The document discusses the importance of data quality in enhancing business analytics and decision-making, highlighting that poor data costs companies significantly each year. It outlines a data maturity model, detailing stages from 'data aware' to 'data driven', where each stage reflects increasing sophistication in data integration and utilization. The goal is to achieve high-quality, trusted data that empowers organizations to enhance performance and make informed decisions.

1. Objective Benchmarking for
Improved Analytics Health
and Effectiveness
Denny Lengkong and Bill Connell

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Our Speakers
Denny Lengkong
President, IntelliData
dlengkong@intellidata.tech
1-844-237-DATA (3282) X700
Bill Connell
Solutions Director, Personify
wconnell@personifycorp.com
571.758.4907

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IntelliData
• Northern Virginia based IT consulting company
• Personify implementation partner
• Specializes in association and not-for-profit implementations
• Focused on business intelligence and analytics
Data ► Information ► Knowledge ► Decision

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Benefits of Good Data Quality
• Reduced cost
 Bad data costs companies $3.1 trillion/year (1)
 Salespeople wasting time dealing with erred prospect data
 Service delivery people wasting time correcting flawed customer orders received
from sales
 Data scientists spend an inordinate amount of time cleaning data
 IT spends enormous effort lining up systems that “don’t talk”
 Senior executives hedge their plans because they don’t trust the numbers
(1) Harvard Business Review

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Benefits of Good Data Quality
• Increased customer and staff
satisfaction
• Increased sales
• Greater confidence in
analytical systems
• Improved decision making
“Improving data quality is a gift that keeps on giving — it enables you to take
out costs permanently and to more easily pursue other data strategies”
– Thomas C. Redman, Ph.D., “The Data Doc” – Harvard Business Review

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What Does Good Data Look Like
• Complete
• Accurate
• Available
• Trusted/reliable
• Consistent
• Up to date

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Companies with Sophisticated Data & Analytics
• Better business performance
• 8% higher operating margins (1)
• Able to develop a “single version of the truth” about their
business
• Use real-time data to anticipate changes in their business
and take corrective action
“The goal is to turn data into information, and information into insight.”
– Carly Fiorina, former executive, president, and chair of Hewlett-Packard Co.
(1) Gartner: Measuring the business value of data quality

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Data Maturity Model

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Data Aware
• Multiple data sources and databases (silos)
• Lack of integration
• Know where the data are stored, but don't know how to
retrieve them
• Run basic reports
• IT Dependent
• Poor data hygiene (duplicates, bad data)
• Need a single database (source of data)
• Need a process and user training to move to the next stage

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Data Proficient
• Multiple data sources and databases
• Little or no integration
• Know where the data are stored and know how to retrieve them
• Data quality is questioned
• Ready to track KPI
• Run reports and manipulate data (on a separate process)
• Need executive sponsorship and the know-how to manipulate or use
unstructured data
• Need a single database
• Need to learn how to use data efficiently to move to the next stage

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Data Savvy
• Few or no silos
• Different data sources are nicely integrated
• Data used to make critical business decisions
• Executive sponsorship is in place to break down both
organizational and data silos
• IT must keep up by with new technologies
• IT must be able store data effectively and serve up data on
demand
• Need to focus on building advanced capabilities such as data
warehouse and predictive analysis

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Data Driven
• Embed data into all business processes
• No data = no decision
• Objective is to scale the data strategy while continuing to reduce
costs
• IT and the business are functioning as a tight, cohesive unit
• IT has integrated all data sources and apps and has
implemented an advanced analytics platform (data warehouse,
data lake, etc.)
• The business has identified where and how to embed analytics
in its processes

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Other Important Data Exercises
(applies to all stages)
•Data profiling
•Data integrity
•Data cleanup
Data is useful. High-
quality, well-understood,
auditable data is
priceless.
– Ted Friedman, Gartner

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Data quality
effects overall
labor productivity
(1)
20%
Realize duplicate
records are a concern,
yet they don’t know how
many duplicate records
they have (2)
7%
Top data quality
priority is
deduplication (2)
68%
Failed business
initiative
because of poor
data quality (1)
40%
Cannot confirm that
their data is fresh / up
to date (2)
39%
Do not have an official
owner of their data
within their
organization (2)
48%
Hire someone to
handle data
quality (2)
41%
(1) Gartner, Measuring the business value of data quality
(2) Ringlead, The state of data quality benchmark report

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Questions?