...consumers, and societies leave behind massive amounts of data as a by-product of their activities. Leading-edge companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to test hypotheses. This course prepares students to understand structured data and business analytics and become leaders in these areas in business organizations. This course teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, algorithms, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing students to apply business analytics algorithms and methodologies to real-world business datasets from finance, marketing, and operations. The use of real-world examples and cases places business analytics techniques in context and teaches students how to avoid the common pitfalls, emphasizing the importance of applying proper business analytics techniques. In addition to cases, this course features hands-on experiences with data collection using Python programs and analytics software such as SAS Enterprise Guide. Throughout the semester, each team works to frame a variety of business issues as an analytics problem, analyze data provided by the company, and generate applicable business...
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...Challenges and Opportunities with Big Data A community white paper developed by leading researchers across the United States Executive Summary The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization. Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep and what to discard, and how to store what we keep reliably with the right metadata. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search: transforming such content into a structured format for later analysis is a major challenge. The value of data explodes when it can be linked with other data, thus data integration is a major creator of value. Since most data is directly generated in digital format today, we have the opportunity and the challenge both to influence the creation to facilitate...
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...Big Data and Data Analytics for Managers Q1. What is meant by Big Data? How is it characterized? Give examples of Big Data. Ans. Big data applies to information that can’t be processed or analysed using traditional processes or tools or software techniques. The data which is massive in volume and can be both structured or unstructured data. Though, it is a bit challenging for enterprises to handle such huge amount fast moving data or one which exceeds the current processing capacity, still there lies a great potential to help companies to take faster and intelligent decisions and improve operations. There are three characteristics that define big data, which are: 1. Volume 2. Velocity 3. Variety * Volume: The volume of data under analysis is large. Many factors contribute to the increase in data volume, for example, * Transaction-based data stored through the years. * Unstructured data streaming in social media. * Such data are bank data (details of the bank account holders) or data in e-commerce wherein customers data is required for a transaction. Earlier there used to data storage issues, but with big data analytics this problem has been solved. Big data stores data in clusters across machines, also helping the user on how to access and analyse that data. * Velocity: Data is streaming in at unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with...
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...Mansour Big Data and Analytics Developer at OMS ahmedelmasry_60311@hotmail.com Summary Working in Big Data & Analytics (2014 - Present). Working in Business Intelligence (IBM Cognos) (2013 - Present). Working in ERP & Data manipulation (Oracle & Asp.net) (2011 - 2013). Skills (Pivotal HD (Hadoop),Oracle, Sql Server, MongoDB, Asp.net, JavaScript, Node.js, C#). Training (Pivotal HD Hadoop training). Master's Degree in Informatics at Nile University (2014-2016) Graduated from Faculty of Science, Cairo University (2011). Awarded (YIA) The Young Innovator Award (2010). Experience Big Data and Analytics Developer at OMS April 2015 - Present (1 month) Developing and analysis Big Data using Hadoop framework (Pivotal HD & Hawq), Hadoop Eco-System Co-Founder and Data Analyst at AlliSootak September 2010 - Present (4 years 8 months) Developing and Researcher Senior Software Developer at Fifth Dimension (5d) October 2014 - April 2015 (7 months) Senior Software Developer at Bizware August 2013 - October 2014 (1 year 3 months) Developing 2 recommendations available upon request Director of Special Projects at CIT Support May 2012 - January 2014 (1 year 9 months) Ensure that the client's requirements are met, the project is completed on time and within budget and that everyone else is doing their job properly. Senior Software Developer at I-Axiom Cloud ERP Solutions November 2011 - August 2013 (1 year 10 months) Developing Certifications The Data Scientist’s...
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...enables sustainable advantage, Matthew A and Stanley E (2013). If we observe carefully, huge amount of data is getting generated at each and every stages of the supply chain. In today’s digital world we are generating around 200 Exabyte of data each year, Silva R, Bogdan F and Marcin R (2013). Organizations are increasingly questioning their own ability to realize full potential from the huge amount of data they have within their supply chain, Steve...
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...REVOLUTION ANALYTICS WHITE PAPER Advanced ‘Big Data’ Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge. Second, Big Analytics refers to the sophistication of the model itself. Increasingly, analysis algorithms are provided directly by database management system (DBMS) vendors. To pull away from the pack, companies must go well beyond what is provided and innovate by using newer, more sophisticated statistical analysis. Revolution Analytics addresses both of these opportunities in Big Analytics while supporting the following objectives for working with Big Data Analytics: 1. 2. 3. 4. Avoid sampling / aggregation; Reduce data movement and replication; Bring the analytics as close as possible to the data and; Optimize computation speed. First, Revolution Analytics delivers optimized statistical algorithms for the three primary data management paradigms being employed to address...
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...Spotlight on Making Your Company Data-Friendly Spotlight 64 Harvard Business Review December 2013 Artwork Chad Hagen Nonsensical Infographic No. 5 2009, digital hbr.org Analytics 3.0 In the new era, big data will power consumer products and services. by Thomas H. Davenport T hose of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case. Some of us now perceive another shift, fundamental and farreaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis December 2013 Harvard Business Review 65 Spotlight on Making Your Company Data-Friendly methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy. I’ll develop this argument in what follows, making...
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...Top Data Management Terms to Know Fifteen essential definitions you need to know Fifteen Essential Data Management Terms We know it’s not always easy to keep up-to-date Contents with the latest data management terms. That’s why we have put together the top fifteen terms and definitions that you and your peers need to know. OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the...
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...Disruptive Innovation: A new era of Crowdsourced Data Analytics! Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. Abstract: The existing business paradigm of data analytics is set for a transformation. Today, companies are experimenting to replicate the “Outsourced data analytics” model to “Crowdsourced data analytics”. Companies like Kaggle, Crowdanalytix and others are hitting the headlines of top analytics blogs across the globe. The reason is that the new business model promises a drastic decrease in the cost of analytics for companies long with the flexibility to get the problem solved anytime with much less effort. In short, it’s not just crowdsourcing that is the novelty of the concept, but the manner in which it is put to use that steals the show. General Management General Management MBA Core, 2nd Year MBA Core, 2nd Year Ayush Malhotra NMIMS,Mumbai Ayush Malhotra ...
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...04-19-2015 The New Frontier: Data Analytics (Course title: Info System Decision Making) Professor: Clifton Howell Student: Deep Ajabani Data analysis is the process of finding the right data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to have the biggest possible impact. Analytics have been used in business since the management exercises were put into place by Frederick Winslow Taylor in the late 19th century. Henry Ford measured the time of each component in his newly established assembly line. But analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics have changed and formed with the development of enterprise resource planning (ERP) systems, data warehouses, and a large number of other software tools and processes. In later years the business analytics have exploded with the introduction to computers. This change has brought analytics to a whole new level and has made the possibilities endless. As far as analytics has come in history, and what the current field of analytics is today many people would never think that analytics started in the early 1900s with Mr. Ford. We are going to have a look on Big Data Analytics. Let’s have a look on advantages of big data analytics. It helps marketing companies build models based on historical data to predict who will respond...
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...4. 4.1 Big Data Introduction In 2004, Wal-Mart claimed to have the largest data warehouse with 500 terabytes storage (equivalent to 50 printed collections of the US Library of Congress). In 2009, eBay storage amounted to eight petabytes (think of 104 years of HD-TV video). Two years later, the Yahoo warehouse totalled 170 petabytes1 (8.5 times of all hard disk drives created in 1995)2. Since the rise of digitisation, enterprises from various verticals have amassed burgeoning amounts of digital data, capturing trillions of bytes of information about their customers, suppliers and operations. Data volume is also growing exponentially due to the explosion of machine-generated data (data records, web-log files, sensor data) and from growing human engagement within the social networks. The growth of data will never stop. According to the 2011 IDC Digital Universe Study, 130 exabytes of data were created and stored in 2005. The amount grew to 1,227 exabytes in 2010 and is projected to grow at 45.2% to 7,910 exabytes in 2015.3 The growth of data constitutes the “Big Data” phenomenon – a technological phenomenon brought about by the rapid rate of data growth and parallel advancements in technology that have given rise to an ecosystem of software and hardware products that are enabling users to analyse this data to produce new and more granular levels of insight. Figure 1: A decade of Digital Universe Growth: Storage in Exabytes Error! Reference source not found.3 1 ...
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...Business Analytic System enables the organizations to operate more efficiently, discover new insights fast, predict the business atmosphere quickly and help executive managers to make better decisions. There is no doubt that the created value is increasing through the big data and business analyst. However, to answer the question “does increasing analytical capabilities change the business radically or just in a manner of incremental improvement”, divergent views have been held by as regards the academic researchers, and as regards the leading practitioners. In my option, I agree with both of them, because they portray the opportunities or practical challenges the IT-enable system facing. The academic view is supported by Aral and Mithas. Aral believe the nano-data on human behavior rather than data volume itself is the key to create value. He suggests that data scientists should take a good understanding of multi-discipline, like human behavior, biology, and economics. Apple, the most creative company in the world, shows the ambition of dealing with big data. Apple Watch, the new product, keeps track of user’s body information, how much food you take, how much you walk, how many hours you sleep. With the help of experts in health or other domain, generated data can make immense economic income. Another example is Nike Company. Rather than a famous sport band company, Nike+ makes it more like a technology company. Nike+ enables users to track their activities, set personal goals...
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...Big Data and its Effects on Society Kayla Seifert MGT-311 November 23, 2015 Big Data is a concept that has existed for a while but only gained proper attention a couple of years ago. One can describe Big Data as extremely large data sets that have grown so big that it becomes almost impossible to manage and analyze with traditional data processing tools. Enterprises can use Big Data by building new applications, improving the effectiveness, lowering the costs of their applications, helping with competitive advantage, and increasing customer loyalty. It can also be used in other industries to enable a better system and better decision-making. Big Data has become a valuable asset to everyone around the world and continues to impact society today. The ideology of Big Data first came up in the days before the age of computers, when unstructured data were the norm and analytics was in its infancy. The first Big Data challenge came in the form of the 1880 U.S. census when the information involving about 50 million people being gathered, classified, and reported. This census contained a lot of facts to deal with, however, limited technology was available to organize and manage it. It took over seven years to manually put the data into tables and report on the data. Thanks to Big Data, the 1890 census could be placed on punch cards that could hold about 80 variables. Instead of seven years, the analysis of the data only took six weeks. Big Data allowed the government...
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...Big Data is Scaling BI and Analytics How the information surge is changing the way organizations use business intelligence and analytics Information Management Magazine, Sept/Oct 2011 Shawn Rogers Like what you see? Click here to sign up for Information Management's daily newsletter to get the latest news, trends, commentary and more. The explosive growth in the amount of data created in the world continues to accelerate and surprise us in terms of sheer volume, though experts could see the signposts along the way. Gordon Moore, co-founder of Intel and the namesake of Moore's law, first forecast that the number of transistors that could be placed on an integrated circuit would double year over year. Since 1965, this "doubling principle" has been applied to many areas of computing and has more often than not been proven correct. When applied to data, not even Moore's law seems to keep pace with the exponential growth of the past several years. Recent IDC research on digital data indicates that in 2010, the amount of digital information in the world reached beyond a zettabyte in size. That's one trillion gigabytes of information. To put that in perspective, a blogger at Cisco Systems noted that a zettabyte is roughly the size of 125 billion 8GB iPods fully loaded. Advertisement As the overall digital universe has expanded, so has the world of enterprise data. The good news for data management professionals is that our working data won't reach zettabyte scale for some...
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...of Data analytics and Sustainable Supply Chain for Acme Mexico City. Introduction Acme Mexico City is facing a major decision with finding the appropriate number of employees that will be needed on the floor during any given point during the day to meet the needs of the customer and creating a supply chain that will meet the demands. Body Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements. (Technopedia) By adding data analytics to Acme Mexico City is would give them a competitive advantage and achieve an insights into consumer shopping patterns that directly affect the business. Data analytics would help facilitate Acme’s decision on how many part time customer service agents to have on the floor at any given time. However, there are limitations with the use of data analytics. Big data analytics is not only positive as it also offer some challenges. Data analytic technique requires powerful computers along with employees who are able to interpret the raw data. Data creates bigger haystacks. As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation. Falsity grows exponentially the more data we...
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