Business Intelligence and Analytics PDF

 Business Inteligence and Analytics LESSON 1  Introduction to Business Intelligence Business Intelligence - strategy and the planning that is incorporated in any business. It may also include products, technologies and analysis and presentation of business information. What is BI? - “a s...

 Business Inteligence and Analytics LESSON 1  Introduction to Business Intelligence Business Intelligence - strategy and the planning that is incorporated in any business. It may also include products, technologies and analysis and presentation of business information. What is BI? - “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes”.  The term “data surfacing” is also more often associated with BI functionality. BI technologies are capable of handling large amounts of structured and sometimes unstructured data to help identify, develop and otherwise create new strategic business opportunities. The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability. provide historical, current and predictive views of business operations. Common functions of business intelligence technologies ○ reporting, ○ business performance ○ online analytical management, processing, ○ benchmarking, ○ analytics, ○ text mining, ○ data mining, ○ predictive analytics ○ process mining, and ○ complex event processing, ○ prescriptive analytics. BI can be used to support a wide range of business decisions ranging from operational to strategic. product positioning ○ external data - data derived Pricing from the market in which a company operates priorities, goals and ○ internal data - data from company sources internal to the directions at the business such as financial and broadest level. operations data  Component Business intelligence is made up of an increasing number of components including: Multidimensional aggregation and allocation Denormalization, tagging and standardization Realtime reporting with analytical alert A method of interfacing with unstructured data sources Group consolidation, budgeting and rolling forecasts Statistical inference and probabilistic simulation Key performance indicators optimization Version control and process management Open item management  History The earliest known use of the term “Business Intelligence” is in Richard Millar Devens’ in the ‘Cyclopædia of Commercial and Business Anecdotes’ from 1865. Devens used the term to describe how the banker, Sir Henry Furnese, gained profit by receiving and acting upon information about his environment, prior to his competitors. “Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He employed the Webster’s dictionary definition of intelligence: “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.  History Business intelligence as it is understood today is said to have evolved from the decision support systems (DSS) that began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.  Data Warehousing A data warehouse contains a copy of analytical data that facilitates decision support. However, not all data warehouses serve for business intelligence, nor do all business intelligence applications require a data warehouse.  Data Warehousing A data warehouse contains a copy of analytical data that facilitates decision support. However, not all data warehouses serve for business intelligence, nor do all business intelligence applications require a data warehouse.  Data Warehousing 1. Using a broad definition: “Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making.” Under this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master-data management, text-and-content-analytics, and many others that the market sometimes lumps into the “Information Management” segment. 2. Forrester defines the narrower business-intelligence market as, “...referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards.”  Comparison with Competitive Intelligence Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors.  Comparison with Business Analytics One definition contrasts the two, stating that the term business intelligence refers to collecting business data to find information primarily through asking questions, reporting, and online analytical processes. Business analytics, on the other hand, uses statistical and quantitative tools for explanatory and predictive modeling.  Applications in an Enterprise Business intelligence can be applied to the following business purposes, in order to drive business value. 1. Measurement – program that creates a hierarchy of performance metrics and benchmarking that informs business leaders about progress towards business goals (business process management). 2. Analytics – program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, data lineage, complex event processing and prescriptive analytics. 3. Reporting/enterprise reporting – program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP.  Applications in an Enterprise 4. Collaboration/collaboration platform – program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange. 5. Knowledge management – program to make the company data-driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance.  Success Factors of Implementation According to Kimball et al., there are three critical areas that organizations should assess before getting ready to do a BI project: 1. The level of commitment and sponsorship of the project from senior management. 2. The level of business need for creating a BI implementation. 3. The amount and quality of business data available.  Business Needs Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation. The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.  Amount and Quality of Available Data Without proper data, or with too little quality data, any BI implementation fails; it does not matter how good the management sponsorship or business-driven motivation is. Before implementation it is a good idea to do data profiling. This analysis identifies the “content, consistency and structure [..]” of the data. When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario  Amount and Quality of Available Data The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence. These essential steps of business intelligence include but are not limited to: 1. Go through business data sources in order to collect needed data 2. Convert business data to information and present appropriately 3. Query and analyze data 4. Act on the collected data  Amount and Quality of Available Data The quality aspect in business intelligence should cover all the process from the source data to the final reporting. At each step, the quality gates are different: 1. Source Data: a. Data Standardization: make data comparable (same unit, same pattern...) b. Master Data Management: unique referential 2. Operational Data Store (ODS): a. Data Cleansing: detect & correct inaccurate data b. Data Profiling: check inappropriate value, null/empty  BI Portals A Business Intelligence portal (BI portal) is the primary access interface for Data Warehouse (DW) and Business Intelligence (BI) applications. The BI portal is the user’s first impression of the DW/BI system. It is typically a browser application, from which the user has access to all the individual services of the DW/BI system, reports and other analytical functionality. The BI portal must be implemented in such a way that it is easy for the users of the DW/BI application to call on the functionality of the application. The BI portal’s main functionality is to provide a navigation system of the DW/BI application. This means that the portal has to be implemented in a way that the user has access to all the functions of the DW/BI application.  BI Portals The most common way to design the portal is to custom fit it to the business processes of the organization for which the DW/BI application is designed, in that way the portal can best fit the needs and requirements of its users. The most common way to design the portal is to custom fit it to the business processes of the organization for which the DW/BI application is designed, in that way the portal can best fit the needs and requirements of its users. The BI portal needs to be easy to use and understand, and if possible have a look and feel similar to other applications or web content of the organization the DW/BI application is designed for (consistency).  BI Portals The following is a list of desirable features for web portals in general and BI portals in particular: Usable User should easily find what they need in the BI tool. Content Rich The portal is not just a report printing tool, it should contain more functionality such as advice, help, support information and documentation. Clean The portal should be designed so it is easily understandable and not over-complex as to confuse the users  BI Portals The following is a list of desirable features for web portals in general and BI portals in particular: Current The portal should be updated regularly. Interactive The portal should be implemented in a way that makes it easy for the user to use its functionality and encourage them to use the portal. Scalability and customization give the user the means to fit the portal to each user. Value Oriented It is important that the user has the feeling that the DW/BI application is a valuable resource that is worth working on.  Marketplace There are a number of business intelligence vendors, often categorized into the remaining independent “pure-play” vendors and consolidated “megavendors” that have entered the market through a recent trend of acquisitions in the BI industry.  Industry-specific Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations or healthcare. The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals.  Semi-structured or Unstructured Data Businesses create a huge amount of valuable information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material and news.These information types are called either semi-structured or unstructured data. However, organizations often only use these documents once. The managements of semi-structured data is recognized as a major unsolved problem in the information technology industry. According to projections from Gartner (2003), white collar workers spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data. BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making.  Unstructured Data vs. Semi-structured Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, unstructured data cannot be stored in predictably ordered columns and rows. One type of unstructured data is typically stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems. Unstructured data may also refer to irregularly or randomly repeated column patterns that vary from row to row within each file or document.  Problems with Semi-structured or Unstructured Data There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are: 1. Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats. 2. Terminology – Among researchers and analysts, there is a need to develop a standardized terminology. 3. Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis.  Problems with Semi-structured or Unstructured Data There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are: 4. Searchability of unstructured textual data – A simple search on some data, e.g. apple, re-sults in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008) gives an example: “a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies.”  Problems with Semi-structured or Unstructured Data There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are: 4. Searchability of unstructured textual data – A simple search on some data, e.g. apple, re-sults in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008) gives an example: “a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies.”  BUSINESS INTELIGENCE AND ANALYTICS LESSON 1 PART 2 Mobile Business Intelligence & Real-time Business Intelligence Mobile Business Intelligence Mobile Business Intelligence (Mobile BI or Mobile Intelligence) is defined as “The capability that enables the mobile workforce to gain business insights through information analysis using applications optimized for mobile devices” Verkooij(2012) Business intelligence (BI) refers to computer-based techniques used in spotting, digging-out, and analyzing business data, such as sales revenue by products and/or departments or associated costs and incomes. History Information Delivery to Mobile Devices The predominant method for accessing BI information is using proprietary software or a Web browser on a personal computer to connect to BI applications. These BI applications request data from databases. Starting in the late 1990s, BI systems offered alternatives for receiving data, including email and mobile devices. History Static Data Push Initially, mobile devices such as pagers and mobile phones received pushed data using a short message service (SMS) or text messages. These applications were designed for specific mobile devices, contained minimal amounts of information, and provided no data interactivity. As a result,the early mobile BI applications were expensive to design and maintain while providing limited informational value, and garnered little interest. History Data Access Via a Mobile Browser The mobile browser on a smartphone, a handheld computer integrated with a mobile phone, provided a means to read simple tables of data. The small screen space, immature mobile browsers, and slow data transmission could not provide a satisfactory BI experience. Accessibility and bandwidth may be perceived as issues when it comes to mobile technology, but BI solutions provide advanced functionality to predict and outperform such potential challenges. History Purpose-built Mobile BI Apps Apple quickly set the standard for mobile devices with the introduction of the iPhone. In the first three years, Apple sold over 33.75 million units. Similarly, in 2010, Apple sold over 1 million iPads in just under three months. Both devices feature an interactive touchscreen display that is the de facto standard on many mobile phones and tablet computers. In 2008, Apple published the SDK for which developers can build applications that run natively on the iPhone and iPad instead of Safari-based applications. These native applications can give the user a robust, easier-to-read and easier-to-navigate experience. Others were quick to join in the success of mobile devices and app downloads. The Google Play Store now has over 700,000 apps available for the mobile devices running the Android operating system. History Web Applications vs. Device-specific Applications for Mobile BI In early 2011, as the mobile BI software market started to mature and adoption started to grow at a significant pace in both small and large enterprises, most vendors adopted either a purpose-built, device-specific application strategy (e.g. iPhone or Android apps, downloaded from iTunes or the Google Play Store) or a web application strategy (browser-based, works on most devices without an application being installed on the device). This debate continues and there are benefits and drawbacks to both methods. One potential solution will be the wider adoption of HTML5 on mobile devices which will give web applications many of the characteristics of dedicated applications while still allowing them to work on many devices without an installed application. Demand Gartner analyst Ted Friedman believes that mobile delivery of BI is all about practical, tactical information needed to make immediate decisions – “The biggest value is in operational BI — information in the context of applications — not in pushing lots of data to somebody’s phone.” Accessing the Internet through a mobile device such as a smartphone is also known as the mobile Internet or mobile Web. IDC expects the US mobile workforce to increase by 73% in 2011. Morgan Stanley reports the mobile Internet is ramping up faster than its predecessor, the desktop Internet, enabling companies to deliver knowledge to their mobile workforce to help them make more profitable decisions. Business Benefits In its latest Magic Quadrant for Business Intelligence Platforms, Gartner examines whether the platform enables users to “fully interact with BI content delivered to mobile devices.” The phrase “fully interact” is the key. The ability to send alerts embedded in email or text messages, or links to static content in email messages hardly represents sophistication in mobile analytics. For users to benefit from mobile BI, they must be able to navigate dashboard and guided analytics comfortably—or as comfortably as the mobile device will allow, which is where devices with high-resolution screens and touch interfaces (like the iPhone and Android-based phones) have a clear edge over, say, earlier editions of BlackBerry. Applications Similar to consumer applications, which have shown an ever increasing growth over the past few years, a constant demand for anytime, anywhere access to BI is leading to a number of custom mobile application development. Businesses have also started adopting mobile solutions for their workforce and are soon becoming key components of core business processes. In an Aberdeen survey conducted in May 2010, 23% of companies participating indicated that they now have a mobile BI app or dashboard in place, while another 31% indicated that they plan to implement some form of mobile BI in the next year. Definitions Mobile BI applications can be defined/segregated as follows: Mobile Browser Rendered App: Almost any mobile device enables Web-based, thin client, HTML-only BI applications. However, these apps are static and provide little data interactivity. Data is viewed just as it would be over a browser from a personal computer Definitions Customized App: A step up from this approach is to render each (or all) reports and dashboards in device-specific format. In other words, provide information specific to the screen size, optimize usage of screen real estate, and enable device-specific navigation controls. Examples of these include thumb wheel or thumb button for BlackBerry, up/down/left/ right arrows for Palm, gestural manipulation for iPhone. This approach requires more effort than the previous but no additional software. Mobile Client App: The most advanced, the client app provides full interactivity with the BI content viewed on the device. In addition, this approach provides periodic caching of data which can be viewed and analyzed even offline. Custom-coded Mobile BI Apps Mobile BI applications are often custom-coded apps specific to the underlying mobile operating system. For example, the iPhone apps require coding in Objective-C while Android apps require coding in Java. In addition to the user functionality of the app, the app must be coded to work with the supporting server infrastructure required to serve data to the mobile BI app. While custom-coded apps offer near limitless options, the specialized software coding expertise and infrastructure can be expensive to develop, modify, and maintain. Fixed-form Mobile BI Applications Business data can be displayed in a mobile BI client (or web browser) that serves as a user interface to existing BI platforms or other data sources, eliminating the need for new master sources of data and specialized server infrastructure. This option offers fixed and configurable data visualizations such as charts, tables, trends, KPIs, and links, and can usually be deployed quickly using existing data sources. However, the data visualizations are not limitless and cannot always be extended to beyond what is available from the vendor. Graphical Tool-developed Mobile BI Apps Mobile BI apps can also be developed using the graphical, drag-and-drop development environments of BI platforms. The advantages including the following: 1. Apps can be developed without coding, 2. Apps can be easily modified and maintained using the BI platform change management tools, 3. Apps can use any range of data visualizations and not be limited to just a few, 4. Apps can incorporate specific business workflows, and 5. The BI platform provides the server infrastructure. Using graphical BI development tools can allow faster mobile BI app development when a custom application is required. Security Considerations for Mobile BI Apps High adoption rates and reliance on mobile devices makes safe mobile computing a critical concern. The Mobile Business Intelligence Market Study discovered that security is the number one issue (63%) for organizations. A comprehensive mobile security solution must provide security at these levels: Device Transmission Authorization, Authentication, and Network Security Device Security A senior analyst at the Burton Group research firm recommends that the best way to ensure data will not be tampered with is to not store it on the client device (mobile device). Role of BI for Securing Mobile Apps To ensure high security standards, BI software platforms must extend the authentication options and policy controls to the mobile platform. Business intelligence software platforms need to ensure a secure encrypted keychain for storage of credentials. Administrative control of password policies should allow creation of security profiles for each user and seamless integration with centralized security directories to reduce administration and maintenance of users. Products A number of BI vendors and niche software vendors offer mobile BI solutions. Some notable examples include: CollabMobile QlikView Cognos Roambi Cherrywork SAP Dimensional Insight InetSoft Tableau Software Infor Sisense Information Builders TARGIT Business MicroStrategy Intelligence Real-time Business Intelligence Real-time business intelligence (RTBI) is the process of delivering business intelligence (BI) or information about [business operations] as they occur. Real time means near to zero latency and access to information whenever it is required. The speed of today’s processing systems has moved classical data warehousing into the realm of real-time. The result is real-time business intelligence. Business transactions as they occur are fed to a real-time BI system that maintains the current state of the enterprise. The RTBI system not only supports the classic strategic functions of data warehousing for deriving information and knowledge from past enterprise activity, but it also provides real-time tactical support to drive enterprise actions that react immediately to events as they occur. As such, it replaces both the classic data warehouse and the enterprise application integration (EAI) functions. Such event-driven processing is a basic tenet of real-time business intelligence. Evolution of RTBI In today’s competitive environment with high consumer expectation, decisions that are based on the most current data available to improve customer relationships, increase revenue, maximize operational efficiencies, and yes – even save lives. This technology is real-time business intelligence. Real-time business intelligence systems provide the information necessary to strategically improve an enterprise’s processes as well as to take tactical advantage of events as they occur. Latency All real-time business intelligence systems have some latency, but the goal is to minimize the time from the business event happening to a corrective action or notification being initiated. Analyst Richard Hackathorn describes three types of latency: Data latency; the time taken to collect and store the data Analysis latency; the time taken to analyze the data and turn it into actionable information Action latency; the time taken to react to the information and take action Latency Real-time business intelligence technologies are designed to reduce all three latencies to as close to zero as possible, whereas traditional business intelligence only seeks to reduce data latency and does not address analysis latency or action latency since both are governed by manual processes. Architectures Event-based Real-time Business Intelligence systems are event driven, and may use Complex Event Processing, Event Stream Processing and Mashup (web application hybrid) techniques to enable events to be analysed without being first transformed and stored in a database. These in- memory techniques have the advantage that high rates of events can be monitored, and since data does not have to be written into databases data latency can be reduced to milliseconds. Architectures Data Warehouse An alternative approach to event driven architectures is to increase the refresh cycle of an existing data warehouse to update the data more frequently. These real-time data warehouse systems can achieve near real-time update of data, where the data latency typically is in the range from minutes to hours. The analysis of the data is still usually manual, so the total latency is significantly different from event driven architectural approaches. Architectures Process-aware This is sometimes considered a subset of Operational intelligence and is also identified with Business Activity Monitoring. It allows entire processes (transactions, steps) to be monitored, metrics (latency, completion/failed ratios, etc.) to be viewed, compared with warehoused historic data, and trended in real-time. Advanced implementations allow threshold detection, alerting and providing feedback to the process execution systems themselves, thereby ‘closing the loop’. Technologies that Support Real-time Analytics Technologies that can be supported to enable real-time business intelligence are data visualization, data federation, enterprise information integration, enterprise application integration and service oriented architecture. Complex event processing tools can be used to analyze data streams in real time and either trigger automated actions or alert workers to patterns and trends. Data Warehouse Appliance Data warehouse appliance is a combination of hardware and software product which was designed exclusively for analytical processing. In data warehouse implementation, tasks that involve tuning, adding or editing structure around the data, data migration from other databases, reconciliation of data are done by DBA. Mobile Technology There are very limited vendors for providing Mobile business intelligence; MBI is integrated with existing BI architecture. MBI is a package that uses existing BI applications so people can use on their mobile phone and make informed decision in real time. Application Areas Algorithmic trading Payments & cash monitoring Fraud detection Data security monitoring Systems monitoring Supply chain optimization Application performance monitoring RFID/sensor network data analysis Customer Relationship Management Workstreaming Demand sensing Call center optimization Dynamic pricing and yield management Enterprise Mashups and Mashup Data validation Dashboards Operational intelligence and risk management Transportation industry Analytics: A Comprehensive Study 1. Business Analytics Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, online analytical processing (OLAP), and “alerts.” In other words, querying, reporting, OLAP, and alert tools can answer questions such as ○ what happened, ○ how many, ○ how often, ○ where the problem is, and ○ what actions are needed. Business analytics can answer questions like ○ why is this happening, ○ what if these trends continue, ○ what will happen next (that is, predict), ○ what is the best that can happen (that is, optimize). Examples of Application Banks, such as Capital One, use data analysis (or analytics, as it is also called in the business setting), to differentiate among customers based on credit risk, usage and other characteristics and then to match customer characteristics with appropriate product offerings. Harrah’s, the gaming firm, uses analytics in its customer loyalty programs. E & J Gallo Winery quantitatively analyzes and predicts the appeal of its wines. Between 2002 and 2005, Deere & Company saved more than $1 billion by employing a new analytical tool to better optimize inventory. A telecoms company that pursues efficient call centre usage over customer service may save money. Types of Analytics Decision analytics: supports human decisions with visual analytics the user models to reflect reasoning. Descriptive analytics: gains insight from historical data with reporting, scorecards, clustering etc. Predictive analytics: employs predictive modeling using statistical and machine learning techniques Prescriptive analytics: recommends decisions using optimization, simulation, etc. Basic Domains within Analytics Behavioral analytics Fraud analytics Cohort Analysis Marketing analytics Collections analytics Pricing analytics Contextual data modeling - supports Retail sales analytics the human reasoning that occurs Risk & Credit analytics after viewing “exec- utive dashboards” or any other visual Supply Chain analytics analytics Talent analytics Cyber analytics Telecommunications Enterprise Optimization Transportation analytics Financial services analytics History 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 himself. Challenges Business analytics depends on sufficient volumes of high quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available. Competing on Analytics Thomas Davenport, professor of information technology and management at Babson College argues that businesses can optimize a distinct business capability via analytics and thus better compete. He identifies these characteristics of an organization that are apt to compete on analytics: One or more senior executives who strongly advocate fact-based decision making and, specifically, analytics Widespread use of not only descriptive statistics, but also predictive modeling and complex optimization techniques Substantial use of analytics across multiple business functions or processes Movement toward an enterprise level approach to managing analytical tools, data, and organizational skills and capabilities 2. ANALYTICS Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance. Analytics often favors data visualization to communicate insight. Analytics vs. Analysis Analytics is multidisciplinary. There is extensive use of mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data—data analysis. The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective.. There is an increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks to do predictive modeling. Examples 1. Marketing Optimization Marketing has evolved from a creative process into a highly data-driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy. 1. Portfolio Analytics A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole. 3. Risk Analytics Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict individual’s delinquency behavior and widely used to evaluate the credit worthiness of each applicant. Furthermore, risk analyses are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions like Online Payment Gateway companies to analyse if a transaction was genuine or fraud. For this purpose they use the transaction history of the customer. This is more commonly used in Credit Card purchase, when there is a sudden spike in the customer transaction volume the customer gets a call of confirmation if the transaction was initiated by him/her. This helps in reducing loss due to such circumstances. 4. Digital Analytics Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automations. This also includes the SEO (Search Engine Optimization) where the keyword search is tracked and that data is used for marketing purposes. Even banner ads and clicks come under digital analytics. All marketing firms rely on digital analytics for their digital marketing assignments, where MROI (Marketing Return on Investment) is important. 5. Security Analytics Security analytics refers to information technology (IT) solutions that gather and analyze security events to bring situational awareness and enable IT staff to understand and analyze events that pose the greatest risk. Solutions in this area include security information and event management solutions and user behavior analytics solutions. 6. Software Analytics Software analytics is the process of collecting information about the way a piece of software is used and produced. Risks The main risk for the people is discrimination like price discrimination or statistical discrimination. Analytical processes can also result in discriminatory outcomes that may violate anti-discrimination and civil rights laws. There is also the risk that a developer could profit from the ideas or work done by users, like this example: Users could write new ideas in a note taking app, which could then be sent as a custom event, and the developers could profit from those ideas. This can happen because the ownership of content is usually unclear in the law. If a user’s identity is not protected, there are more risks; for example, the risk that private information about users is made public on the internet. In the extreme, there is the risk that governments could gather too much private information, now that the governments are giving themselves more powers to access citizens’ information. 3. Software Analytics Software Analytics refers to analytics specific to software systems and related software develop- ment processes. It aims at describing, predicting, and improving development, maintenance, and management of complex software systems. Methods and techniques of software analytics typically rely on gathering, analyzing, and visualizing information found in the manifold data sources in the scope of software systems and their software development processes---software analytics “turns it into actionable insight to inform better decisions related to software”. Software analytics represents a base component of software diagnosis that generally aims at generating findings, conclusions, and evaluations about software systems and their implementation, composition, behavior, and evolution. Software analytics frequently uses and combines approaches and techniques from statistics, prediction analysis, data mining, and scientific visualization. For example, software analytics can map data by means of software maps that allow for interactive exploration. Software Analytics Providers CAST Software IBM Cognos Business Intelligence Kiuwan Microsoft Azure Application Insights Nalpeiron Software Analytics New Relic Squore Tableau Software Trackerbird Software Analytics 4. Embedded Analytics Embedded analytics is the technology designed to make data analysis and business intelligence more accessible by all kind of application or user. Definition According to Gartner analysts Kurt Schlegel, traditional business intelligence were suffering in 2008 a lack of integration between the data and the business users. This technology intention is to be more pervasive by real-time autonomy and self-service of data visualization or customization, meanwhile decision makers, business users or even customers are doing their own daily workflow and tasks. History First mentions of the concept were made by Howard Dresner, consultant, author, former Gartner analyst and inventor of the term “business intelligence”. Consolidation of business intelligence “doesn’t mean the BI market has reached maturity” said Howard Dresner while he was working for Hyperion Solutions, a company that Oracle bought in 2007. Oracle started then to use the term “embedded analytics” at their press release for Oracle® Rapid Planning on 2009. Gartner Group, a company for which Howard Dresner has been working, finally added the term to their IT Glossary on November 5, 2012.. It was clear this was a mainstream technology when Dresner Advisory Services published the 2014 Embedded Business Intelligence Market Study as part of the Wisdom of Crowds® Series of Research, including 24 vendors. Tools Actuate Pentaho Dundas Data Qlik Visualization SAP GoodData SAS IBM Tableau icCube TIBCO Logi Analytics Sisense 5. Learning Analytics Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. A related field is educational data mining. What is Learning Analytics? The definition and aims of Learning Analytics are contested. One earlier definition discussed by the community suggested that “Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people’s learning.”  It has been pointed out that there is a broad awareness of analytics across educational institutions for various stakeholders, but that the way ‘learning analytics’ is defined and implemented may vary, including: 1. for individual learners to reflect on their achievements and patterns of behaviour in relation to others; 2. as predictors of students requiring extra support and attention; 3. to help teachers and support staff plan supporting interventions with individuals and groups; 4. for functional groups such as course team seeking to improve current courses or develop new curriculum offerings; and 5. for institutional administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.” Differentiating Learning Analytics and Educational Data Mining Differentiating the fields of educational data mining (EDM) and learning analytics (LA) has been a concern of several researchers. George Siemens takes the position that educational data mining encompasses both learning analytics and academic analytics, the former of which is aimed at governments, funding agencies, and administrators instead of learners and faculty. Baepler and Murdoch define academic analytics as an area that “...combines select institutional data, statistical analysis, and predictive modeling to create intelligence upon which learners, instructors, or administrators can change academic behavior”. History The Context of Learning Analytics In “The State of Learning Analytics in 2012: A Review and Future Challenges” Rebecca Ferguson tracks the progress of analytics for learning as a development through: 1. The increasing interest in ‘big data’ for business intelligence 2. The rise of online education focussed around Virtual Learning Environments (VLEs), Content Management Systems (CMSs), and Management Information Systems (MIS) for education, which saw an increase in digital data regarding student background (often held in the MIS) and learning log data (from VLEs). This development afforded the opportunity to apply ‘business intelligence’ techniques to educational data 3. Questions regarding the optimisation of systems to support learning particularly given the question regarding how we can know whether a student is engaged/understanding if we can’t see them? 4. Increasing focus on evidencing progress and professional standards for accountability systems 5. This focus led to a teacher stakehold in the analytics - given that they are associated with accountability systems 6. Thus an increasing emphasis was placed on the pedagogic affordances of learning analytics 7. This pressure is increased by the economic desire to improve engagement in online education for the deliverance of high quality - affordable - education History of The Techniques and Methods of Learning Analytics In a discussion of the history of analytics, Cooper highlights a number of communities from which learning analytics draws techniques, including: 1. Statistics - which are a well established means to address hypothesis testing 2. Business Intelligence - which has similarities with learning analytics, although it has historically been targeted at making the production of reports more efficient through enabling data access and summarising performance indicators. 3. Web analytics - tools such as Google analytics report on web page visits and references to websites, brands and other keyterms across the internet. The more ‘fine grain’ of these techniques can be adopted in learning analytics for the exploration of student trajectories through learning resources (courses, materials, etc.). 4. Operational research - aims at highlighting design optimisation for maximising objectives through the use of mathematical models and statistical methods. Such techniques are implicated in learning analytics which seek to create models of real world behaviour for practical application. 5. Artificial intelligence and Data mining - Machine learning techniques built on data mining and AI methods are capable of detecting patterns in data. In learning analytics such techniques can be used for intelligent tutoring systems, classification of students in more dynamic ways than simple demographic factors, and resources such as ‘suggested course’ systems modelled on collaborative filtering techniques. 6. Social Network Analysis - SNA analyses relationships between people by exploring im- plicit (e.g. interactions on forums) and explicit (e.g. ‘friends’ or ‘followers’) ties online and offline. SNA developed from the work of sociologists like Wellman and Watts, and mathe. 7. Information visualization - visualisation is an important step in many analytics for sense making around the data provided - it is thus used across most techniques (including those above). Analytic Methods Methods for learning analytics include: Content analysis - particularly of resources which students create (such as essays) Discourse Analytics Discourse analytics aims to capture meaningful data on student interactions which (unlike ‘social network analytics’) aims to explore the properties of the language used, as opposed to just the network of interactions, or forum-post counts, etc. Social Learning Analytics which is aimed at exploring the role of social interaction in learning, the importance of learning networks, discourse used to sensemake, etc. Disposition Analytics which seeks to capture data regarding student’s dispositions to their own learning, and the relationship of these to their learning. For example, “curious” learn ers may be more inclined to ask questions - and this data can be captured and analysed for learning analytics. Analytic Outcomes Analytics have been used for: Prediction purposes, for example to identify ‘at risk’ students in terms of drop out or course failure Personalization & Adaptation, to provide students with tailored learning pathways, or assessment materials Intervention purposes, providing educators with information to intervene to support students Information visualization, typically in the form of so-called learning dashboards which provide overview learning data through data visualisation tools Ethics & Privacy The ethics of data collection, analytics, reporting and accountability has been raised as a potential concern for Learning Analytics (e.g.,), with concerns raised regarding: Data ownership Communications around the scope and role of Learning Analytics The necessary role of human feedback and error-correction in Learning Analytics systems Data sharing between systems, organisations, and stakeholders Trust in data clients Open Learning Analytics Chatti, Muslim and Schroeder note that the aim of Open Learning Analytics (OLA) is to improve learning effectiveness in lifelong learning environments. The authors refer to OLA as an ongoing analytics process that encompasses diversity at all four dimensions of the learning analytics reference model. Analytics: A Comprehensive Study 6. Predictive Analytics Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. 6. Predictive Analytics The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Definition Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Types Generally, the term predictive analytics is used to mean predictive modeling, “scoring” data with predictive models, and forecasting. However, people are increasingly using the term to refer to related analytical disciplines, such as descriptive modeling and decision modeling or optimization. These disciplines also involve rigorous data analysis, and are widely used in business for segmentation and decision making, but have different purposes and the statistical techniques underlying them vary. Types 1. Predictive Models Predictive models are models of the relation between the specific performance of a unit in a sample and one or more known attributes or features of the unit. The objective of the model is to assess the likelihood that a similar unit in a different sample will exhibit the specific performance. This category encompasses models in many areas, such as marketing, where they seek out subtle data patterns to answer questions about customer performance, or fraud detection models. Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision. With advancements in computing speed, individual agent modeling systems have become capable of simulating human behaviour or reactions to given stimuli or scenarios. Types 2. Descriptive Models Descriptive models quantify relationships in data in a way that is often used to classify customers or prospects into groups. Unlike predictive models that focus on predicting a single customer behavior (such as credit risk), descriptive models identify many different relationships between customers or products. Descriptive models do not rank-order customers by their likelihood of taking a particular action the way predictive models do. Instead, descriptive models can be used, for example, to categorize customers by their product preferences and life stage. Descriptive modeling tools can be utilized to develop further models that can simulate large number of individualized agents and make predictions. Types 3. Decision Models Decision models describe the relationship between all the elements of a decision—the known data (including results of predictive models), the decision, and the forecast results of the decision in order to predict the results of decisions involving many variables. These models can be used in optimization, maximizing certain outcomes while minimizing others. Decision models are generally used to develop decision logic or a set of business rules that will produce the desired action for every customer or circumstance. Analytical Customer Relationship Management (CRM) Analytical customer relationship management (CRM) is a frequent commercial application of predictive analysis. Methods of predictive analysis are applied to customer data to pursue CRM objectives, which involve constructing a holistic view of the customer no matter where their information resides in the company or the department involved. CRM uses predictive analysis in applications for marketing campaigns, sales, and customer services to name a few. These tools are required in order for a company to posture and focus their efforts effectively across the breadth of their customer base. They must analyze and understand the products in demand or have the potential for high demand, predict customers’ buying habits in order to promote relevant products at multiple touch points, and proactively identify and mitigate issues that have the potential to lose customers or reduce their ability to gain new ones. Analytical customer relationship management can be applied throughout the customers lifecycle (acquisition, relationship growth, retention, and win-back). Several of the application areas described below (direct marketing, cross-sell, customer retention) are part of customer relationship management. Clinical Decision Support Systems Experts use predictive analysis in health care primarily to determine which patients are at risk of developing certain conditions, like diabetes, asthma, heart disease, and other lifetime illnesses. Additionally, sophisticated clinical decision support systems incorporate predictive analytics to support medical decision making at the point of care. A working definition has been proposed by Jerome A. Osheroff and colleagues: Clinical decision support (CDS) provides clinicians, staff, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. It encompasses a variety of tools and interventions such as computerized alerts and reminders, clinical guidelines, order sets, patient data reports and dashboards, documentation templates, diagnostic support, and clinical workflow tools. Collection Analytics Many portfolios have a set of delinquent customers who do not make their payments on time. The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs. Cross-sell Often corporate organizations collect and maintain abundant data (e.g. customer records, sale transactions) as exploiting hidden relationships in the data can provide a competitive advantage. For an organization that offers multiple products, predictive analytics can help analyze customers’spending, usage and other behavior, leading to efficient cross sales, or selling additional products to current customers. This directly leads to higher profitability per customer and stronger customer relationships. Customer Retention With the number of competing services available, businesses need to focus efforts on maintaining continuous customer satisfaction, rewarding consumer loyalty and minimizing customer attrition.In addition, small increases in customer retention have been shown to increase profits disproportionately. One study concluded that a 5% increase in customer retention rates will increase profits by 25% to 95%. Businesses tend to respond to customer attrition on a reactive basis, acting only after the customer has initiated the process to terminate service. Direct Marketing When marketing consumer products and services, there is the challenge of keeping up with competing products and consumer behavior. Apart from identifying prospects, predictive analytics can also help to identify the most effective combination of product versions, marketing material, communication channels and timing that should be used to target a given consumer. The goal of predictive analytics is typically to lower the cost per order or cost per action. Fraud Detection Fraud is a big problem for many businesses and can be of various types: inaccurate credit applications, fraudulent transactions (both offline and online), identity thefts and false insurance claims. These problems plague firms of all sizes in many industries. Some examples of likely victims are credit card issuers, insurance companies, retail merchants, manufacturers, business-to-business suppliers and even services providers. A predictive model can help weed out the “bads” and reduce a business’s exposure to fraud. Portfolio, Product or Economy-level Prediction Often the focus of analysis is not the consumer but the product, portfolio, firm, industry or even the economy. For example, a retailer might be interested in predicting store-level demand for inventory management purposes. Or the Federal Reserve Board might be interested in predicting the unemployment rate for the next year. These types of problems can be addressed by predictive analytics using time series techniques. They can also be addressed via machine learning approaches which transform the original time series into a feature vector space, where the learning algorithm finds patterns that have predictive power. Project Risk Management When employing risk management techniques, the results are always to predict and benefit from a future scenario. The capital asset pricing model (CAP-M) “predicts” the best portfolio to maximize return. Probabilistic risk assessment (PRA) when combined with mini-Delphi techniques and statistical approaches yields accurate forecasts. These are examples of approaches that can extend from project to market, and from near to long term. Underwriting and other business approaches identify risk management as a predictive method. Underwriting Many businesses have to account for risk exposure due to their different services and determine the cost needed to cover the risk. For example, auto insurance providers need to accurately determine the amount of premium to charge to cover each automobile and driver. A financial company needs to assess a borrower’s potential and ability to pay before granting a loan. For a health insurance provider, predictive analytics can analyze a few years of past medical claims data, as well as lab, pharmacy and other records where available, to predict how expensive an enrollee is likely to be in the future. Predictive analytics can help underwrite these quantities by predicting the chances of illness, default, bankruptcy, Technology and Big Data Influences Big data is a collection of data sets that are so large and complex that they become awkward to work with using traditional database management tools. The volume, variety and velocity of big data have introduced challenges across the board for capture, storage, search, sharing, analysis, and visualization. Examples of big data sources include web logs, RFID, sensor data, social networks, Internet search indexing, call detail records, military surveillance, and complex data in astronomic, biogeochemical, genomics, and atmospheric sciences. Big Data is the core of most predictive analytic services offered by IT organizations. Analytical Techniques The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques. Regression Techniques Regression models are the mainstay of predictive analytics. The focus lies on establishing a mathematical equation as a model to represent the interactions between the different variables in consideration. Depending on the situation, there are a wide variety of models that can be applied while performing predictive analytics. Some of them are briefly discussed below. Linear Regression Model The linear regression model analyzes the relationship between the response or dependent variable and a set of independent or predictor variables. This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters. These parameters are adjusted so that a measure of fit is optimized. Much of the effort in model fitting is focused on minimizing the size of the residual, as well as ensuring that it is randomly distributed with respect to the model predictions. Discrete Choice Models Multivariate regression (above) is generally used when the response variable is continuous and has an unbounded range. Often the response variable may not be continuous but rather discrete. While mathematically it is feasible to apply multivariate regression to discrete ordered dependent variables, some of the assumptions behind the theory of multivariate linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis. If the dependent variable is discrete, some of those superior methods are logistic regression, multinomial logit and probit models. Logistic regression and probit models are used when the dependent variable is binary. Logistic Regression In a classification setting, assigning outcome probabilities to observations can be achieved through the use of a logistic model, which is basically a method which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model. Multinomial Logistic Regression An extension of the binary logit model to cases where the dependent variable has more than 2 categories is the multinomial logit model. In such cases collapsing the data into two categories might not make good sense or may lead to loss in the richness of the data. The multinomial logit model is the appropriate technique in these cases, especially when the dependent variable categories are not ordered (for examples colors like red, blue, green). Some authors have extended multinomial regression to include feature selection/importance methods such as random multinomial logit. Probit Regression Probit models offer an alternative to logistic regression for modeling categorical dependent variables. Even though the outcomes tend to be similar, the underlying distributions are different. Probit models are popular in social sciences like economics. A good way to understand the key difference between probit and logit models is to assume that the dependent variable is driven by a latent variable z, which is a sum of a linear combination of explanatory variables and a random noise term. Logit Versus Probit The probit model has been around longer than the logit model. They behave similarly, except that the logistic distribution tends to be slightly flatter tailed. One of the reasons the logit model was formulated was that the probit model was computationally difficult due to the requirement of numerically calculating integrals. Modern computing however has made this computation fairly simple. The coefficients obtained from the logit and probit model are fairly close. However, the odds ratio is easier to interpret in the logit model. Practical reasons for choosing the probit model over the logistic model would be: There is a strong belief that the underlying distribution is normal The actual event is not a binary outcome (e.g., bankruptcy status) but a proportion (e.g., proportion of population at different debt levels). 7. Prescriptive Analytics Prescriptive analytics is the third and final phase of analytics (BA) which also includes descriptive and predictive analytics. Referred to as the “final frontier of analytic capabilities,” prescriptive analytics entails the application of mathematical and computational sciences suggests decision options to take advantage of the results of descriptive and predictive analytics. The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting - such as sales, marketing, operations, and finance - uses this type of post-mortem analysis.  The next phase is predictive analytics. Predictive analytics answers the question what is likely to happen. This is when historical data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. The final phase is prescriptive analytics, which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option. Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities. History While the term Prescriptive Analytics, first coined by IBM and later trademarked by Ayata, the underlying concepts have been around for hundreds of years. The technology behind prescriptive analytics synergistically combines hybrid data, business rules with mathematical models and computational models. The data inputs to prescriptive analytics may come from multiple sources: internal, such as inside a corporation; and external, also known as environmental data. The data may be structured, which includes numbers and categories, as well as unstructured data, such as texts, images, sounds, and videos. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. More than 80% of the world’s data today is unstructured, according to IBM. Pricing Pricing is another area of focus. Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geopolitics, and weather conditions. Gas producers, pipeline transmission companies and utility firms have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk. Prescriptive analytics software can accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and show the impact of each decision option. Applications in Healthcare Multiple factors are driving healthcare providers to dramatically improve business processes and operations as the United States healthcare industry embarks on the necessary migration from a largely fee-for service, volume-based system to a fee-for-performance, value-based system. Prescriptive analytics is playing a key role to help improve the performance in a number of areas involving various stakeholders: payers, providers and pharmaceutical companies. 8. Social Media Analytics Social Media Analytics as a part of social analytics is the process of gathering data from stakeholder conversations on digital media and processing into structured insights leading to more information-driven business decisions and increased customer centrality for brands and businesses. Social media analytics can also be referred as social media listening, social media monitoring or social media intelligence. Digital media sources for social media analytics include social media channels, blogs, forums, image sharing sites, video sharing sites, aggregators, classifieds, complaints, Q&A, reviews, Wikipedia and others.  Social media analytics is an industry agnostic practice and is commonly used in different approaches on business decisions, marketing, customer service, reputation management, sales and others. There is an array of tools that offers the social media analysis, varying from the level of business requirement. Logic behind algorithms that are designed for these tools is selection, data pre-processing, transformation, mining and hidden pattern evaluation. In order to make the complete process of social media analysis a success it is important that key performance indicators (KPIs) for objectively evaluating the data is defined. Social media analytics is important when one needs to understand the patterns that are hidden in large amount of social data related to particular brands. 9. Behavioral Analytics Behavioral analytics is a recent advancement in business analytics that reveals new insights into the behavior of consumers on eCommerce platforms, online games, web and mobile applications, and IoT. The rapid increase in the volume of raw event data generated by the digital world enables methods that go beyond typical analysis by demographics and other traditional metrics that tell us what kind of people took what actions in the past. Behavioral analysis focuses on understanding how consumers act and why, enabling accurate predictions about how they are likely to act in the future. It enables marketers to make the right offers to the right consumer segments at the right time.  Behavioral analytics utilizes the massive volumes of raw user event data captured during sessions in which consumers use application, game, or website, including traffic data like navigation path, clicks, social media interactions, purchasing decisions and marketing responsiveness. Also, the event-data can include advertising metrics like click-to-conversion time, as well as comparisons between other metrics like the monetary value of an order and the amount of time spent on the site. These data points are then compiled and analyzed, whether by looking at session progression from when a user first entered the platform until a sale was made, or what other products a user bought or looked at before this purchase. Behavioral analysis allows future actions and trends to be predicted based on the collection of such data.  Data shows that a large percentage of users using a certain eCommerce platform found it by searching for “Thai food” on Google. After landing on the homepage, most people spent some time on the “Asian Food” page and then logged off without placing an order. Looking at each of these events as separate data points does not represent what is really going on and why people did not make a purchase. However, viewing these data points as a representation of overall user behavior enables one to interpolate how and why users acted in this particular case. Behavioral analytics looks at all site traffic and page views as a timeline of connected events that did not lead to orders. Since most users left after viewing the “Asian Food” page, there could be a disconnect between what they are searching for on Google and what the “Asian Food” page displays. Knowing this, a quick look at the “Asian Food” page reveals that it does not display Thai food prominently and thus people do not think it is actually offered, even though it is. Types Ecommerce and retail – Product recommendations and predicting future sales trends Online gaming – Predicting usage trends, load, and user preferences in future releases Application development – Determining how users use an application to predict future usage and preferences. Cohort analysis – Breaking users down into similar groups to gain a more focused understanding of their behavior. Security – Detecting compromised credentials and insider threats by locating anomalous behavior. Suggestions – People who liked this also liked... Presentation of relevant content based on user behavior. Components of Behavioral Analytics An ideal behavioral analytics solution would include: Real-time capture of vast volumes of raw event data across all relevant digital devices and applications used during sessions Automatic aggregation of raw event data into relevant data sets for rapid access, filtering and analysis Ability to query data in an unlimited number of ways, enabling users to ask any business question Extensive library of built-in analysis functions such as cohort, path and funnel analysis A visualization component Subsets of Behavioral Analytics Path Analysis (Computing) Path analysis, is the analysis of a path, which is a portrayal of a chain of consecutive events that a given user or cohort performs during a set period of time while using a website, online game, or eCommerce platform. As a subset of behavioral analytics, path analysis is a way to understand user behavior in order to gain actionable insights into the data. Path analysis provides a visual portrayal of every event a user or cohort performs as part of a path during a set period of time. While it is possible to track a user’s path through the site, and even show that path as a visual representation, the real question is how to gain these actionable insights. If path analysis simply outputs a “pretty” graph, while it may look nice, it does not provide anything concrete to act upon. Examples In order to get the most out of path analysis the first step would be to determine what needs to be analyzed and what are the goals of the analysis. A company might be trying to figure out why their site is running slow, are certain types of users interested in certain pages or products, or if their user interface is set up in a logical way.