Business Analytics and Big Data 1 Application Case Eliminating Inefficiencies at Seattle Children’s • Background • 7th Highest ranked children’s hospital in US (2011) • Continuously looking for improvement • Problem • Spent days to weeks for developing simple dashboard • Solution • Implement Tableau (BI application) • Provide browser-based, easy-to-use analytics • Allows individuals to create visualization to understand what the data means 2 Application Case Eliminating Inefficiencies at Seattle Children’s • Results • Data analysts, Business Managers, Financial Analysts, Clinicians, Doctors, and Researchers are all using Tableau • Solve different problems that they could not do before • Significantly improve day-to-day decision making via dashboards • Discovered ways to “virtually increase beds” and treat more patients • Saved more than $3 million • Lesson Learn • Visualization helps getting insight and root cause of problem • Users in different domains can contribute toward process and quality improvement 3 Business Analytics Overview • Analytics is the process of developing actionable decisions or recommendation for actions based upon insights generated from historical data • Analytics makes extensive use of data, statistical and quantitative analysis, 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 4 Business Analytics Overview • 3 levels/categories of analytics • Descriptive (or Reporting) analytics • Predictive analytics • Prescriptive analytics What happened? Descriptive Analytics (Past) How can we make it happen? Predictive Analytics (Future) Prescriptive Analytics (Decision) What will happen? 5 Business Analytics Overview • Descriptive Analytics • The most traditional of business analytics is descriptive analytics, and it accounts for the majority of all current business analytics • Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure • Example • Almost all management reporting such as sales, marketing, operations, and finance, uses this type of post-mortem analysis 6 Business Analytics Overview • Predictive Analytics • Historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or a likelihood of a situation occurring • Examples • Predictive Models in banking industry is widely developed to bring certainty across the risk scores for individual customers. Credit Scores are built to predict individual’s behavior and also scores are widely used to evaluate the credit worthiness of each applicant and rated while processing loan applications • Used in health care to determine which patients are at risk of developing certain conditions, like diabetes, asthma, heart disease, and other lifetime illnesses 7 Business Analytics Overview • Prescriptive Analytics • Prescriptive analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker 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 • Prescriptive analytics can suggest decision options on how to take advantage of a future opportunity or mitigate a future risk and illustrate the implication of each decision option • Prescriptive analytics can continually and automatically process new data to improve prediction accuracy and provide better decision options 8 Business Analytics Overview • Analytics Applied to Different Domains • Text analytics – aimed at getting value out of text • Web analytics – analyzing Web data streams • Many industry-specific / problem-specific analytics • E.g., marketing analytics, retail analytics, fraud analytics, transportation analytics, health analytics, sports analytics, talent analytics, behavioral analytics, bank analytics, insurance analytics • May result in overselling the concepts of analytics • Differentiation of analytics based on a vertical focus is good for overall growth of the discipline 9 Business Analytics Overview 10 Big Data • Big Data • Big data is a collection of data sets so large and complex that it becomes difficult to process using traditional data processing tools and applications • Challenges include capture, curation, storage, search, sharing, analysis, and visualization • The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to spot business trends, determine quality of research, prevent diseases, combat crime, and determine real-time roadway 11 Big Data • Characteristics of Big Data – Vs • Volume • Amount of data is enormous • Velocity • Data are generated very fast, often faster than the ability to process them • Variety • Multimedia data constitute an important data variety 12 Big Data • Additional Vs from leading Big Data solution providers • Veracity • Captured data quality can vary greatly, affecting accurate analysis • Variability • Data flows can be highly inconsistent • Value • Usefulness of the data 13 Big Data • 1V → 3Vs → 6Vs → more Vs? 14 THE END 15
