Business Analytics
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The document discusses business analytics and decision making. It defines key concepts like data warehousing, data mining, business intelligence, descriptive analytics, predictive analytics, and prescriptive analytics. It explains how these concepts are used to extract insights from data to support decision making in organizations. Examples of how different types of analytics can be applied in a retail context are provided.
Business Analytics
- 2. Data Where house Data Mining Business Analytics Business Intelligence Decision Making
- 3. Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel?
- 4. 4 I can’t find the data I need – data is scattered over the network – many versions, subtle differences I can’t get the data I need need an expert to get the data I can’t use the data I found results are unexpected data needs to be transformed from one form to other
- 5. 5 5 A single, complete and consistent store of data obtained from a variety of different sources made available to end users in a what they can understand and use in a business context.
- 6. 6 Data Warehouse: (W.H. Immon) A subject-oriented, integrated, time-variant, non-updatable collection of data used in support of management decision-making processes Subject-oriented: e.g. customers, patients, students, products Integrated: Consistent naming conventions, formats, encoding structures; from multiple data sources Time-variant: Can study trends and changes Non-updatable: Read-only, periodically refreshed
- 8. 8 Data is categorized and stored by business sub rather than by application Equity Plans Shares Customer financial information Savings Insurance Loans OLTP Applications Data Warehouse Subject
- 9. 9 Constructed by integrating multiple, heterogeneous data sources ◦ relational databases, flat files, on-line transaction records Data cleaning and data integration techniques are applied. ◦ Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources E.g., Hotel price: currency, tax, breakfast covered, etc. ◦ When data is moved to the warehouse, it is converted.
- 10. 10 The time horizon for the data warehouse is significantly longer than that of operational systems. ◦ Operational database: current value data. ◦ Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) Every key structure in the data warehouse ◦ Contains an element of time, explicitly or implicitly ◦ But the key of operational data may or may not contain “time element”.
- 11. 11 Data is stored as a series of snapshots, each representing a period of time Time Data Jan-97 January Feb-97 February Mar-97 March
- 12. 12 A physically separate store of data transformed from the operational environment. Operational update of data does not occur in the data warehouse environment. ◦ Does not require transaction processing, recovery, and concurrency control mechanisms. ◦ Requires only two operations in data accessing: initial loading of data and access of data.
- 13. 13 Typically data in the data warehouse is not updated or delelted. Insert Update Delete Read Read Operational Warehouse Load
- 14. 14 A process of transforming data into information and making it available to users in a timely enough manner to make a difference The process of constructing and using a data warehouseData Information
- 15. DM is a way to develop the intelligence from data which organization collects, organize and stores to gain better understanding of their customers, operations and to solve the organizational problems. Latest strategic weapons are : Decision making based on analytics To understand the customer better To optimize the supply chain to maximize their return on investments
- 16. DM was originally used to describe the process through which previously unknown patterns in data were discovered Used to describe or discover or “Mining” the “Knowledge” from the large amount of data. It include : 1. Knowledge Mining 2. Knowledge Discovery 3. Pattern Searching/Analysis 4. Find out correlation b/w data 5. To find out Trends in business 6. Prediction for the future
- 17. DM relatively new term but roots are in traditional analysis and statistical methods DM is positioned at intersection of 1. Artificial Intelligence 2. Machine Learning 3. Mathematical/Statistical Modeling 4. Management Science and Information System 5. Data Bases Works on Client/Server Architecture or web based Information System New tool for visualization To find out the unexpected results.
- 19. Customer Relationship Management Banking Insurance Manufacturing and Production Government and Defense Travel Industry Health Care Medicine Entertainment Securities Sports.
- 20. Analytics is the use of: data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models To help managers gain improved insight about their business operations and make better, fact-based decisions.
- 21. Business Analytics The use of analytical methods: 1. Either manually or automatically 2. To derive relationships from data 3. It include the access & reporting Analysis of data supported by software to drive business performance and decision making
- 22. Information, Analysis And Decisions: The Basics Analysis Information Why did it happens What is Happening What is Likely to happen What should I do about you ? Descriptive Analysis Diagnostics Analytics Predictive Analytics Prescriptive Analytics Analytic excellence leads to better decisions
- 25. OLAP: On Line Analytical Processing : An information system that enables the user, while at a PC, to query the system, conduct an analysis, and so on. The result is generated in seconds. OLTP: Online Transaction Processing : OLTP concentrates on processing repetitive transactions in large quantities and conducting simple manipulations Ad hoc Queries & Reports: A query that cannot be determined prior to the moment the query is issued. (On Demand Report). Routine Reports like Schedule report, Summary Report, Key Indicator, Annual Report > Data/Text/Web Mining & Search Engine:
- 26. EIS: Executive information systems (EIS) Provides rapid access to timely and relevant information as well as monitoring an organization’s performance Executive support systems (ESS) Also provides analysis support, communications, office automation, and intelligence support Management Science and Statistical Analysis Data Mining & Predictive Analysis: analyzes past performance Business Performance Management
- 27. A graphical, animation, or video presentation of data and the results of data analysis: The ability to quickly identify important trends in corporate and market data can provide competitive advantage Check their magnitude of trends by using predictive models that provide significant business advantages in applications that drive content, transactions, or processes
- 30. Score Cards: Focuses on a given metric and compares it to a forecast or target Dash Board: Visual Similarities to a car Dash Board. Provide graphical depiction of current key performance indicators in order to get the faster response to change the areas such as sales, customer relation, performance assessment on a single screen. “Score cards” and “Dash Boards” are often used interchangeably .
- 31. There is a strong relationship of BA with: - profitability of businesses - revenue of businesses - shareholder return BA enhances understanding of data BA is vital for businesses to remain competitive BA enables creation of informative reports
- 32. Descriptive analytics: Uses data to understand past and present Predictive analytics Analyzes past performance Prescriptive analytics Uses optimization techniques
- 33. Example: Retail Markdown Decisions Most department stores clear seasonal inventory by reducing prices. The question is: When to reduce the price and by how much? Descriptive analytics: examine historical data for similar products (prices, units sold, advertising, …) Predictive analytics: predict sales based on price Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue
- 34. Performance management systems (PMS) are BI tools that provide scorecards and other relevant information that decision makers use to determine their level of success in reaching their goals
- 35. Business Intelligence is an umbrella that combines 1. architectures 2. analytical tools 3. databases 4. applications and methodologies. Business Intelligence is transformation of data to information, then to decisions and finally to action. Business intelligence; major objective is to enable the real time access, manipulation, appropriate analysis of data. By providing current data, situations and performance to decision maker so that they get valuable insight to make powerful decision.
- 36. Business intelligence portals Querying & report Work flow ETL Data where house data marts
- 37. The Data/information explosion is the rapid increase in the amount of published information or data and the effects of this abundance. As the amount of available data grows, the problem of managing the information becomes more difficult, which can lead to information overload.
- 38. From clicks to likes, today's networked world is creating vast amounts of data at a significantly increasing rate. Combine that with exponentially increasing internal data and you get the data explosion commonly known as Big Data. The ability to translate this vast array of structured and unstructured input into usable business intelligence provides a key competitive advantage for technology companies.