Mainstreaming BI and Analytics with Enterprise Data

Unification

Shobhit Chugh | Tamr

Data Heterogeneity is Inherent in Large Companies

Data sources are bound to applications with idiosyncratic bias

Sales

Marketing

Manufacturing

HR

Support

Finance

AppsStoreApps Store

Sales

Marketing

Manufacturing

HR

Support

Finance

Aggregation of Data Creates Ambiguity/Complexity

Broad analytics create need to bring data together from many sources

Outside Forces = More Confusion + Complexity

Leadership

Changes

Mergers &

AcquisitionsReorganizations

Result: Just 10% of Data is Consumable by Any One Person

And 80% of data scientist time is spent preparing it

90%Dark Data

Expectations for Global Corporate IT as Data Broker

Increasing quickly -- along with the hype about Big Data/Analytics 3.0

HR

Sales

FinanceDivisions

Marketing MFG

ENG

Some Options

Option #1 - Deny Variety - use information that is easiest/closest

Option #2 - Manage Variety incrementally - using traditional approaches:

● Standardization

● Aggregation

● Master Data Management

● Rationalize Systems

● Throw Bodies at it

● Improve Individual Productivity

Option #3 - Embrace Variety using probabalistic/model based approach - Tamr

Logical Evolution to Probabilistic/Model-Based Approach

Probabilistic

Deterministic

Probabilistic

Deterministic

Today Future

Probabilistic (Tamr) complements, NOT Replaces, Deterministic (MDM)

INTRODUCING TAMR

▪ Founded in 2013 by

enterprise database software

veterans

▪ World-class engineering team

▪ Top tier venture backing

(Google Ventures, NEA)

Jerry Held, PhD

Andy Palmer Mike Stonebraker,PhD

Ihab Ilyas, PhD

Kevin Burke Nidhi Aggarwal, PhD

Min Xiao Nik Bates-Haus

Kevin Willis

9

Managing enterprise information as an asset requires a new,

bottom-up design pattern

Catalog Connect Consume

ALL your metadata and

map it to logical entitiesEntities and attributes to

remove information silos

Unified data in the application

of your choice via APIs

“Embrace” Variety -- Tamr’s NextGen Approach

Tamr’s Design Pattern: “Back to the Future”

1990’s Web:Yahoo’s top-down

organization

2020’s Enterprise:Probabilistic data source cataloging,

connection and consumption

12

ARCHITECTURE

DATA &

METADAT

A

SOURCES

Analytics,

visualization,

Data Warehouse

Expert Sourcing

Data

Profiling

Schema

Matching

Record

Deduplication

Data Connection Activities

Data

Security

Data

Governance

Machine Learning

DB, ERP,

CRM, CSV

+ DATA

USES

Data

Security

Fortune 50 company -- Optimized Sourcing Analysis

Benefits● Massive reductions in

supplier list size & number

of distinct suppliers

● Automated data

maintenance; lower cost

of ownership

● Powering strategic

sourcing analytics and

governance

● Empowering individual

procurement team with

global view of payment

terms

Catalog

Tamr helps you catalog

metadata across the entire

enterprise, providing a logical

map of all of your information

Find us at Booth #613

Connect

Tamr helps match entities

and attributes across the

full variety of your sources,

leveraging entity relationships

for high accuracy

Consume

Tamr provides a consolidated

view of entities and records for

downstream applications via

a set of RESTful APIs

learn more at tamr.com

Find us at Booth #613

ABSTRACT (FOR REFERENCE)

Organizations want to use all the data available to them for analytics. But they’ve been thwarted by

data silos and top-down, mostly manual approaches to unifying data for analytics. A new approach,

based on machine learning combined with human expert sourcing, dramatically speeds analytics’

time-to-value. It automates data unification end-to end: from finding and connecting diverse data to

interactive consumption by virtually anyone using any analytic tool.

20 MINUTES => 15 SLIDES