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

[email protected] @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

[email protected] @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

Data usage is increasing


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


of organizations we surveyed say data clearly ties into their business objectives

Data drives business initiatives


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


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

Trusted data is high quality


Data quality is the foundation

Data Governance

BI & Reporting

Data Integration

Master Data Management

Data Quality

Getting that level of insight


Experian Pandora methodology

Data QualityManagement

Profile / QuantifyM

onito

r / R

eport

Cleanse / Enrich

CO

NTR

OL ANALYZE

IMPROVE


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


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


Control

Continue to manage data §  Automate data quality monitoring

§  Share your dashboards

§  Continue to uncover issues and apply new rules

§  Take action


Built-in data quality reporting

Data profiling leads to data insight

Thank you! Here’s how we can stay connected: [email protected]

(888) 727-8822

@ExperianDQ

Perceptions & Questions

Analyst: Robin Bloor

Data Quality

Robin Bloor, PhD

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, PoliciesGuidelines, Procedures

Linked data, Structured data,Visualization, Glossaries, Schemas, Ontologies

Signals, Measurements, Recordings,Events, Transactions, Calculations, Aggregations

NewData

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

ServiceMgt

Life CycleMgt

MetaDataDiscovery

MDM

MetaDataMgt

DataCleansing

DataLineage

ACCESS

WRANGLING

Staging Area(Hadoop)

Data Warehouseor 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

DataAccess

DataPrep

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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