. They use advanced statistics, artificial intelligence techniques, machine learning, deep learning, feedback and natural language processing (NLP) to mine through the data. They detect patterns in data to provide new discovery and knowledge. The patterns have a geometric shape and these shapes as some data scientists believe, have mathematical representations that explain the relationships and associations between data elements. Unlike BI dashboards that are static and give snapshots of data, Big Data Analytics methods provide data exploration, visualization and adaptive models that are robust and immune to changes in data. The machine learning feature of advanced analytics models is able to learn from changes in data and adapt the model over time. While BI uses simple mathematical and descriptive statistics, Big Data Analytics is highly model-based. A data scientist builds models from data to show patterns and actionable insight...
eBook - ePubData Analytics, Computational Statistics, and Operations Research for Engineers
Methodologies and Applications
- Debabrata Samanta, SK Hafizul Islam, Naveen Chilamkurti, Mohammad Hammoudeh, Debabrata Samanta, SK Hafizul Islam, Naveen Chilamkurti, Mohammad Hammoudeh(Authors)
- 2022(Publication Date)
- CRC Press(Publisher)
...2019). 6.7 LATEST TREND IN BIG DATA It is a surprising fact that the data that we produce in two days is much higher than decades of history. Over the years, there has been a lot of transformations which have occurred, thereby making data science a prominent field. Further technological advancements will result in mass production of data. Following are a few trends observed in Big Data Analytics (Karuppiah et al. 2014). 6.7.1 I NFORMATION FROM D ATA A few years back, data stores were repository for data. While SaaS was widely used, Daas had just started. Moreover, SaaS-driven applications focused upon utilizing cloud technology which aids clients using the application with on-demand access privilege irrespective of the location. The greatest feature that Big Data Analytics provides is transforming raw data into information which can be interpreted. This helps develop businesses and share informative data among the industry (Tolan et al. 2015). 6.7.2 P REDICTIVE A NALYSIS Analysis of big data has been a pivotal factor which has helped companies to accomplish their agendas, thereby having greater success. They employ analytics tools to train big data and find out why issues arise. Predictive analytics plays a conducive role in analyzing data from various events which help in enhanced knowledge of customers leading to an accurate prediction of hazards the corporation is vulnerable to. Furthermore, analysis of big data helps in forecasting future happenings. This approach is extensively useful in analyzing customer’s responses efficiently (Karuppiah et al. 2015). 6.7.3 E DGE C OMPUTING The edge computing works on the principle of transferring multiple processes to a local system or server and running it on them. This helps in reducing distant connections which interlink servers and customers. Furthermore, edge computing involves real-time streaming of data and analyzing the data without being affected by latency difficulties. This, in turn, helps to respond much faster...
eBook - ePubBig Data Analytics
Turning Big Data into Big Money
- Frank J. Ohlhorst(Author)
- 2012(Publication Date)
- Wiley(Publisher)
...Although massive data sets have been created in just the last two years, Big Data has its roots in the scientific and medical communities, where the complex analysis of massive amounts of data has been done for drug development, physics modeling, and other forms of research, all of which involve large data sets. Yet it is these very roots of the concept that have changed what Big Data has come to be. THE ARRIVAL OF ANALYTICS As analytics and research were applied to large data sets, scientists came to the conclusion that more is better—in this case, more data, more analysis, and more results. Researchers started to incorporate related data sets, unstructured data, archival data, and real-time data into the process, which in turn gave birth to what we now call Big Data. In the business world, Big Data is all about opportunity. According to IBM, every day we create 2.5 quintillion (2.5 × 10 18) bytes of data, so much that 90 percent of the data in the world today has been created in the last two years. These data come from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos posted online, transaction records of online purchases, and cell phone GPS signals, to name just a few. That is the catalyst for Big Data, along with the more important fact that all of these data have intrinsic value that can be extrapolated using analytics, algorithms, and other techniques. Big Data has already proved its importance and value in several areas. Organizations such as the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), several pharmaceutical companies, and numerous energy companies have amassed huge amounts of data and now leverage Big Data technologies on a daily basis to extract value from them. NOAA uses Big Data approaches to aid in climate, ecosystem, weather, and commercial research, while NASA uses Big Data for aeronautical and other research...
eBook - ePubHandbook of Research for Big Data
Concepts and Techniques
- Brojo Kishore Mishra, Vivek Kumar, Sanjaya Kumar Panda, Prayag Tiwari(Authors)
- 2022(Publication Date)
- Apple Academic Press(Publisher)
...With improvement in artificial intelligence and its shift towards an autonomous, heuristic approach towards learning forays, deep learning is one of the newest techniques introduced in artificial intelligence and subsequently inducted in big data science, whose framework for operations on big data is elucidated. With an ever-increasing amount of data being generated in the contemporary scenarios, Big Data Analytics is a growing necessity than a commodity. With applications ranging from commercial implementations and business analytics to scientific research and medical applications, big data has been a major influence on how data is perceived and processed in the recent times. With a potential to expand its processing power so as to encompass the growing data generations and the inferential mechanism to crunch it in the future, big data is evolving in its structural and functional frameworks, serving the vision for the future of data analytics. KEYWORDS artificial intelligence big data big data mining big data modeling data classification algorithms data integration and labeling data processing data retrieval deep learning machine learning search types REFERENCES LaValle, S., Eric, L., Rebecca, S., Michael, S. H., & Nina, K., (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52 (2), 21–32. De, M. A., Marco, G., & Michele, G., (2015). What is Big Data? A Consensual Definition and A Review of Key Research Topics. doi: 10.1063/1.4907823. Jean-Louis, M., (2016). Data value, Big Data Analytics, and decision-making. Journal of the Knowledge Economy. doi: 10.1007/s13132-016-0396-2. Kambatla, K., Giorgos, K., Vipin, K., & Ananth, G., (2014). Trends in Big Data Analytics. Journal of Parallel and Distributed Computing, 74 (7), 2561–2573. Sharma, S., Udoyara, S. T., Johnny, W., Shashi, G., & Subhash, S., (2014). A brief review on leading big data models. Data Science Journal, 14 –41. Chandarana, P., & Vijayalakshmi, M., (2014)...
eBook - ePubThe Organisation of Tomorrow
How AI, blockchain and analytics turn your business into a data organisation
- Mark Van Rijmenam(Author)
- 2019(Publication Date)
- Routledge(Publisher)
...Once a security threat is detected, you should focus on the response to minimise the possible damage. Security analytics is a difficult field, which involves large volumes of data, smart algorithms, and extensive encryptions. The brightest minds and/or smart software tools should be used. It should be on top of the agenda for every company. For many organisations, it will be an expensive investment to make, but not doing it could turn out to be a lot more expensive. 3.7 Conclusion Big data has often been coined the next “management revolution”, the Fourth Industrial Revolution, or “the next frontier for innovation, competition, and productivity”. 34, 170, 238, While only a few years ago, organisations were still struggling to understand the impact of these trends on their business, big data has now emerged as the corporate standard. 167 Big Data Analytics affects all organisations, big or small, has an impact on every industry around the globe, and is a key characteristic of the organisation of tomorrow. 164 Especially in these ambiguous and uncertain times, analytics enables organisations to sense opportunities. Using large amounts of structured and unstructured data and applying it to advanced analytics enables organisations to understand their environment and seize opportunities, which enables them to remain competitive. Data analytics, when conceptualised as dynamic capabilities, can help to interpret the business environment, enable managers to act, and result in sustained superior performance and competitive advantage. Therefore, the introduction of descriptive, predictive, and prescriptive analytics means that the traditional way of decision-making, based on experience and expertise, is exchanged for data-driven decision-making. When organisations provide more people with access to knowledge, power is distributed more equally, enabling employee empowerment within an organisation...
eBook - ePubCreating Value with Data Analytics in Marketing
Mastering Data Science
- Peter C. Verhoef, Edwin Kooge, Natasha Walk, Jaap E. Wieringa(Authors)
- 2021(Publication Date)
- Routledge(Publisher)
...CHAPTER 7 Data analytics DOI: 10.4324/9781003011163-7 7.1 Introduction Analytics is a major element of creating value from data. Statistical analytics of marketing data have been around for decades. The revolutions in scanner data and customer relationship management (CRM) have considerably increased the importance of analytics in marketing: it creates a strong market and customer insights and models that can be used for decision support, campaigns, and data-driven solutions. However, the emerging presence of big data and AI is changing analytics. Taking a more historical lens, we can observe certain developments in analytics. First, we will discuss the different strategies for analyzing data. Subsequently, we describe the role of analytics and general types of marketing analysis. We end with an understanding of the meaning of data science, Artificial Intelligence, Machine Learning en Deep Learning. This is followed by a discussion on how big data and AI is changing the working field of analytics. In this chapter we do not discuss details of specific analytical techniques—we do that in the in-depth Chapters 8 and 9. We then discuss how to have a greater impact with analytical results, through story-telling and visualization, in Chapter 10. 7.2 The power of analytics In an era of big data, firms heavily rely on the analytical function. Davenport and Harris (2007) argue that firms can gain a competitive advantage if they build up strong and effective analytical capabilities: “Analytics is then 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 & Harris, 2007). These analytical capabilities can be used in different kinds of functions, such as human resource management, logistics, finance, and marketing...
eBook - ePubEssentials of Pricing Analytics
Tools and Implementation with Excel
- Erik Haugom(Author)
- 2020(Publication Date)
- Routledge(Publisher)
...Chapter 12 Big Data and pricing analytics Big Data has become one of the hottest buzzwords over the last years. Figure 12.1 illustrates this by displaying the popularity of the terms “Big Data” and “Big Data Analytics” in Google searches from 2004 to 2020. It is probably not an exaggeration to state that its development has been massive, starting first and foremost in 2011. In 2012, which could be labeled the early days of the “Age of Big Data,” Steve Lohr described the future as being very bright for those who are good with numbers and are fascinated by data: “The sound you hear is opportunity knocking,” he wrote in the introduction of a New York Times article (Lohr, 2012). Even though some of the hype may have overestimated these opportunities, there is no doubt that the enormous amount of data becoming available each day, hour, minute, second, or even hundredth of a second, can be used to create business value if utilized correctly. In this chapter we shall focus on what Big Data is and how it can be used in combination with pricing analytics to create business value. We will also examine its applications in various industry sectors, introduce you to specific data analytics techniques that can be useful in pricing analytics, and go through a relevant case from real life. The chapter will also focus on how Excel can be used efficiently for pricing analytics when working with big data sets. Figure 12.1 Google searches for terms related to Big Data and Big Data Analytics from 2004 to 2020 (as of March 23, 2020). Source: trends.google.com. 12.1 What is Big Data? Laney (2001) proposed using three Vs to characterize the key features of Big Data. These Vs stand for Volume, Velocity, and Variety, and are now included in most definitions of the term...
eBook - ePubIt's All Analytics!
The Foundations of Al, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government
- Scott Burk, Gary D. Miner(Authors)
- 2020(Publication Date)
- Productivity Press(Publisher)
...Users needed to access the data, and analyze and model the data more expediently. From an analytics and data science perspective, there was a major technology and practice shift when adopting Big Data technology. Traditionally, most analysis and machine learning involved moving data from large data repositories (and most of these distributed) into a single “sandbox” for analytics. This involved increasingly more time as the volume of the data increased; it was taking more and more time to move the data across the wire. It also was a security risk since you were moving this data across a network, thus making it easier for someone to tap into sensitive data. You were also creating a duplicate copy of all this data on another server or servers, which increased costs and added additional security risks. What if you could instead move the algorithms (machine learning and other) to where the data lives in the first place? Move the algorithms instead of the data? This was the brilliance of big data technology. We will call this in-cluster, in-database or in-memory machine learning for short. A cluster refers to a group of servers that are grouped together to work on the same computational set of problems and can be viewed as one computer resource. Our examples will focus on Hadoop and Spark, two open source technologies available as part of the Apache (see gray box on “Apache, Hadoop, and Spark”) scalable in-database and in-cluster processing. For in-cluster, in-database computing, we are referring to moving the algorithms to the cluster where the data lives (Disk, Hadoop HDFS, and MapReduce). In-memory computing refers to moving the algorithms to faster, volatile/RAM, which is much faster (Spark). We address near-memory computing in the “Other Important Data Focuses of Today and Tomorrow” section of this chapter. The Hype of Big Data For many years “big data” was the rage, it was a major hype cycle (see gray box, “Big Data and the Gartner Hype Cycle”)...
