A Better Understanding: Solving Business Challenges with Data

Grab some
coffee and
enjoy the
pre-show
banter
before the
top of the
hour! !

The Briefing Room
A Better Understanding: Solving Business Challenges with Data

Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
@eric_kavanagh

u  Reveal the essential characteristics of enterprise
software, good and bad
u  Provide a forum for detailed analysis of today s innovative
technologies
u  Give vendors a chance to explain their product to savvy
analysts
u  Allow audience members to pose serious questions... and
get answers!
Mission

Topics
December: INNOVATORS
January: ANALYTICS
February: BIG DATA

Quality First?
u  Garbage in, garbage
out
u  Big garbage in, big
garbage out
u  Golden record is
pure gold
u  A future in the
Cloud?

Analyst
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
@robinbloor

Experian Data Quality
u  Experian Data Quality offers a comprehensive suite of
data quality solutions, including cleansing,
standardization, matching, monitoring, enrichment
and profiling
u  Its real-time address verification helps maintain
accurate customer information for name, physical
address, email and phone
u  Experian Pandora allows businesses to prototype data
quality rules and transform data on the fly

Guests
Rishi Patel, Senior Sales Engineer, Experian Data Quality
Rishi has over 10 years experience in data quality software from development and
implementation to best practices and solution strategy. He is an active member in the
data quality community and focuses on building out highly skilled consultancy practices
within Experian focused on enterprise applications and architecture. He works on go-to-
market strategies and technical subject matter expertise in new and emerging
technologies for Experian Data Quality such as Experian Pandora.
Erin Haselkorn, Analyst Relations Manager, Experian Data Quality
As the Analyst Relations Manager for Experian Data Quality, Erin Haselkorn leverages her
understanding of data quality to help organizations better understand leading data
management strategies and how to create actionable insights. She is the author of
numerous data quality research reports, guest blog posts and articles. During her eight
years at Experian Data Quality, Erin has helped numerous clients gain a deeper
understanding of their customers through data and analytics.

©2016 Experian Information Solutions, Inc. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of
Experian Information Solutions, Inc. Other product and company names mentioned herein are the trademarks of their respective owners.
No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian.
Experian Public.
A Better Understanding
Solving business challenges with data

11©2013 Experian Information Solutions, Inc. All rights reserved.
Experian Public. 11©2016 Experian Information Solutions, Inc. All rights reserved.
Experian Public.
§  The trends in data usage are changing
§  How data quality can help improve insight
§  Building an understanding of data
§  What can data profiling do for you?
Agenda

Experian Public.
Turning data into insight
6%
9%
15%
19%
21%
24%
24%
26%
30%
32%
34%
36%
37%
38%
39%
Segmentation
Driving more traffic from one channel to another
Determine marketing campaign performance
Comply with government regulations
Find new revenue streams
Provide insight to make intelligent decisions
Tailor real-time offers
Reduce risk
Personalize future campaigns
Secure future budgets
Business growth
Increase the value of each customer
Understand customer needs
Customer retention
Find new customers

Experian Public.
of organizations we
surveyed say data
clearly ties into their
business objectives
Data drives business initiatives

Experian Public.
Inaccurate data
Most companies today have seen an increase in the amount of data errors.
26%
28%
30%
37%
51%
54%
60%
Data entered in the incorrect field
Spelling mistakes
Typos
Inconsistent data
Duplicate data
Outdated information (not current)
Incomplete or missing data

Experian Public.
Consequences of inaccurate data
21%
29%
31%
34%
36%
37%
37%
Process inefficiency due to data
problems
Lost revenue opportunities
Distrust in decisions
Potential brand / reputational damage
Customer experience is not optimal
Regulatory risk
Difficulty using data for decision-making

Experian Public.
Data quality is the foundation
Data Governance
BI & Reporting
Data Integration
Master Data Management
Data Quality

Experian Public.
Experian Pandora methodology
Data Quality
Management
Profi
le/Quantify
Monitor/R
eport
Cleanse / Enrich
CONTRO
L A
NALYZE
IMPROVE

Experian Public.
Analyze
Investigate your data
§  Uncover the issues you weren’t looking
for through automatic, proactive profiling
§  Find and document issues
§  Align priorities and estimate complexity
§  Collaborate across business lines

Experian Public.
Improve
Take intelligent action
§  Use hard facts to determine next
steps
§  Set priorities based on insights
§  Build data improvement rules
§  Complete inventory and issue
documentation

Experian Public.
Control
Continue to manage data
§  Automate data quality monitoring
§  Share your dashboards
§  Continue to uncover issues and apply
new rules
§  Take action

Experian Public.
Built-in data quality reporting

Experian Public.

Data profiling leads to
data insight

Thank you!
Here’s how we can stay connected:
dataquality.info@experian.com
(888) 727-8822
@ExperianDQ

Perceptions & Questions
Analyst:
Robin Bloor

Data Value
Data per se has no value – it is raw
material.
The PROCESSING of data in its
myriad ways generates the value.

The Data Pyramid
u  Most of us are aware of this refinement of data and the
processes involved. Difficulties arise from:
u  Fragmentation (of data, information, knowledge &
understanding)
u  The incessant supply of new data
Rules, Policies
Guidelines, Procedures
Linked data, Structured data,
Visualization, Glossaries, Schemas, Ontologies
Signals, Measurements, Recordings,
Events, Transactions, Calculations, Aggregations
New
Data
Refinement

The Hadoop/Spark “Lake” Scenario
u  Multiple external and
internal data sources
u  Presume IT Security
u  Assume the full gamut of
Data Wrangling tools (LHS)
u  Assume data management
tools (RHS)
u  Assume Analytics and BI
tools either local or at the
data warehouse
u  It all adds up to data
governance
Data Sources
Analytics
Service
Mgt
Life Cycle
Mgt
MetaData
Discovery
MDM
MetaData
Mgt
Data
Cleansing
Data
Lineage
A
C
C
E
S
S
W
R
A
N
G
L
I
N
G
Staging Area
(Hadoop)
Data Warehouse
or other location
Data Streams
ETL
ETL

The Analytics Business Process
§  The main point to note about
analytics is that it is still iterative
§  The process changed because of:
o  Data Availability
o  Parallel Technology
o  Scalable Software
o  Open Source Tools
o  M/C Learning
§  It is naturally becoming
integrated into the Data Lake
Data
Access
Data
Prep
Model
Analyze
Deploy
Execute

A Practical View
The “data wrangling” activities
transform data into information in
preparation for transforming it into
knowledge

u  How would you define data governance – would
you include provenance/lineage?
u  How does Experian integrate with data streams
(or doesn’t it)?
u  In respect of scale, what is your largest
implementation by data volume and what was
the industry sector/problem space?
u  Who do you serve, the business analysts or the
data scientist?

u  Is your capability only relevant to analytics or
does it have broader areas of application?
u  Technically, what makes it fast?
u  Please comment on analytical workloads:
- What do you see as the natural IT bottlenecks?
- What do you see as the natural business
bottlenecks?
u  Who do you partner with?

Upcoming Topics
www.insideanalysis.com
December: INNOVATORS
January: ANALYTICS
February: BIG DATA

THANK YOU
for your
ATTENTION!
Some images provided courtesy of Wikimedia Commons

A Better Understanding: Solving Business Challenges with Data

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