University of Massachusetts Amherst Boston Dartmouth Lowell Worcester

Transcription

1 Amherst Boston Dartmouth Lowell Worcester UMassOnline Welcome!

2 Today s Agenda Topic Presenter Length Start End Welcome and Agenda Shahr Panahi :10 9:30 9:40 Introduction/Keynote John Letchford :25 9:40 10:05 Summit Status and Roadmap Shahr Panahi :40 10:05 10:45 A&F Dashboard Discussion Lisa Calise :30 10:45 11:15 Break :15 11:15 11:30 BPR Update BPR Leads :25 11:30 11:55 Tableau for Summit Bill Manteiga :15 11:55 12:10 HelioCampus Introduction Lori Dembowitz :20 12:10 12:30 Lunch (Provided Downstairs) 1:00 12:30 1:30 Breakout Sessions :HR Carol Dugard, HR Attendees 1:30 1:30 3:00 :Finance John Munroe, Finance Attendees 1:30 1:30 3:00 :Student Jeff Glatstein, Student Attendees 1:30 1:30 3:00 Report back from Breakouts All :30 3:00 3:30 Closing Remarks/Evaluation forms Shahr Panahi :10 3:30 3:40 Middlesex Essex Berkshire

3 SUMMIT Summit University of Massachusetts Amherst Boston Dartmouth Lowell Worcester Roadmap UMassOnline DECEMBER 2017 By Anju Sherpa - Own work, CC BY-SA 3.0,

4 Topics What is Summit? Where are we today: Architecture Usage Where are we going UMass Community of Data Practitioners Future Architecture (Draft) Roadmap

5 What is Summit? - UMass Business Intelligence (BI) and Analytics program. - The main objective of Summit is to facilitate and promote data-centric decision making by: Deploying solutions that transform raw data into actionable information; Providing access to that information to decision makers; Championing data governance across the university; Supporting BI / Analytics community s data, information, and technology needs. UMass Enterprise Data Architecture Enterprise Data Warehouse Enterprise Reporting and Analysis tools (OBIEE, Tableau, etc.) Data Integration for Analysis Tools that transform and move data Data Access for all analysts Future technologies such as data lake, access brokers, Analytics marts

6 Amherst Boston Dartmouth Lowell Worcester UMassOnline SUMMIT: Today s Architecture Tableau Tableau Tableau Tableau Tableau Servers Access Layer: OBIEE Tableau Server External Data Campus Campus Marts Marts IR Census Mart Metadata Layer Summit Data Marts EDW 3rd Party Integration PS SA AMHERST PS SA MEDICAL PS SA BDL PS HCM PS Fin Buyways, Sunapsis, Equifax, IDM, Other Sources

7 Where are we: Summit Usage and Statistics - Increasing amount of data being queried (800 M rows per quarter) - 17 Data Marts, dashboards - Thousands of unique users per month

8 How do we as a university system maximize potential of data analytics in the most efficient and productive way? People, Organization, Processes Position UMass resources on campus and centrally for maximum efficiency and productivity Invest in analysts on campus to answer business questions using data Centralize where it can lead to efficiencies Technology and Architecture Modernize UMass enterprise data architecture Take advantage of new technologies, cloud hosting, best practices in analytics BI / Analytics Content Build / buy analytics content to support UMass strategic direction both on campus and as a system Care for campus as well as system data and analytics needs

9 Introducing UMass Community of Data Practitioners UMass Community of Data Practitioners UMASS COMMUNITY OF DATA PRACTITIONERS: - Collection of BI / Analytics professionals across UMass - Optimize collaboration across campuses and UITS NOW: - Share knowledge; - Collaborate online - Organize events FUTURE: - Project and research opportunities for faculty and students - Budgeted through grants where possible - Cooperate with higher ed. Institutions across the commonwealth and beyond - Invest in and provide access to new technologies around data - Support open source innovation - Involved in data governance and strategy for the enterprise - Kaggle like analytics contests and scholarships on real life UMass business questions UMass Community of Data Practitioners MED LOW AMH UMCDP DAR BOS CEN/ UITS

10 Future Summit Architecture Guiding Principles Facilitate ease of access to all available data for mining and analysis Strive to make transforming data into actionable information,including advanced analytics simple and repeatable Accommodate purchased analytics solutions / hosts Support governance, standardization, and access security Care for agility and self service in developing BI / Analytics content Take advantage of latest technology and cloud hosting Pay special attention to user experience, make mobile available Ensure proper buy-in and support from UMass BI / Analytics community Capitalize on the community and engage faculty and student practitioners Prioritize based on utility and benefit to the entire enterprise (rather than single campus) Maximize use of existing investments and minimize redoing work that has already been done

11 Summit: Future BI/Analytics Architecture University of Massachusetts Amherst Boston Dartmouth Lowell Worcester Future state enables big data, analytics, self service, mobile, and data exploration UMassOnline Access Layer Marts Layer Repository Layer Source Layer Access Broker Data Integration Content Vendors (e.g. HelioCampus) Data Lake Massive Repository of Raw Data In All Formats Access Layer: Reporting and Analytics tools Optimized and integrated for best user experience Analytics Engine Artificial Intelligence Specific Data Marts (Collection of Cleansed Data for Specific Subjects) Metadata Enterprise Data Warehouse Highly Cleansed Transformed Data Sources: All ERP, Cloud, On Premise, and External Data Sources Other Web & Mobile Applications Entire Stack Hosted on Cloud?

12 What is a Data Lake Able to store vast quantities of data in raw format It stores data of different types: Database tables Log files (i.e. web log information) Binary files (i.e. pictures, voice, etc.) Other It can be Hadoop based, RDBMS based or both Used for Advanced analytics as well as quick access to data (don t have to wait for data to get to EDW before using) Best practice* is to implement alongside enterprise data warehouse * According to TDWI, Gartner, Cloudera

13 Architecture Roadmap Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Data Lake / Access / Auto-Discovery Discovery Tool Vendor Acquisition & Deployment Advanced Analytics SQL Server POC First Predictive Model Advanced Analytics Development Helio Campus HelioCampus Deployment (DL) More campus /Central deployments (ABWC)?? EDW Oracle 12.2 / HW Upgrade Cloud Host OBIEE OBIEE 12c / HW Upgrade Oracle Cloud? Tableau Metadata Integration Expand Central Server (PaaS?) Enterprise Server (PaaS) Decision Milestone Planned Placeholder

14 Content Roadmap Some key upcoming content: A&F Executive Dashboard A&F Executive Dashboard: Campus Detail System IR Data Mart Deans dashboard HelioCampus Visualizations Student Success Appliance Predictive models

15 Appendix University of Massachusetts

16 Basics: Optimizing Data Analysis and Delivery Information Delivery Analysis Majority of the time and effort by analysts is spent on accessing and preparing data. This reduces time available for actual analysis! Data Cleansing and Transformation Time and Effort Data Discovery and Acquisition Traditional

17 Basics: Optimizing Data Analysis and Delivery Information Delivery Analysis Data Cleansing and Transformation Time and Effort Tools to access data from disparate systems, raw data repositories (like data lakes), automated discovery tools will help access and discovery Data Discovery and Acquisition - Simplify governed data access - Invest in metadata - Automate discovery and visualization - Make ALL data reachable for analysis (Data Lake + Data Warehouse + ) Traditional Optimized

18 Basics: Optimizing Data Analysis and Delivery Information Delivery Analysis Data Cleansing and Transformation Time and Effort - Use EDW and marts where possible - Automate data wrangling - Standardize data definitions The EDW already has huge amounts of cleansed data; subject specific marts can help in automating data transformation and cleansing Data Discovery and Acquisition - Simplify governed data access - Invest in metadata - Automate discovery and visualization - Make ALL data reachable for analysis (Data Lake + Data Warehouse + ) Traditional Optimized

19 Basics: Optimizing Data Analysis and Delivery Information Delivery Analysis Data Cleansing and Transformation Time and Effort - Enable best of breed BI tools - Invest in data visualization - Open source and other advanced analytics - Analytics marts and servers - Facilitate self service on campus - Use EDW and marts where possible - Automate data wrangling - Standardize data definitions We will have more time to do analytics, and more time to spend with business owners, defining questions and refining results. Where feasible, we can buy rather than build. Campus (and central) analysts can spend more time on the actual analysis. Data Discovery and Acquisition - Simplify governed data access - Invest in metadata - Automate discovery and visualization - Make ALL data reachable for analysis (Data Lake + Data Warehouse + ) Traditional Optimized

20 Basics: Optimizing Data Analysis and Delivery Information Delivery Analysis Data Cleansing and Transformation Time and Effort - Simplify and automate deployment of interactive content and advanced analytics results - Streamline user experience, add mobile - Enable best of breed BI tools - Invest in data visualization - Open source and other advanced analytics - Analytics marts and servers - Facilitate self service on campus - Use EDW and marts where possible - Automate data wrangling - Standardize data definitions By automating the delivery of analytics results: interactive dashboards, visualizations, and/or model scores, we are able further optimize the process. Pay attention to overall user experience. Data Discovery and Acquisition - Simplify governed data access - Invest in metadata - Automate discovery and visualization - Make ALL data reachable for analysis (Data Lake + Data Warehouse + ) Traditional Optimized

21 Future BI/Analytics Architecture University of Massachusetts Amherst Boston Dartmouth Lowell Worcester Future state enables big data, analytics, self service, mobile, and data exploration UMassOnline Access Layer Marts Layer Repository Layer Source Layer Access Broker Tableau Data Lake OBIEE Analytics AI Engine AWS, Azure, Cloudera, Oracle or? Data Integration Content Content Vendor(s) Vendor(s) e.g. HelioCampus SalesForce Web Integration for Seamless User Experience ERP Analytics Marts Analytics Marts Mobile Other Cloud Metadata Analytics Marts Subject Marts Other BI Tools Advanced Analytics Enterprise Data Warehouse LMS Metadata Other Other Web & Mobile Applications Entire Stack Hosted on Cloud?