5630 Final Exam

List and describe the steps in the methodology of simulation.

1. Define the problem. We examine and classify the real-world problem, specifying why a simulation approach is appropriate. The system's boundaries, environment, and other such aspects of problem clarification are handled here. 2. Construct the simulation model. This step involves determination of the variables and their relationships, as well as data gathering. Often the process is described by using a flowchart, and then a computer program is written. 3. Test and validate the model. The simulation model must properly represent the system being studied. Testing and validation ensure this. 4. Design the experiment. When the model has been proven valid, an experiment is designed. Determining how long to run the simulation is part of this step. There are two important and conflicting objectives: accuracy and cost. It is also prudent to identify typical (e.g., mean and median cases for random variables), best-case (e.g., low-cost, high-revenue), and worst-case (e.g., high-cost, low-revenue) scenarios. These help establish the ranges of the decision variables and environment in which to work and also assist in debugging the simulation model. 5. Conduct the experiment. Conducting the experiment involves issues ranging from random-number generation to result presentation. 6. Evaluate the results. The results must be interpreted. In addition to standard statistical tools, sensitivity analyses can also be used. 7. Implement the results. The implementation of simulation results involves the same issues as any other implementation. However, the chances of success are better because the manager is usually more involved with the simulation process than with other models.

What is a decision tree?

A decision tree shows the relationships of the problem graphically and can handle complex situations in a compact form.

Describe what it means to have multiple goals.

A multiple goals situation is one in which alternatives are evaluated with several, sometimes conflicting, goals.

What is a stream (in the Big Data world)?

A stream can be thought of as an unbounded flow or sequence of data elements, arriving continuously at high velocity. Streams often cannot be efficiently or effectively stored for subsequent processing; thus Big Data concerns about Velocity (one of the six Vs) are especially prevalent when dealing with streams. Examples of data streams include sensor data, computer network traffic, phone conversations, ATM transactions, web searches, and financial data.

Describe an allocation problem.

A typical allocation problem focuses around the most efficient use of a scarce resource.

Define the blending problem.

A typical blending problem combines the issues of an allocation problem (scarcity) with the issues of a product-mix problem (ratios).

Define the product-mix problem.

A typical product-mix problem focuses around the most efficient ratios of two or more products to be produced.

7. Why is AaaS cost effective?

AaaS in the cloud has economies of scale and scope by providing many virtual analytical applications with better scalability and higher cost savings. The capabilities that a service orientation (along with cloud computing, pooled resources, and parallel processing) brings to the analytic world enable cost-effective data/text mining, large-scale optimization, highly-complex multi-criteria decision problems, and distributed simulation models.

7. Which jobs are most likely to change as a result of automation?

According to the articles, initial job losses will focus on areas that are not skill-based, and that may require repetitive actions that do not require a high amount of knowledge.

What is a spreadsheet add-in? How can add-ins help in DSS creation and use?

Add-in packages for spreadsheets are additional software items that can be integrated into an existing spreadsheet application in order to expand and enhance the type of calculations that can be performed.

Out of the Vs that are used to define Big Data, in your opinion, which one is the most important? Why?

Although all of the Vs are important characteristics, value proposition is probably the most important for decision makers' "big" data in that it contains (or has a greater potential to contain) more patterns and interesting anomalies than "small" data. Thus, by analyzing large and feature rich data, organizations can gain greater business value that they may not have otherwise. While users can detect the patterns in small data sets using simple statistical and machine-learning methods or ad hoc query and reporting tools, Big Data means "big" analytics. Big analytics means greater insight and better decisions, something that every organization needs nowadays.

7. List the impacts of analytics on decision making.

Analytics can change the manner in which many decisions are made and can consequently change managers' jobs. They can help managers gain more knowledge, experience, and expertise, and consequently enhance the quality and speed of their decision making. In particular, information gathering for decision making is completed much more quickly when analytics are in use. This affects both strategic planning and control decisions, changing the decision-making process and even decision-making styles.

7. Explore more transportation applications that may employ location-based analytics.

Another app that is mentioned in the text is one deployed in Pittsburgh, Pennsylvania, and developed in collaboration with Carnegie Mellon University. This app, called ParkPGH, includes predictive capabilities to estimate parking availability. It calculates the number of spaces available in downtown Pittsburgh parking lots and garages.

Why is Big Data important? What has changed to put it in the center of the analytics world?

As more and more data becomes available in various forms and fashions, timely processing of the data with traditional means becomes impractical. The exponential growth, availability, and use of information, both structured and unstructured, brings Big Data to the center of the analytics world. Pushing the boundaries of data analytics uncovers new insights and opportunities for the use of Big Data.

How do you think the Big Data vendor landscape will change in the near future? Why?

As the field matures, more and more traditional data vendors will incorporate Big Data into their architectures. We already saw something similar with the incorporation of XML data types and XPath processing engines in relational database engines. Also, the Big Data market will be increasingly cloud-based, and hosting services will include Big Data data storage options, along with the traditional MySql and SqlServer options. Vendors providing Big Data applications and services, for example in the finance domain or for scientific purposes, will begin to proliferate.

What do you think is the path to becoming a great data scientist?

Becoming a great data scientist requires you to delve deeply into developing quantitative and technical skills, as well as interpersonal and communication skills. In addition, you will need to gain significant domain knowledge (e.g., in business). This effort will most likely require an advanced degree. It also requires a continuous thirst for knowledge and an intense curiosity; you will always be learning in this profession. In addition to meticulous analytical skills, it also requires creativity and imagination.

What is Big Data analytics? How does it differ from regular analytics?

Big Data analytics is analytics applied to Big Data architectures. This is a new paradigm; in order to keep up with the computational needs of Big Data, a number of new and innovative analytics computational techniques and platforms have been developed. These techniques are collectively called high-performance computing, and include in-memory analytics, in-database analytics, grid computing, and appliances. They differ from regular analytics which tend to focus on relational database technologies

What do you think the future of Big Data will be like? Will it lose its popularity to something else? If so, what will it be?

Big Data could evolve at a rapid pace. The buzzword "Big Data" might change to something else, but the trend toward increased computing capabilities, analytics methodologies, and data management of high volume heterogeneous information will continue.

How do you define Big Data? Why is it difficult to define?

Big Data means different things to people with different backgrounds and interests, which is one reason it is hard to define. Traditionally, the term "Big Data" has been used to describe the massive volumes of data analyzed by huge organizations such as Google or research science projects at NASA. Big Data includes both structured and unstructured data, and it comes from everywhere: data sources include Web logs, RFID, GPS systems, sensor networks, social networks, Internet-based text documents, Internet search indexes, detailed call records, to name just a few. Big data is not just about volume, but also variety, velocity, veracity, and value proposition.

Define cloud computing. How does it relate to PaaS, SaaS, and IaaS?

Cloud computing offers the possibility of using software, hardware, platform, and infrastructure, all on a service-subscription basis. Cloud computing enables a more scalable investment on the part of a user. Like PaaS, etc., cloud computing offers organizations the latest technologies without significant upfront investment. In some ways, cloud computing is a new name for many previous related trends: utility computing, application service provider grid computing, on-demand computing, software as a service (SaaS), and even older centralized computing with dumb terminals. But the term cloud computing originates from a reference to the Internet as a "cloud" and represents an evolution of all previous shared/centralized computing trends.

7. How does cloud computing affect BI?

Cloud-computing-based BI services offer organizations the latest technologies without significant upfront investment.

7. How is cognitive computing affecting industry structure?

Cognitive computing will have a large impact in many different industries because jobs that were historically completed by humans may be automated. This would have large cultural implications as well as business implications. From a business perspective, automation has the possibility to decrease cycle time while increasing quality. Conversely, startup cost for automation may be significant.

7. Give examples of companies offering cloud services.

Companies offering such services include 1010data, LogiXML, and Lucid Era. These companies offer feature extract, transform, and load capabilities as well as advanced data analysis tools. Other companies, such as Elastra and Rightscale, offer dashboard and data management tools that follow the SaaS and DaaS models.

How can stream analytics be used in e-commerce?

Companies such as Amazon and eBay use stream analytics to analyze customer behavior in real time. Every page visit, every product looked at, every search conducted, and every click made is recorded and analyzed to maximize the value gained from a user's visit. Behind the scenes, advanced analytics are crunching the real-time data coming from our clicks, and the clicks of thousands of others, to "understand" what it is that we are interested in (in some cases, even we do not know that) and make the most of that information by creative offerings.

What is critical event processing? How does it relate to stream analytics?

Critical event processing is a method of capturing, tracking, and analyzing streams of data to detect events (out of normal happenings) of certain types that are worthy of the effort. It involves combining data from multiple sources to infer events or patterns of interest. An event may also be defined generically as a "change of state," which may be detected as a measurement exceeding a predefined threshold of time, temperature, or some other value. This applies to stream analytics because the events are happening in real time.

What are the critical success factors for Big Data analytics?

Critical factors include a clear business need, strong and committed sponsorship, alignment between the business and IT strategies, a fact-based decision culture, a strong data infrastructure, the right analytics tools, and personnel with advanced analytic skills.

What are the major types of models used in DSS?

DSS uses mostly quantitative models, whereas expert systems use qualitative, knowledge-based models in their applications.

Where do data scientists come from? What educational backgrounds do they have?

Data scientist is an emerging profession, and there is no consensus on where data scientists come from or what educational background a data scientist has to have. Master of Science (or Ph.D.) in Computer Science, MIS, Industrial Engineering, of postgraduate analytics are common examples. But many data scientists have advanced degrees in other disciplines, like the physical or social sciences, or more specialized fields like ecology or system biology.

What is a data scientist? What makes them so much in demand?

Data scientists use a combination of their business and technical skills to investigate Big Data, looking for ways to improve current business analytics practices (from descriptive to predictive and prescriptive) and hence to improve decisions for new business opportunities. One of the biggest differences between a data scientist and a business intelligence user—such as a business analyst—is that a data scientist investigates and looks for new possibilities, while a BI user analyzes existing business situations and operations. Data scientist is an emerging profession, and there is no consensus on where data scientists come from or what educational background a data scientist has to have. But there is a common understanding of what skills and qualities they are expected to possess, which involve a combination of soft and hard skills.

Define data stream mining. What additional challenges are posed by data stream mining?

Data stream mining is the process of extracting novel patterns and knowledge structures from continuous, rapid data records. Processing data streams, as opposed to more permanent data storages, is a challenge. Traditional data mining techniques can process data recursively and repetitively because the data is permanent. By contrast, a data stream is a continuous flow of ordered sequence of instances that can only be read once and must be processed immediately as they come in.

What is a decision table?

Decision tables conveniently organize information and knowledge in a systematic, tabular manner to prepare it for analysis.

How can a decision tree be used in decision making?

Decision trees can be used in decision making to help graphically display the different options that may be selected from so that the results of those decisions can be evaluated in isolation.

What is a decision variable?

Decision variables describe alternative courses of action. The decision maker controls the decision variables.

How can decision-making problems under assumed certainty be handled?

Decision-making problems under assumed certainty are handled as if there is only one possible outcome.

How can decision-making problems under assumed risk be handled?

Decision-making problems under assumed risk are handled as if multiple outcomes are possible, but the probability of each outcome is known.

How can decision-making problems under assumed uncertainty be handled?

Decision-making problems under assumed uncertainty are handled as if multiple outcomes are possible.

7. What are the different types of cloud platforms?

Differing types include IaaS (Infrastructure as a Service), PaaS (Platform as a Service), and Saas (Software as a Service).

7. Identify other examples of analytics applications to redesign work space or team behavior.

Examples can include the use of HR systems to identify potential job candidates that will be the best fit within an existing organization. Another example is analyzing how employees move through the organization and who they collaborate with. This is data that can be used to design office space that is more efficient.

7. What is the value provided by geospatial analytics?

Geospatial analysis gives organizations a broader perspective and aids in decision making. Location intelligence (LI) is enabling organizations to gain critical insights and make better decisions by optimizing important processes and applications. By incorporating demographic details into locations, retailers can determine how sales vary by population level and proximity to other competitors; they can assess the demand and efficiency of supply chain operations. Consumer product companies can identify the specific needs of the customers and customer complaint locations, and easily trace them back to the products. Sales reps can better target their prospects by analyzing their geography.

What is Hadoop? How does it work?

Hadoop is an open source framework for processing, storing, and analyzing massive amounts of distributed, unstructured data. It is designed to handle petabytes and exabytes of data distributed over multiple nodes in parallel, typically commodity machines connected via the Internet. It utilizes the MapReduce framework to implement distributed parallelism. The file organization is implemented in the Hadoop Distributed File System (HDFS), which is adept at storing large volumes of unstructured and semi-structured data. This is an alternative to the traditional tables/rows/columns structure of a relational database. Data is replicated across multiple nodes, allowing for fault tolerance in the system.

Define what it means to perform decision making under assumed certainty, risk, and uncertainty.

In decision making under certainty, it is assumed that complete knowledge is available so that the decision maker knows exactly what the outcome of each course of action will be. A decision made under risk is one in which the decision maker must consider several possible outcomes for each alternative, each with a given probability of occurrence. In decision making under uncertainty, the decision maker considers situations in which several outcomes are possible for each course of action.

7. Describe privacy concerns in analytics.

In general, privacy is the right to be left alone and the right to be free from unreasonable personal intrusions. The Internet, in combination with large-scale databases, has created an entirely new dimension of accessing and using data. The inherent power in systems that can access vast amounts of data can be used for the good of society. For example, by matching records with the aid of a computer, it is possible to eliminate or reduce fraud, crime, government mismanagement, tax evasion, welfare cheating, family-support filching, employment of illegal aliens, and so on. The same is true on the corporate level. Private information about employees may aid in better decision making, but the employees' privacy may be affected. Similar issues are related to information about customers.

What are the motivations for stream analytics?

In situations where data streams in rapidly and continuously, traditional analytics approaches that work with previously accumulated data (i.e., data at arrest) often either arrive at the wrong decisions because of using too much out-of-context data, or they arrive at the correct decisions but too late to be of any use to the organization. Therefore it is critical for a number of business situations to analyze the data soon after it is created and/or as soon as it is streamed into the analytics system. It is no longer feasible to "store everything." Otherwise, analytics will either arrive at the wrong decisions because of using too much out-of-context data, or at the correct decisions but too late to be of any use to the organization. Therefore it is critical for a number of business situations to analyze the data as soon as it is streamed into the analytics system.

List some difficulties that may arise when analyzing multiple goals.

In situations where multiple goals exist, it may be difficult to analyze and optimize for each of these goals. This is because the goals may be conflicting, they may be more complex than they initially appear, and the importance of goals may vary depending on the stakeholder.

What are the use cases for Big Data and Hadoop?

In terms of its use cases, Hadoop is differentiated two ways: first, as the repository and refinery of raw data, and second, as an active archive of historical data. Hadoop, with their distributed file system and flexibility of data formats (allowing both structured and unstructured data), is advantageous when working with information commonly found on the Web, including social media, multimedia, and text. Also, because it can handle such huge volumes of data (and because storage costs are minimized due to the distributed nature of the file system), historical (archive) data can be managed easily with this approach.

7. How does DaaS change the way data is handled?

In the DaaS model, the actual platform on which the data resides doesn't matter. Data can reside in a local computer or in a server at a server farm inside a cloud-computing environment. With DaaS, any business process can access data wherever it resides. Customers can move quickly thanks to the simplicity of the data access and the fact that they don't need extensive knowledge of the underlying data.

Explain the role of intermediate result variables.

Intermediate result variables reflect intermediate outcomes in mathematical models.

7. List the impacts of analytics on other managerial tasks.

Less expertise (experience) is required for making many decisions. Faster decision making is possible because of the availability of information and the automation of some phases in the decision-making process. Less reliance on experts and analysts is required to provide support to top executives. Power is being redistributed among managers. (The more information and analysis capability they possess, the more power they have.) Support for complex decisions allows decisions to be made faster and of better quality. Information needed for high-level decision making is expedited or even self-generated. Automation of routine decisions or phases in the decision-making process (e.g., for frontline decision making and using ADS) may eliminate some managers, especially middle level managers. Routine and mundane work can be done using an analytic system, freeing up managers and knowledge workers to do more challenging tasks.

7. How can location-based analytics help individual consumers?

Location-based behavioral targeting can help to narrow the characteristics of users who are most likely to utilize a retailer's services or products. This sort of analytics would typically target the tech-savvy and busy consumers of the company in question.

What are the main Hadoop components?

Major components of Hadoop are the HDFS, a Job Tracker operating on the master node, Name Nodes, Secondary Nodes, and Slave Nodes. The HDFS is the default storage layer in any given Hadoop cluster. A Name Node is a node in a Hadoop cluster that provides the client information on where in the cluster particular data is stored and if any nodes fail. Secondary nodes are backup name nodes. The Job Tracker is the node of a Hadoop cluster that initiates and coordinates MapReduce jobs or the processing of the data. Slave nodes store data and take direction to process it from the Job Tracker.

Identify some key players in the IoT ecosystem. Explore their offerings.

Major players in the Internet of things can be classified into building block suppliers, platforms and enablement, and applications across multiple verticals. A discussion of any of these areas will be highly variable based on the player and sub area selected and when the research is conducted.

Explain why a manager might use goal seeking.

Managers may elect to perform a goal-seek analysis to find a specific numeric answer given a known set of variables and equations. This type of analysis is easy to perform and provides immediate feedback.

Explain why a manager might perform what-if analysis.

Managers may elect to perform a what-if analysis because they are very approachable and simple to run. They provide immediate feedback, and can be used over a wide variety of scenarios to get a general idea of what possible outcomes may be.

What are the most fruitful industries for stream analytics?

Many industries can benefit from stream analytics. Some prominent examples include e-commerce, telecommunications, law enforcement, cyber security, the power industry, health sciences, and the government.

What is MapReduce?

MapReduce is a programming model that allows the processing of large-scale data analysis problems to be distributed and parallelized.

List three lessons learned from modeling.

Models can be used for a wide array of applications. Some examples include making efficient purchasing decisions, making cost-effective travel plans, and efficiently managing a workforce.

Why are models not used in industry as frequently as they should or could be?

Models may not be used as often in industry as possible because users see them as being too difficult to create, the software too difficult to use, or fear of making mistakes in the creation of the model itself.

What is NoSQL? How does it fit into the Big Data analytics picture?

NoSQL, also known as "Not Only SQL," is a new style of database for processing large volumes of multi-structured data. Whereas Hadoop is adept at supporting large-scale, batch-style historical analysis, NoSQL databases are mostly aimed at serving up discrete data stored among large volumes of multi-structured data to end-user and automated Big Data applications. NoSQL databases trade ACID (atomicity, consistency, isolation, durability) compliance for performance and scalability.

7. Describe new organizational units that are created because of analytics.

One change in organizational structure is the possibility of creating an analytics department, a BI department, or a knowledge management department in which analytics play a major role. This special unit can be combined with or replace a quantitative analysis unit, or it can be a completely new entity

What are the common characteristics of data scientists? Which one is the most important?

One of the most sought-out characteristics of a data scientist is expertise in both technical and business application domains. Data scientists are expected to have soft skills such as creativity, curiosity, communication/interpersonal skills, domain expertise, problem definition skills, and managerial skills as well as sound technical skills such as data manipulation, programming/hacking/scripting, and knowledge of Internet and social media/networking technologies. Data scientists are supposed to be creative and curious, and should be excellent communicators, with the ability to tell compelling stories about their data.

List and describe the types of simulation.

Probabilistic Simulation - In probabilistic simulation, one or more of the independent variables (e.g., the demand in an inventory problem) are probabilistic. Time-independent refers to a situation in which it is not important to know exactly when the event occurred. Time-dependent refers to situations in which time is a factor in the simulation and can affect the outcome.

What functions do they perform?

Querying for data in the distributed system is accomplished via MapReduce. The client query is handled in a Map job, which is submitted to the Job Tracker. The Job Tracker refers to the Name Node to determine which data it needs to access to complete the job and where in the cluster that data is located, then submits the query to the relevant nodes which operate in parallel. A Name Node acts as facilitator, communicating back to the client information such as which nodes are available, where in the cluster certain data resides, and which nodes have failed. When each node completes its task, it stores its result. The client submits a Reduce job to the Job Tracker, which then collects and aggregates the results from each of the nodes.

What is RFID?

RFID is a generic technology that refers to the use of radio-frequency waves to identify objects. Fundamentally, RFID is one example of a family of automatic identification technologies, which also includes the ubiquitous barcodes and magnetic strips.

List the reasons for performing sensitivity analysis.

Sensitivity analysis is extremely important in prescriptive analytics because it allows flexibility and adaptation to changing conditions and to the requirements of different decision-making situations, provides a better understanding of the model and the decision-making situation it attempts to describe, and permits the manager to input data to increase the confidence in the model.

What is a spreadsheet?

Spreadsheet packages are easy-to-use implementation software for the development of a wide range of applications in business, engineering, mathematics, and science. Spreadsheets include extensive statistical, forecasting, and other modeling and database management capabilities, functions, and routines.

Explain why a spreadsheet is so conducive to the development of DSS.

Spreadsheets are very conducive to the development of DSS because they are easy to implement and use for a large base of users. Out of the box they have a wide array of functions and tools. These abilities can be easily augmented through add-in packages and templates.

Compared to regular analytics, do you think stream analytics will have more (or less) use cases in the era of Big Data analytics? Why?

Stream analytics can be thought of as a subset of analytics in general, just like "regular" analytics. The question is, what does "regular" mean? Regular analytics may refer to traditional data warehousing approaches, which does constrain the types of data sources and hence the use cases. Or, "regular" may mean analytics on any type of permanent stored architecture (as opposed to transient streams). In this case, you have more use cases for "regular" (including Big Data) than in the previous definition. In either case, there will probably be plenty of times when "regular" use cases will continue to play a role, even in the era of Big Data analytics

In addition to what is listed in this section, can you think of other industries and/or application areas where stream analytics can be used?

Stream analytics could be of great benefit to any industry that faces an influx of relevant real-time data and needs to make quick decisions. One example is the news industry. By rapidly sifting through data streaming in, a news organization can recognize "newsworthy" themes (i.e., critical events). Another benefit would be for weather tracking in order to better predict tornadoes or other natural disasters.

What is stream analytics? How does it differ from regular analytics?

Stream analytics is the process of extracting actionable information from continuously flowing/streaming data. It is also sometimes called "data in-motion analytics" or "real-time data analytics." It differs from regular analytics in that it deals with high velocity (and transient) data streams instead of more permanent data stores like databases, files, or web pages.

What is special about the Big Data vendor landscape? Who are the big players?

The Big Data vendor landscape is developing very rapidly. It is in a special period of evolution where entrepreneurial startup firms bring innovative solutions to the marketplace. Cloudera is a market leader in the Hadoop space. MapR and Hortonworks are two other Hadoop startups. DataStax is an example of a NoSQL vendor. Informatica, Pervasive Software, Syncsort, and MicroStrategy are also players. Most of the growth in the industry is with Hadoop and NoSQL distributors and analytics providers. There is still very little in terms of Big Data application vendors. Meanwhile, the next-generation data warehouse market has experienced significant consolidation. Four leading vendors in this space—Netezza, Greenplum, Vertica, and Aster Data—were acquired by IBM, EMC, HP, and Teradata, respectively. Mega-vendors Oracle and IBM also play in the Big Data space, connecting and consolidating their products with Hadoop and NoSQL engines.

What does it do?

The MapReduce technique, popularized by Google, distributes the processing of very large multi-structured data files across a large cluster of machines. High performance is achieved by breaking the processing into small units of work that can be run in parallel across the hundreds, potentially thousands, of nodes in the cluster.

List the characteristics of simulation.

The major characteristics of a simulation are that it is a model used to approximate reality, that it is used to conduct experiments, and that it is used for problems that are too complex to be evaluated using numerical optimization techniques.

How does it do it?

The map function in MapReduce breaks a problem into sub-problems, which can each be processed by single nodes in parallel. The reduce function merges (sorts, organizes, aggregates) the results from each of these nodes into the final result.

What are the major uses of IoT?

There are a wide variety of uses for the Internet of Things (IoT). Examples can include monitoring the status of different devices, as well as communicating that status and other environmental information to other devices or to central systems.

In what scenarios can Hadoop and RDBMS coexist?

There are several possible scenarios under which using a combination of Hadoop and relational DBMS-based data warehousing technologies makes sense. For example, you can use Hadoop for storing and archiving multi-structured data, with a connector to a relational DBMS that extracts required data from Hadoop for analysis by the relational DBMS. Hadoop can also be used to filter and transform multi-structural data for transporting to a data warehouse, and can also be used to analyze multi-structural data for publishing into the data warehouse environment. Combining SQL and MapReduce query functions enables data scientists to analyze both structured and unstructured data. Also, front end query tools are available for both platforms.

What are the technology building blocks of IoT?

These major building blocks include hardware, connectivity, the software backend, and applications.

Describe the features of VIS (i.e., VIM) that make it attractive for decision makers.

These simulations have many attractive features for users. The first is that they are visually oriented, and easier for users to interact with and follow their progression. Additionally, they allow the end user to apply their knowledge and experiment with different decision strategies while the model is running. Finally, these systems are very flexible and can represent both static and dynamic systems.

How can VIS be used in operations management?

These systems can be used in operations management because they provide a visual representation of the activities within the business. This provides an easy way for managers to visualize these activities and make adjustments to optimize them.

7. How can geocoded locations assist in better decision making?

They help the user in understanding "true location-based" impacts, and allow them to view at higher granularities than that offered by the traditional postal code aggregations. Addition of location components based on latitudinal and longitudinal attributes to the traditional analytical techniques enables organizations to add a new dimension of "where" to their traditional business analyses, which currently answer questions of "who," "what," "when," and "how much." By integrating information about the location with other critical business data, organizations are now creating location intelligence (LI).

What are the common characteristics of emerging Big Data technologies?

They take advantage of commodity hardware to enable scale-out, parallel processing techniques; employ nonrelational data storage capabilities in order to process unstructured and semi-structured data; and apply advanced analytics and data visualization technology to Big Data to convey insights to end users.

What are the use cases for data warehousing and RDBMS?

Three main use cases for data warehousing are performance, integration, and the availability of a wide variety of BI tools. The relational data warehouse approach is quite mature, and database vendors are constantly adding new index types, partitioning, statistics, and optimizer features. This enables complex queries to be done quickly, a must for any BI application. Data warehousing, and the ETL process, provide a robust mechanism for collecting, cleaning, and integrating data. And, it is increasingly easy for end users to create reports, graphs, and visualizations of the data.

7. How does traditional analytics make use of location-based data?

Traditional analytics produce visual maps that are geographically mapped and based on the traditional location data, usually grouped by the postal codes. The use of postal codes to represent the data is a somewhat static approach for achieving a higher level view of things.

What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

Traditional ways of capturing, storing, and analyzing data are not sufficient for Big Data. Major challenges are the vast amount of data volume, the need for data integration to combine data of different structures in a cost-effective manner, the need to process data quickly, data governance issues, skill availability, and solution costs

List and describe the major issues in modeling.

Two major issues in modeling focus on model management and knowledge-based modeling. Model management focuses on the use and reuse of existing models in a fashion that maintains their integrity. Knowledge-based modeling allows for the construction of solvable/usable models and predictive analysis techniques.

What is the role of visual analytics in the world of Big Data?

Visual analytics help organizations uncover trends, relationships, and anomalies by visually sifting through very large quantities of data. Many vendors are developing visual analytics offerings, which have traditionally applied to structured data warehouse environments (relational and multidimensional), for the Big Data space. To be successful, a visual analytics application must allow for the coexistence and integration of relational and multi-structured data.

Define visual simulation and compare it to conventional simulation.

Visual interactive simulation (VIS), is a simulation method that lets decision makers see what the model is doing and how it interacts with the decisions made, as they are made. This is in contrast to a conventional simulation where only the end product is visible.

What are the challenges facing data warehousing and Big Data? Are we witnessing the end of the data warehousing era? Why or why not?

What has changed the landscape in recent years is the variety and complexity of data, which made data warehouses incapable of keeping up. It is not the volume of the structured data but the variety and the velocity that forced the world of IT to develop a new paradigm, which we now call "Big Data." But this does not mean the end of data warehousing. Data warehousing and RDBMS still bring many strengths that make them relevant for BI and that Big Data techniques do not currently provide.

What are some of the major issues managers have to keep in mind in exploring IoT?

When managers consider the IoT there are several important concepts to take into account. The first is organizational alignment; how does this technology fit in with the company's current goals and resources? Second are interoperability challenges; will the company be able to use this advancement within their current infrastructure? The final issue is security; will information be able to be controlled in a manner that is required and consistent with company policy and existing law?

What are the current trends in modeling?

o the development of model libraries and solution technique libraries o developing and using cloud-based tools and software to access and even run software to perform modeling, optimization, simulation o making analytics models completely transparent to the decision maker o building a model of a model to help in its analysis

7. List ethical issues in analytics.

· Electronic surveillance · Ethics in DSS design · Software piracy · Invasion of individuals' privacy · Use of proprietary databases · Use of intellectual property such as knowledge and expertise · Exposure of employees to unsafe environments related to computers · Computer accessibility for workers with disabilities · Accuracy of data, information, and knowledge · Protection of the rights of users · Accessibility to information · Use of corporate computers for non-work-related purposes · How much decision making to delegate to computers

What are the common business problems addressed by Big Data analytics?

· Process efficiency and cost reduction · Brand management · Revenue maximization, cross-selling, and up-selling · Enhanced customer experience · Churn identification, customer recruiting · Improved customer service · Identifying new products and market opportunities · Risk management · Regulatory compliance · Enhanced security capabilities

List and briefly discuss the major components of a quantitative model.

· Result (outcome) variables reflect the level of effectiveness of a system; that is, they indicate how well the system performs or attains its goal(s). · Decision variables describe alternative courses of action. The decision maker controls the decision variables. · Uncontrollable Variables in any decision-making situation, there are factors that affect the result variables but are not under the control of the decision maker. · Intermediate result variables reflect intermediate outcomes in mathematical models.

List and explain the characteristics of LP

· Returns from different allocations can be compared; that is, they can be measured by a common unit (e.g., dollars, utility). · The return from any allocation is independent of other allocations. · The total return is the sum of the returns yielded by the different activities. · All data are known with certainty. · The resources are to be used in the most economical manner.

7. List some legal issues of analytics.

· What is the value of an expert opinion in court when the expertise is encoded in a computer? · Who is liable for wrong advice (or information) provided by an intelligent application? · What happens if a manager enters an incorrect judgment value into an analytic application and the result is damage or a disaster? · Who owns the knowledge in a knowledge base? · Can management force experts to contribute their expertise?

List and explain the assumptions involved in LP.

• A limited quantity of economic resources is available for allocation. • The resources are used in the production of products or services. • There are two or more ways in which the resources can be used. Each is called a solution or a program. • Each activity (product or service) in which the resources are used yields a return in terms of the stated goal. • The allocation is usually restricted by several limitations and requirements, called constraints.

List several common optimization models.

• Assignment (best matching of objects) • Dynamic programming • Goal programming • Investment (maximizing rate of return) • Linear and integer programming • Network models for planning and scheduling • Nonlinear programming • Replacement (capital budgeting) • Simple inventory models (e.g., economic order quantity) • Transportation (minimize cost of shipments)

List the advantages and disadvantages of simulation.

• The theory is fairly straightforward. • A great amount of time compression can be attained, quickly giving a manager some feel as to the long-term (1- to 10-year) effects of many policies. • Simulation is descriptive rather than normative. This allows the manager to pose what-if questions. Managers can use a trial-and-error approach to problem solving and can do so faster, at less expense, more accurately, and with less risk. • A manager can experiment to determine which decision variables and which parts of the environment are really important, and with different alternatives. • An accurate simulation model requires an intimate knowledge of the problem, thus forcing the model builder to constantly interact with the manager. This is desirable for DSS development because the developer and manager both gain a better understanding of the problem and the potential decisions available. • The model is built from the manager's perspective. • The simulation model is built for one particular problem and typically cannot solve any other problem. Thus, no generalized understanding is required of the manager; every component in the model corresponds to part of the real system. • Simulation can handle an extremely wide variety of problem types, such as inventory and staffing, as well as higher-level managerial functions, such as long-range planning. • Simulation generally can include the real complexities of problems; simplifications are not necessary. For example, simulation can use real probability distributions rather than approximate theoretical distributions. • Simulation automatically produces many important performance measures. • Simulation is often the only DSS modeling method that can readily handle relatively unstructured problems. • Some relatively easy-to-use simulation packages (e.g., Monte Carlo simulation) are available.