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University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnlineUniversity of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
2
12.07.2017
Welcome!
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Today’s AgendaTopic Presenter Length Start EndWelcome and Agenda Shahr Panahi :10 9:30 9:40Introduction/Keynote John Letchford :25 9:40 10:05Summit Status and Roadmap Shahr Panahi :40 10:05 10:45A&F Dashboard Discussion Lisa Calise :30 10:45 11:15Break :15 11:15 11:30BPR Update BPR Leads :25 11:30 11:55Tableau for Summit Bill Manteiga :15 11:55 12:10HelioCampus Introduction Lori Dembowitz :20 12:10 12:30Lunch (Provided Downstairs) 1:00 12:30 1:30Breakout 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 3:30Closing Remarks/Evaluation forms Shahr Panahi :10 3:30 3:40
MiddlesexEssexBerkshire
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnlineUniversity of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Roadmap
DECEMBER 2017
SUMMIT Summit
By Anju Sherpa - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=33073777
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Topics– What is Summit?– Where are we today:
• Architecture• Usage
– Where are we going• UMass Community of Data
Practitioners• Future Architecture (Draft)• Roadmap
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
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
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnlineUniversity of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
SUMMIT: Today’s Architecture
Summit Data MartsEDW
PS – SABDL
IR Census Mart
PS HCM PS Fin Buyways, Sunapsis, Equifax,
IDM,Other Sources
3rd
Part
y In
tegr
atio
n
Access Layer: OBIEE
Exte
rnal
Da
ta
Campus Marts
Met
adat
a La
yer
Campus Marts
TableauServer
TableauTableauTableauTableauTableauTableauServers
PS – SAAMHERST
PS – SAMEDICAL
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Where are we: Summit Usage and Statistics
- Increasing amount of data being queried (800 M rows per quarter)
- 17 Data Marts, 40 ++ dashboards
- Thousands of unique users per month
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
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
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
UMCDP
UMass Community of Data Practitioners
UM
ass
Com
mun
ity o
f Dat
a Pr
actit
ione
rs
Introducing UMass Community of Data Practitioners
UMASS COMMUNITY OF DATA PRACTITIONERS:
- Collection of BI / Analytics professionals across UMass
- Optimize collaboration across campuses and UITSNOW:- 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
AMH
BOS
CEN/UITS
DAR
LOW
MED
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
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
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnlineUniversity of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Analytics Engine Artificial Intelligence
Access Layer:Reporting and Analytics tools
Optimized and integrated for best user experience
Enterprise Data WarehouseHighly Cleansed Transformed Data
Future state enables big data, analytics, self service, mobile, and data exploration
Data LakeMassive Repository of Raw Data
In All Formats
Sources: All ERP, Cloud, On Premise, and External Data Sources
Metadata
Entire Stack Hosted on C
loud?Summit: Future BI/Analytics Architecture
Access Broker
Source Layer
Repository Layer
Marts Layer
Access Layer
DataIntegration
Other Web & Mobile
Applications
Specific Data Marts (Collection of Cleansed Data for Specific
Subjects)
Content Vendors(e.g. HelioCampus)
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
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
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Architecture Roadmap Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
2017 2018 2019 2020 2021
Oracle 12.2 / HW Upgrade
OBIEE 12c / HW Upgrade
Tableau Metadata Integration
HelioCampus Deployment (DL)
Enterprise Server (PaaS)
Cloud Host
OracleCloud?
More campus /Central deployments (ABWC) ??
PlaceholderPlannedMilestoneDecision
HelioCampus
EDW
OBIEE
Expand Central Server (PaaS?)
Vendor Acquisition & DeploymentData
Lake / Access / Auto-Discovery
Discovery Tool
AdvancedAnalytics
First Predictive Model Advanced Analytics DevelopmentSQL Server
POC
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
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 – …
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Basics: Optimizing Data Analysis and Delivery
Traditional
Data Discovery and Acquisition
Tim
e an
d Ef
fort
Data Cleansing and Transformation
Analysis
Information Delivery
Majority of the time and effort by analysts is spent on accessing and preparing data. This reduces time available for actual analysis!
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Basics: Optimizing Data Analysis and Delivery
Traditional
Data Discovery and Acquisition
Tim
e an
d Ef
fort
Data Cleansing and Transformation
Analysis
Information Delivery
Optimized
Tools to access data from disparate systems, raw data repositories (like data lakes), automated discovery tools will help access and discovery
- Simplify governed data access- Invest in metadata- Automate discovery and visualization- Make ALL data reachable for analysis
(Data Lake + Data Warehouse + …)
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Basics: Optimizing Data Analysis and Delivery
Traditional
Data Discovery and Acquisition
Tim
e an
d Ef
fort
Data Cleansing and Transformation
Analysis
Information Delivery
Optimized
- 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
- Simplify governed data access- Invest in metadata- Automate discovery and visualization- Make ALL data reachable for analysis
(Data Lake + Data Warehouse + …)
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Basics: Optimizing Data Analysis and Delivery
Traditional
Data Discovery and Acquisition
Tim
e an
d Ef
fort
Data Cleansing and Transformation
Analysis
Information Delivery
Optimized
- 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.
- Simplify governed data access- Invest in metadata- Automate discovery and visualization- Make ALL data reachable for analysis
(Data Lake + Data Warehouse + …)
- 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
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Basics: Optimizing Data Analysis and Delivery
Traditional
Data Discovery and Acquisition
Tim
e an
d Ef
fort
Data Cleansing and Transformation
Analysis
Information Delivery
Optimized
- Use EDW and marts where possible- Automate data wrangling- Standardize data definitions
- Simplify and automate deployment of interactive content and advanced analytics results
- Streamline user experience, add mobile
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.
- Simplify governed data access- Invest in metadata- Automate discovery and visualization- Make ALL data reachable for analysis
(Data Lake + Data Warehouse + …)
- 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
University of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnlineUniversity of MassachusettsAmherst Boston Dartmouth Lowell Worcester UMassOnline
Analytics Marts
Analytics Marts
Enterprise Data Warehouse
OBIEETableau
AnalyticsAI
Engine
Future state enables big data, analytics, self service, mobile, and data exploration
Data LakeAWS, Azure, Cloudera, Oracle or …?
Analytics Marts
Subject Marts
Advanced Analytics
LMS OtherERP Other CloudSalesForce
Metadata
Entire Stack Hosted on C
loud?Future BI/Analytics Architecture
Other BI Tools
Access Broker
Source Layer
Repository Layer
Marts Layer
Access Layer Mobile
DataIntegration
Other Web & Mobile
Applications
Web Integration for Seamless User Experience
Metadata
Content Vendor(s)
Content Vendor(s)
e.g. HelioCampus
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