...Spend Analysis & Data Analysis Robert Knieriem BUS 307 Professor Ray May 21, 2012 In today’s fast paced, technology heavy corporate world, data that pertains to any and every business aspect, regardless of size has become ever more important in daily operations and decision making. The data that results from these operations and decisions and its collection, analysis and concluding into upcoming decisions will have a deciding factor in how well or how poorly the company operates in the future. This data is made on its own, but putting it together and making sense of it all remains the challenge today’s companies have and continue to deal with. Data analysis and critical thinking skills are important to spend analysis because this information put out requires a human element to understand what is really being put out. As mentioned by Purchasing, data is only data until it’s analyzed and acted on, and only then does it become valuable information to the company. Further, today’s companies who actively collect data are finding themselves looking for those with the knowledge and skill to analyze this data and allow the company to act upon it (Bozarth & Handfield, 2008). Even with today’s most powerful software which simplifies the task of collecting and maintaining this data, the process of analyzing and understanding of this data requires the human touch and only someone with this background and knowledge can put the data into easy to read, easy to understand...
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...Business AnAlytics And intelligence Course Starting: 29 June 2014 Application Deadline: Early Decision 28 February 2014 Regular Decision 15 April 2014 (Batch 5) (Classes conducted on-campus as well as off-campus) Certificate Programme on Business Analytics and Intelligence BATCH 5 in god We trust, All Others Must Bring data - W edwards deming he theory of bounded rationality proposed by nobel laureate Herbert Simon is evermore significant today with increasing complexity of the business problems; limited ability of human mind to analyze alternative solutions and the limited time available for decision making. introduction of enterprise resource planning (eRP) systems has ensured availability of data in many organizations; however, traditional eRP systems lacked data analysis capabilities that can assist the management in decision making. Business Analytics is a set of techniques and processes that can be used to analyse data to improve business performance through fact-based decision making. Business Analytics is the subset of Business intelligence, which creates capabilities for companies to compete in the market effectively. Business Analytics is likely to become one of the main functional areas in most companies. Analytics companies develop the ability to support their decisions through analytic reasoning using a variety of statistical and mathematical techniques. thomas devonport in his book titled, “competing on analytics: the new science of winning”, claims...
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...locations in California is faced with increasing productivity while controlling costs internally. In short, Kudler Foods has to manage costs and use technology to aid in the process of streamlining operations in all locations. The purpose of this analysis is to provide a clear and concise analysis of the method Kudler Fine Foods is currently using and offer recommendations to improve efficiency by capturing important data rather than extemporaneous data. Specifically, the current system used by Kudler Fine Foods does little to provide a true picture of inventory sources and the effect of the data on Kudler Fine Foods bottom line. The current data table found on Kudler Fine Foods Finance & Accounting tab simply lists GL codes, products, and cost. While this information provides basic information regarding inventory and costs, the data table does not break out for users of the data comparable data to make decisions regarding inventory. One benefit of the data table currently used is that it provides departmental data that can be used and analyzed by department managers. Recommendations Data tables, specifically a pivot table can provide essential data that can be used not only departmentally, but also across the organization. The data extracted can be then analyzed and compared location to location providing management insight on cost expenditures and vendor inventory. Many...
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...strongest players among the Non Banking Financial Institutions (NBFI’s) in Sri Lanka. The NBFI’s can once again be categorized into two main sectors and they are the Licensed Finance Companies (LFC’s) and the Specialized Leasing Companies (SLC’s) such as ABC. The NBFI sector in Sri Lanka consists of 48 LFC’s and 8 SLC’s as at2014. The total market of the Non Banking Financial institutions in Sri Lanka, it can be interpreted that the portfolio is divided into three major markets and mainly classified as Finance Leasing, Hire Purchase, Secured Loans and Advances. 1.2 Introduction to the Company “ABC Finance Company PLC is one of the most stable and reputed financial institutions in Sri Lanka”. The life time of the company spans over a period of nearly six decades and has served to add value and positively impact the lives of all its stakeholders. ABC Finance Company PLC (herein after referred to as ABC) started operations as a private limited liability company in the hill capital, Kandy in the year 1967. ABC is among the top 35 corporate entities in Sri Lanka consecutively for the last 12 years. As per the annual report of the company for the year 2014/2015, currently ABC employs 1244 employees, has a distribution reach of 87 branches and serves 124,917 customers. The core business of ABC is the finance leasing business whereas they also provide financial services like hire purchase, operational leasing, small and medium enterprise. The leasing business of ABC focuses mainly...
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...CHAPTER ONE 1.0 INTRODUCTION 1.1 BACKGROUND OF THE STUDY For any business offering services or physical product/ goods depends on the availability of customers. No customers no business at all. Both internal and external customers if well satisfied with the product offered including quality and the extent to which their needs are met they will always wish to consume that product, from the same supplier .Banks as it is in any other business enterprises focuses much on retention of its customers and making them royal to their Bank The rapid growing Banking industry and other financial institutions in Tanzania has lead to increased competition in wining customers and hence banks are struggling to retain their customers them .there is increasing evidence of the benefits of service management in service organizations. For example the benefits of maintaining long term relation ship with customers through quality performance and customer satisfaction has lead to marketing strategies to focus on defensive strategies that are based on retaining customers.For these reasons they have focused on relationship marketing to improve retention and customer relationship with service organizations. 1.2 STATEMENT OF THE PROBLEM Customer retention the activity that a selling organization undertakes in order to reduce customer defections. various studies has been done to explore the factor tha influence of customer retention . 1.3. Objectives of the study The following...
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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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...Data analysis has provided businesses with new opportunities. It provides companies with information on what their customers want and enables businesses to respond to changing market trends in a timely manner. Decision-making is crucial in every business today. It has become important to adapt to, a data-driven decision-making process. Companies are taking advantage of the new technologies in data analysis to benefit from good decisions and identify new opportunities to gain a competitive advantage. Hadoop It is open source software designed to provide massive storage and large data processing power. Hadoop has the ability to handle tasks running at the same time. Hadoop has a storage and processing part. It works by dividing files into large blocks and distributing them amongst the nodes (Kozielski & Wrembel, 2014). In processing, it works with MapReduce to ensure that codes are transferred and nodes are processed in parallel. By using nodes, Hadoop allows data manipulation making it is process faster and more efficiently. It has four main components: The Hadoop Common which contains utilities required, the Hadoop Distributed File System which is the storage part, Hadoop Yarn which manages and computes resources and Hadoop MapReduce which is a program responsible for processing large-scale data. It can process large amounts of data quickly by using multiple computers (Kozielski & Wrembel, 2014). Hadoop is being turned into a data processing operating system by large organizations...
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...Requirements Document 1) In the portal associated with the financial analyst role there will be a BAPI based web service that assists the analyst in performing a detailed cost analysis. Ideally the BAPI will be able to pull major cost centers throughout the organization which will give the analyst an estimate of the expenses generated along with their breakdown 2) Also part of the role would be a web service that returns a financial position summary with current assets, liabilities of the organization credit ratings of vendors. All the above information can be looked at by month, by year or by department. This data gives the analyst valuable information of the company’s current standing compared to where it envisions being. 3) Analytical capabilities of SAP’s HANA which processes years’ worth of data in a matter of seconds will also be an integral part of the finance role. Maintaining cash flow is essential to keep the organization functioning on a daily basis. Data analytics can be used to conduct “Variance Analysis” which is a comparison of projected cash flow against actual cash flow. Such a comparison over different periods gives a financial analyst vital insight about cash flow requirements and helps him better anticipate times when greater cash flow is needed, for example purchasing bulk material, handing out bonuses. 4) i-Views containing SAP services will assist the financial analyst to view detailed reports before they are published. Access to...
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...Quantitative Analysis When research is being conducted you must gather data. “Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation” (Anderson, Sweeney & Williams, 2005, 5.) You must differentiate the type of data before you can analyze it. “There are basically two ways to go about an analysis, qualitative analysis and quantitative analysis” ("Difference between qualitative," 2011). “You can use both qualitative and quantitative reports to track the work performance of individuals, business units and your workforce as a whole” (Ciaran, John). Each type of data has its own advantages and many times analyzers use a combination of both types of data to make decisions. Qualitative and quantitative data are important to gather because they provide different outcomes. These are often used together when analyzing in order to get a full picture of a population. Qualitative data is either on the “nominal or ordinal scale of measurement and may be nonnumeric or numeric” (Anderson, Sweeney & Williams, 2005, 7.). This type of data focuses on interpreting raw data. This type of data is also known as “categorical” data. Qualitative data can be used to evaluate investments or other business opportunities. This type of data can also assist when it comes to decision making. Some believe qualitative analysis is “the foundation of a broad array of investment and financial decision-making methods” ("Qualitative analysis," 2010). ...
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...could be formulated and were introduced to several tabular and graphical procedures for summarizing data. Furthermore, you were shown how crosstabulations and scatter diagrams can be used to summarize data for two variables simultaneously. The terms that you should have learned from this chapter include: A. Qualitative Data: Data that are measured by either nominal or ordinal scales of measurement. Each value serves as a name or label for identifying an item. B. Quantitative Data: Data that are measured by interval or ratio scales of measurement. Quantitative data are numerical values on which mathematical operations can be performed. C. Bar Graph: A graphical method of presenting qualitative data that have been summarized in a frequency distribution or a relative frequency distribution. D. Pie Chart: A graphical device for presenting qualitative data by subdividing a circle into sectors that correspond to the relative frequency of each class. 23 24 Chapter Two E. Frequency Distribution: A tabular presentation of data, which shows the frequency of the appearance of data elements in several nonoverlapping classes. The purpose of the frequency distribution is to organize masses of data elements into smaller and more manageable groups. The frequency distribution can present both qualitative and quantitative data. F. Relative Frequency Distribution: A tabular...
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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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...in well-designed data stores coupled with business friendly software tool that provide knowledge workers timely access, effective analysis and intuitive presentation of the right information, enabling them to take right actions or make decisions". White (2005) it defined BIS as information systems that provide information and improve its quality that supports decision making and achieves business goals. It divided BIS into two parts: 1) data warehouse 2) access to data, data analysis and reporting. KalKaota &Robinson, (1999) business intelligence systems infrastructure components that support the quality of decision making: 1. Key information technology related to store data (Extraction, transforming...
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...ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics...
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...INTRODUCTION TO BIG DATA IN RETAIL Big Data is a massive pool of data (both structured and unstructured) that cannot be processed using traditional database and software techniques. When any particular organization uses this catch phrase they refer to the technology that can be used to channelize this huge pool of data into some useful information. This channelization includes modification, creation, manipulation, storage, transfer, sharing and analysis of the data. Big data in Retail Consider a situation in which it is the busiest shopping time of the year and only one particular product is flying off the shelf. In the past, the stores who reacted to this demand forecast or had good amount of inventory of that product had an advantage....
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... . . . . . . . . . . . . . .7 Benefits and Barriers to Implementation 10 Business Benefits Sought from Customer Analytics . . . . . . . . . . 10 Barriers to Adoption of Customer Analytics . . . . . . . . . . . . . . 12 role of Analytics in Increasing Marketing roI . . . . . . . . . . . . . 13 Analytics Tools, Data Sources, and Techniques 17 BI, olAP, and data discovery for Customer Analytics . . . . . . . . . 18 In-Memory Computing for More rapid discovery Analysis . . . . . . . 20 Predictive Analytics, data Mining, and Advanced Statistics Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 filling the role of the data Scientist for Customer Analytics . . . . . . 23 Applying Technologies for Social Media Data Analysis 24 Applying Analytics to find and Influence the Influencers . . . . . . . . 26 Selecting and Accessing Internal and External Social Media data . . . 27 finding the right role for Hadoop and Mapreduce . . . . . . . . . . 28 Data Management and Integration...
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