Analytics: A Comprehensive Study LESSON 2 - Part 1 PDF

Analytics: A Comprehensive Study LESSON 2 - Part 1  Business Analytics Analytics Software Analytics Embedded Analytics Learning Analytics Predictive Analytics Prescriptive Analytics Social Media Analytics Behavioral Analytics 1. Business Analytics Business analytics (B...

Analytics: A Comprehensive Study LESSON 2 - Part 1  Business Analytics Analytics Software Analytics Embedded Analytics Learning Analytics Predictive Analytics Prescriptive Analytics Social Media Analytics Behavioral Analytics 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.