...Name: Professor’s Name: Date: Define data analytics Data analytics is the art of examing raw data with an aim of analyzing the information for evaluation and research. Data analytics as used in industry is to allow companies and organizations to analyze their data in order to improve their production. It focuses on inference, the process of reaching a conclusion based on the known by a researcher. (Lavalle & Kruschwitz 2013) Evolution of data analytics Some years ago when we talked about modeling and database profiling it was for the special few who had the privilege of owning a PHD in order to analyze statistical data and make meaning of it. The uses were limited and it was impossible for small businesses/enterprises to make use of the process due to the complexity of the process. That was all in the past and has changed today with a large number of Multi-Nationals adopting the idea and process of data analyzing. This is because data driven solutions have proven to provide a competitive different ion from users. For this reason data analytic solutions are cheaper to small companies and enterprises through the use of cloud networks which in turn reduces the cost of setting up and running networks from within. Due to the low cost, researchers and solution providers have the opportunity to enhance the process to their customers. A good example is the transformation of EBay and Amazon; when Krishnan was thinking of a larger data book, Facebook was still blossoming while...
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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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...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 analytics is projected to change the way companies manage and analyze large information set and how people produce massive amounts of data. A recent findings produced by few Internet and Online Business Degree looked at the future of this trend sweeping through the IT industry. This concept is up-growing one as the current data storage pattern utilized by the companies is not as productive as plotted. It is refers to following type of data 1) Traditional Enterprise Data:- includes customer related data ERP, CRM, web transaction 2) Machine Generated Data:- weblogs, Trading Systems etc 3) Social Data: - data of facebook, twitter, google etc. Big Data can be seen in the finance and business where enormous amount of stock exchange, banking, online and onsite purchasing data flows through computerized systems every day and are then captured and stored for inventory monitoring, customer behavior and market behavior. Day by day the capacity of data is increasing & many of industries are not able to manage it efficiently. By 2020, a total of 35 zeta-bytes of data will be produced as the average annual generation of information grows 43,000 percent, according to Computer Sciences Corporation. Big data may still be a relatively new phenomenon, but its impact is already being felt throughout various industries. Organizations that can effectively store, manage and analyze this information may set themselves apart from their competitors or, even better, make key advancements...
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...Assignment 1: The New Frontier: Data Analytics xxxxxx Professor xxxx CIS500: Information System Decision Making April 17, 20xx Strayer University The New Frontier: Data Analytics Abstract The word “tweet” was first defined as “a chirping noise” whose origin dated back to 1768. Since 2011, Merriam Webster dictionary extends that definition to mean “a post made on the Twitter online messaging service”. Mention of the adoption of “tweet” into the Merriam Webster Dictionary is designed to illustrate two main points; that information can be ambiguous and that technology can reweave the very fabric of human culture. According to research done by Zikopoulos, Eaton, Deroos, Deutsch, & Lapis (2012), there exists 800,000 petabytes (PB) of data stored in the world in the year 2000. By their estimates, that number could reach 35 zettabytes (ZB) by the year 2020 (p. 39). The ability to analyze and process the enormous amount of data is a costly undertaking for companies that are behind the curve and a lucrative business for those that are ahead of the game. Each tweet and post contribution from the users that share the web space further buries the proverbial haystack. It is the ability to sift through the data that determines whether a company can gain traction in their respective industry or if they are simply spinning their wheels. This research paper, centered on Capital Cube and their parent company analytixinsight, will aim to discuss how data analytics is paramount to present and...
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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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...Data Mining for Predictive Analytics Stanley Kenton Marks December 11th, 2012 Abstract Simply collecting data for research is nearly a faux pas in today’s competitive web-market. Analysts are now looking toward the predictive analytics of association discovery in web and data mining, to find Business Intelligence of clustering sub=populations while eliminating errors to keep collected data valid. In the midst this data crunch are fears of lost privacy. Do not fear. Creative innovations are bringing mash-ups to our diversity. Data Analytics Report Useful information, knowledge and finding some unexpected results can “strike it rich” with added creative thinking. Data mining supplies analysts, investors, and traders with customers buying patterns, historical trading rules, even fraudulent behavior for insurance claims. Predictive analytics is used in web mining by analyzing user’s movements from one web content to another. Collecting the data of where a user browses and the content they are seeking can become knowledge if the analyst understands the patterns (Turban & Volonino, 2011). An Association Discovery Algorithm is a tool of data mining where new rules are discovered such that if one item is present then another will also be found. This type of knowledge benefits analyst’s predictability of future probabilities and is very useful to the marketing department, (Ranjan, 2008). A traditional example you...
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...Data Analytics, LLC has tremendously grown in the limited time that it has been in operation and with an expected growth of 60% over the next 18 months the company must continue grow in other ways and not just in personnel and IT infrastructure. The company must leverage technology and the tools they provide for the company to secure our place in the web analytics market. Business analytics can provide the organization many benefits, it can aid in the ability to provide business reports that are more accurate, improve operational efficiency, and better business decisions. Additionally, it can provide the organization the ability to make smarter and faster business decisions which can result in reducing cost, increasing revenue and increase...
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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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...Data Analytics Assignment The random sample of fish with age in days, room temperature and length of fish in centimeter is given. > a data data age temp length 1 14 25 620 2 28 25 1315 3 41 25 2120 4 55 25 2600 5 69 25 3110 6 83 25 3535 7 97 25 3935 8 111 25 4465 9 125 25 4530 10 139 25 4570 11 153 25 4600 12 14 27 625 13 28 27 1215 14 41 27 2110 15 55 27 2805 16 69 27 3255 17 83 27 4015 18 97 27 4315 19 111 27 4495 20 125 27 4535 21 139 27 4600 22 153 27 4600 23 14 29 590 24 28 29 105 25 41 29 2140 26 55 29 2890 27 69 29 3920 28 83 29 3920 29 97 29 4515 30 111 29 4520 31 125 29 4525 32 139 29 4565 33 13 29 4566 34 14 31 590 35 2 31 1205 36 41 31 1915 37 55 31 2140 38 69 31 2710 39 83 31 3020 40 97 31 3030 41 111 31 3040 42 125 31 3180 43 139 31 3257 44 153 31 3214 > res=lm(age~ temp + length,data=data) > data age temp length 1 14 25 620 2 28 25 1315 3 41 25 2120 4 55 25 2600 5 69 25 3110 6 83 25 3535 7 97 25 3935 8 111 25 4465 9 125 25 4530 10 139 25 4570 11 153 25 4600 12 14 27 625 13 28 27 1215 14 41 27 2110 15 55 27 2805 16 69 27 3255 17 83 27 4015 18 97 27 4315 19 111 27 4495 20 125 27 4535 21 139 27 4600 22 153 27 4600 23 14 29 590 24 28 29 105 25 41 29 2140 26 55 29 2890 27 69 29 3920 28 83 29 3920 29 97 29 4515 30 111 29 4520 31 125 29 4525 32 139 29 4565 33 13 29 4566 34 14 31 590 35 2 31 1205 36 41 31 1915 37 55 31 2140 38 69 31 2710 39 83 31 3020 40 97 31 3030 41 111 31 3040 42 125 31 3180 43 139 31 3257 44 153 31 3214 > res=lm(age~ temp + length,data=data)...
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...Business Analytics using Secured Cloud Storage System M Swetha Chandra1, M Suma Latha2, KODAVATIKANTI S M Aditya Kumar3, S K R Swamy4 1,2,3,4 Department of CSE, TRR College of Engineering, Inole, Patancheru, Hyderabad, AP, India 1 sweet.smily99@gmail.com 2 msumalathacse@gmail.com 3 smaditya@gmail.com 4 kramas2004@yahoo.com | | | ABSTRACT Business analytics go far beyond reports, dashboards, and scorecards. Analytic impact occurs after the numbers are delivered, and analytic value is driven by the kinds of questions that are answered. Ordinary analytics tell you what has already happened. Good analytics provide insight into why things happen, and great analytics provide foresight to see what lies ahead. Today’s business climate demands extraordinary analytics. Business managers need to know more than what. The hard questions today are why, what if, and what next. According to Gartner, BI and Analytics is a $12.2 billion market with 16.4% growth in 2011. Gartner's 2012 CIO survey showed that analytics/BI is the No. 1 technology priority for CIOs. The mega vendors such as Oracle, SAS, IBM etc., are already having major portion of the revenue with their packaged applications in these areas. It is estimated by Gartner that Analytics will be touching 75% of potential users by 2020. This is proven by the growth rate of new vendors such as QlickTech and Tableau by 45% (as per Gartner report). Cloud Storage: Cloud Storage, also referred as Data Storage as a Service, is...
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...The New Frontier: Data Analytics CIS 500: Information Systems For Decision-Making Anywhere we travel on the internet we have been bombarded with seemingly codependent requests to, “Like US!”, “Follow US!”, “Watch US!”, “Share US!” from businesses of all types. If you think about it, this is exactly the dynamic. Businesses are in a dependent relationship, relying on the consumers for their survival. If a business wants to engage and remain in a viable relationship, it has to have a product/service the consumer desires and be able to deliver it in a manner that the consumer perceives as valuable. Even if a business has the “next greatest thing”, the consumer will not engage/continue in the relationship if the business does not or cannot provide it in a valuable way to the consumer. Amazon is one company that has been able to successfully develop this dynamic relationship. So then how does a business become and remain successful in the relationship? They need a better understanding of what the consumer wants by improving their Business Intelligence (BI). For decades, businesses have used statistics to analyze data to obtain information and insights for improving BI. With the advent of the computer, data analysis moved from statistical based inferences about the data to a more scientific method based on empirical results. This was the foundational begins of Data Analytics. Data Analytics, as defined in our course text is, specialized software, capabilities, and...
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...Demand and GDP Relationships Regression Statistics | Multiple R | 0.973261851 | R Square | 0.947238631 | Adjusted R Square | 0.946414235 | Standard Error | 611.7650139 | Observations | 66 | | | | Coefficients | Standard Error | t Stat | P-value | Intercept | -7.962621221 | 83.69853866 | -0.095134531 | 0.924505216 | GDP Data (USD $M) | 0.001155711 | 3.40948E-05 | 33.89703095 | 1.32639E-42 | Table 1. Oil Consumption and GDP GDP regression analysis results Table 1 above shows that 94.7% of the 2010 oil demand of selected countries can be explained by the explanatory variable (2010 GDP growth of those countries). The remaining 5.3% of the oil demand in 2010 is unknown and cannot be explained using the linear regression model Oil demand = 1 + (2*GDP) + Residual. Additionally, a $ USD 1,155.71 increase in a country’s GDP will lead to a 1,000 barrel per day increase in that country’s oil demand, assuming all other variables are held fixed. Figure 1. Oil consumption by country The graph above shows the predicted vs. actual oil consumption by country using GDP and oil consumed (‘000 barrel per day) data for 2010. Based from the above, the U.S. was the top oil consumer in 2010. It consumed 16.7 M barrels per day - exceeding the benchmark result from the regression analysis by 15% or 2.4 M barrels per day. Other countries observed to have exceeded their benchmarked oil consumption are Saudi Arabia, Singapore, Iran, Canada, Korea, Venezuela, Thailand...
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...a world ruled by data. Anything and everything we do is recorded in binary coded, tiny information bits. The trend is so rampant that we have companies recording terabytes of data without actually knowing why, what and how the data is going to be used. One of the major factor contributing to this alarming trend is availability of cheaper storage. March this year, Google announced a cold storage called NEARLINE; a competitor to Amazon’s GLACIER. A cold storage is a cloud-based version of an old-fashioned way to store massive amounts of data; too important to delete but not important enough to keep close. Hence, the data isn’t immediately available, usually because it’s stored on tape drives or other media and filed away in a vault somewhere. The scientists at UC San Diego estimate that by 2024, the world’s enterprise servers will annually process the digital equivalent of a stack of books extending more than 4.37 light-years to Alpha Centauri, our closest neighboring star system in the Milky Way Galaxy. (Each book is assumed to be 4.8 centimeters thick and contain 2.5 megabytes of information.). However, this brings us to another question; how useful is the collated data actually to the users? The data only becomes useful when it provides real value to the consumers, citizens and companies i.e. the data should start making sense. However, in real sense, we are far from it. We have barely begun to scratch the surface of the data iceberg. The real analytics and mining with respect...
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