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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


Roadmap

DECEMBER 2017

SUMMIT Summit

By Anju Sherpa - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=33073777


Topics– What is Summit?– Where are we today:

• Architecture• Usage

– Where are we going• UMass Community of Data

Practitioners• Future Architecture (Draft)• Roadmap


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


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


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


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


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


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


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)


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


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


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 – …


Appendix


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!



Traditional


Tim

e an

d Ef

fort


Analysis


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 + …)



Traditional


Tim

e an

d Ef

fort


Analysis


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





Traditional


Tim

e an

d Ef

fort


Analysis


Optimized


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.



- 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



Traditional


Tim

e an

d Ef

fort


Analysis


Optimized


- 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.



- 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


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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