Introduction To Analytics 2022-2023 PDF

INTRODUCTION TO ANALYTICS 2022 - 2023  LESSON 3. ANALYTICS PRIMER: GOALS, APPLICATIONS & TYPES OF ANALYTICS  Learning Objectives 1. Articulate goals of analytics 2. Recognize how analytics is used in real world 3. Justify business benefits of analytics 4. Identify anal...

INTRODUCTION TO ANALYTICS 2022 - 2023  LESSON 3. ANALYTICS PRIMER: GOALS, APPLICATIONS & TYPES OF ANALYTICS  Learning Objectives 1. Articulate goals of analytics 2. Recognize how analytics is used in real world 3. Justify business benefits of analytics 4. Identify analytics application in different domains 5. Distinguish between descriptive, diagnostic, predictive and prescriptive analytics 6. Practice asking analytics questions Agenda 1. Goals, applications & benefits of analytics 2. Analytics domains; future trends 3. Types of analytics 4. Analytics questions  ANALYTICS GOALS, APPLICATIONS, DOMAINS What do we do with data? Discuss the article given out as home assignment. What data was collected? What was done with the collected data? How was it used?  Goal of analytics Analytics: The examination of information to uncover insights that give a businessperson the knowledge to make informed decisions. https://www.ibm.com/developerworks/community/blogs/jfp/entry/the_analytics_maturity_model?lang=en Benefits of Analytics Textbook Chapter 2 Figure 2.1 Using analytics Using analytics Using analytics Using analytics Analytics Applications Examples Recommender systems - based on previous search results for a user Targeted advertising - targeted based on user’s past behavior; social media analytics Speech recognition – sound patterns are compared to find best match; predict intention of speech Typing autocomplete – predicting the words based on the context and first letters Gaming - algorithms which improve / upgrade themselves as the player moves up to a higher level Fraud and risk detection – likelihood to default on loan is calculated based on customer profile Logistics - best routes to ship, best time to deliver, best mode of transport Sports cameras – real-time prediction of game direction and switching cameras  Analytics Domains Marketing: Security: Targeted marketing Spam filtering Online advertising Suspicious behaviour detection Cross-selling recommendations Data access pattern monitoring Customer relationship: Human resources: Reduce attrition Performance measurement Predict customer need: next best action Employee satisfaction Detect or predict problems Hiring and retention Retail: Finance: Supply chain management Credit scoring Demand prediction Trading Market basket analysis Fraud detection Analytics Trends Self-driving cars Sentiment analysis Face recognition Natural language processing Robots Endless…. TYPES OF ANALYTICS  Types of Analytics Descriptive The examination of data or content, to answer the question Analytics “What happened?” or “What is happening?” Diagnostic A form of advanced analytics that examines data or content to analytics answer the question, “Why did it happen?” Predictive A form of advanced analytics which examines data or content Analytics to answer the question “What is going to happen?” or more precisely, “What is likely to happen?” Prescriptive A form of advanced analytics which examines data or content Analytics to answer the question “What should be done?” or “What can we do to make X happen?” https://www.gartner.com/en/information-technology/glossary Analytics Value and Focus Traditional (Descriptive) Analytics 2. Infer why has it occurred 1. Monitor what has occurred Descriptive Analytics Questions “What happened?”, “What is happening?” Goals Make data from multiple data sources visible to human decision makers; describe data samples through summarization and visualization. Describes the world as it is. Provides insights into the past and present Tools Business intelligence (BI) and visualization tools: Spreadsheets, pie charts, bar charts, line graphs, tables, OLAP, dashboards and reports, data exploration and visualization Examples Google Analytics Diagnostic Analytics Questions “Why did it happen?”, “What are the trends?”, ”What patterns are there?” Goals Measure historical data, compare it to other data to find patterns, dependencies and identify trends. Provides insights into the past. Tools Drill-down, data discovery, data mining and correlations Examples Market segments, sentiment analysis, root cause for failures, IT log analytics, price elasticity, fraud patterns, scientific research Advanced Analytics 2. Determine the best course of action 1. Predict and forecast future Predictive Analytics Questions “What is likely to happen?” Goals Examine findings of descriptive and diagnostic analytics to predict new data about present and future. Tools Regression analysis, forecasting, multivariate statistics. Unique algorithms are created from data sets to build predictive models Examples Predict customer behavior (buy propensity), customer churn propensity (likelihood that a customer leaves for a competitor), fraud score (likelihood of a credit card transaction to be fraudulent), extract meaning from free text (what is the most likely meaning) Prescriptive Analytics Questions “What should we done?” or “What can we do to make it happen?” Goals Prescribe what action to take to eliminate a future problem or take advantage of an opportunity. Determine what actions should be executed now (operational decisions) or in the future (tactical and strategic decisions). Tools Graph analysis, optimization, simulation, complex event processing, neural networks, recommendation engines, heuristics. Leverages big data, algorithms ands business rules. Relies on machine learning and deep learning. Examples Demand forecast, inventory management Cognitive Analytics – the next level Cognitive The future state of AI analytics Human-like decision-making Advanced machine intelligence with self-learning capabilities Self- Draw inferences from existing data and patterns learning Draw conclusions based on existing knowledge Add new conclusions (new knowledge) back into the knowledge base for future inferences  Later in the program Quantitative Research Using quantitative methods to generate descriptive statistics Methods Course Predictive Analytics Methods applied to create predictive models Course Types of Analytics: let’s check our understanding A. Descriptive B. Diagnostic C. Predictive D. Prescriptive ANALYTICS QUESTIONS The importance of asking the right questions The importance of asking the right questions https://www.linkedin.com/feed/update/urn:li:activity:6887906139858591744/ How to ask analytics questions? Work in groups: A streaming platform has collected a lot of data about user behaviour on the platform, as well as customer surveys. What analytics questions could we ask to improve the platform movie recommendations algorithm?  Descriptive Analytics Cumulative (counter, Number of products sold last month, monthly data usage volume) Total followers, Total rainfall Delta (change) Sales growth Defect reduction Gauge (value measured CPU usage at a moment in time) Speed Sound volume Rate of occurrence Infections per million population Accidents per million miles driven Descriptive statistics Central tendency (mean, median, mode), Measures of variation and distribution  Descriptive Analytics Aggregate Percentage of customers by age group, income level or gender analysis Total products sold in each product category Profitability of each product category Trend analysis Sales trends Customer growth trend Stock price trend Contribution Contribution to sales volume by product category analysis Movies watched by customer by genre Key Customer retention rate performance indicators Net profit Employee satisfaction score 25 Need-To-Know Key Performance Indicators (Bernard Marr, 2014) FT Press  Diagnostic Analytics Correlation Correlation between two observations (positive or negative correlation) analysis Hypothesis Prove or disprove an assumption e.g. identify likely reasons behind customer testing churn increase, product popularity, or customer dissatisfaction Regression Relationship between two or more variables e.g. number of accidents vs. rainfall analysis vs. time of the day Cohort analysis Comparison of metrics of different cohorts (groups) e.g. spending patterns, reading preferences by demographic group  Good Analytics Questions Clear Was there any difference in how many sales we’ve made last year considering that we have added two new products and including each country that we exited, except for our leading products? Specific What did we do well last year? Use accurate terms How many widgets were produced lately? Feasible to get How many people will live on Mars by 2300? answered The answer can be What party is most likely to be in power in used 2068?