a better understanding: solving business challenges with data
Post on 10-Jan-2017
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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
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?
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
13 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 13 © 2016 Experian Information Solutions, Inc. All rights reserved. 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
14 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 14 © 2016 Experian Information Solutions, Inc. All rights reserved. Experian Public.
of organizations we surveyed say data clearly ties into their business objectives
Data drives business initiatives
15 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 15 © 2016 Experian Information Solutions, Inc. All rights reserved. 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
16 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 16 © 2016 Experian Information Solutions, Inc. All rights reserved. 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
18 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 18 © 2016 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Data quality is the foundation
Data Governance
BI & Reporting
Data Integration
Master Data Management
Data Quality
20 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 20 © 2016 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Experian Pandora methodology
Data QualityManagement
Profile / QuantifyM
onito
r / R
eport
Cleanse / Enrich
CO
NTR
OL ANALYZE
IMPROVE
21 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 21 © 2016 Experian Information Solutions, Inc. All rights reserved. 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
22 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 22 © 2016 Experian Information Solutions, Inc. All rights reserved. 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
23 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 23 © 2016 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Control
Continue to manage data § Automate data quality monitoring
§ Share your dashboards
§ Continue to uncover issues and apply new rules
§ Take action
24 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 24 © 2016 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Built-in data quality reporting
25 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 25 © 2016 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Built-in data quality reporting
26 © 2013 Experian Information Solutions, Inc. All rights reserved. Experian Public. 26 © 2016 Experian Information Solutions, Inc. All rights reserved. Experian Public.
Built-in data quality reporting
Thank you! Here’s how we can stay connected: dataquality.info@experian.com
(888) 727-8822
@ExperianDQ
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?
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