Untrustworthy results or inaccurate insights from ML, AI, and advanced analytics systems were due to a lack of quality in the data as reported by nearly half of respondents to Precisely’s Enterprise Data Quality survey. Are you ready to improve your trust in the data your organization is using in the cloud for business decision-making? Register now to learn how to take the first steps to high-quality data in the cloud by better understanding your data through profiling. During this on-demand webinar, we will explore key topics such as: • The five key steps to effective data profiling • How profiling informs your next steps to deliver quality data to the cloud • How Precisely customers have elevated marketing and customer service results by focusing on data quality

1. Understand Your Data
Transform your downstream cloud analytics with
data quality
Marco de Jong | Sales Engineering Director

2. What you will learn today
• Why Data needs Data Quality
• How Data Profiling helps
understanding your data
• The top 5 steps needed for
effective data profiling
• How another company saw
success through data profiling
• What you can do in the next 90
days to take action on DQ

3. Data needs
data quality
“Societal trust in business is
arguably at an all-time low
and, in a world increasingly
driven by data and technology,
reputations and brands are
ever harder to protect.”
EY “Trust in Data and Why it Matters”, 2017.
80%
of AI/ML projects are stalling due to
poor data quality
Dimensional Research, 2019
90%
of executives are concerned about
how misused data can impact
corporate reputation
PWC, 22nd Annual Global CEO Survey, 2019
64%
of IT executives have
trouble finding and cleaning the right
data for strategic data projects
Sierra Venture, 2020
The importance of data
quality in the enterprise:
• Decision making
• Customer centricity
• Compliance
• Machine learning & AI

4. Understanding your data
Data Profiling
• The set of analytical techniques
that evaluate actual data
content (vs. metadata) to
provide a complete view of
each data element in a data
source.
• Provides summarized
inferences, and details of value
and pattern frequencies to
quickly gain data insights.
• Business Rules
• The data quality or validation
rules that help ensure that data
is “fit for use” in its intended
operational and decision-
making contexts.
• Covers the accuracy,
completeness, consistency,
relevance, timeliness and
validity of data.

5. Five Key Steps to effective
Data Profiling
These are not new, but good to reiterate
1. How do you want to analyze the data?
2. What should you review? (there's a lot of stuff)
3. What should you look for? (based on data “type”)
4. When should you build rules? (laser-focus; CDE’s)
5. What needs to be communicated?

6. 1. How do you want to analyze the data?

7. “Never lead with a data set;
lead with a question.”
Anthony Scriffignano, Chief Data Scientist, Dun & Bradstreet
Forbes Insights, May 31, 2017, “The Data Differentiator”

8. Universal DQ best practices:
Understand the End Goal
• How does the business intend
to use the data (i.e. what’s the
use case)?
• Empower users (“Who”) to
gain new clarity into the core
problem (“Why”)
• What will the data be used
for?
• What defines the Fitness for
your Purpose?
Establish Scope
• Ask the “right questions”
about the use case and the
data (not just “what” and
“how”)
• What data is relevant to the
effort?
• Big Data or other, you need to
set boundaries for the work
Understand Context
• How does the business define
the data?
• What are the important
characteristics and context of
the data?
• What are the Critical Data
Elements?
• What qualities will you need
to address, or leave alone?
• “High-quality data” definition
will vary by business problem
“If you don’t know what you
want to get out of the data, how
can you know what data you
need – and what insight you’re
looking for?”
Wolf Ruzicka, Chairman of the Board at EastBanc
Technologies, Blog post: June 1, 2017, “Grow A Data Tree
Out Of The “Big Data” Swamp”

9. 2. What do you want to review?

10. Common data quality measurements
What measures can we take advantage of?
1. Completeness – Are the relevant fields populated?
2. Integrity – Does the data maintain an internal
structural integrity or a relational integrity across
sources
3. Uniqueness – Are keys or records unique?
4. Validity – Does the data have the correct values?
• Code and reference values
• Valid ranges
• Valid value combinations
5. Consistency – Is the data at consistent levels of
aggregation or does it have consistent valid
values over time?
6. Timeliness – Did the data arrive in a time period
that makes it useful or usable?

11. New data quality problems
New data, new data quality challenges
• 3rd Party and external data with unknown provenance or
relevance
• Bias in the data – whether in collection, extraction, or other
processing
• Data without standardized structure or formatting
• Continuously streaming data
• Disjointed data (e.g. gaps in receipt)
• Consistency and verification of data sources
• Changes and transformation applied to data (i.e. does it really
represent the original input)

12. Let data profiling guide you
• Contextual visualizations
• Value and pattern distributions
• Attribute summaries and metadata
• Sort and filter to quickly find data of interest
• Detail drilldowns to any content

13. 3. What should you look for?

14. Common data types
What do you need to be aware of?
1. Identifiers – data that uniquely identifies
something
2. Indicators – data that flags a specific
condition
3. Dates – data that identifies a point in time
4. Quantities – data that identifies an amount
or value of something
5. Codes – data that segments other data
6. Text – data that describes or names
something

15. 4. When do you build rules?

16. Build rules for defined conditions
Focus on:
• Critical Data Elements (data quality dimensions)
• Policy-based conditions (e.g. regulatory compliance)
• Correlated data conditions (e.g. If x, then y)
• Filtering and segmenting data (refining evaluations;
investigating root cause)

17. Benefits of business rules
• Validate critical requirements within or across
data sources
• Build common rules that can be readily tested
and shared
• Evaluate and remediate issues
• Take action on incorrect data and defaults
• Create flags for subsequent use in marking or
remediating data
• Filter result sets and export for additional use

18. 5. What should you communicate?

19. Communicate!
Culture of Data Literacy
• “Democratization of Data” requires
cultural support
Program of Data Governance
• Provide the processes and practices
necessary for success
Center of Excellence/Knowledge Base
• Where do you go to find answers?
• Who can help show you how?

20. Annotate results with findings

21. Large European Telco
Leveraging data as a critical asset
• Business Rules
Goal
• Ensure accurate data to support
customer service, marketing,
retention and loyalty
• Implement enterprise-wide data
governance
Challenge
• Data from multiple
sources/systems, stored in many
different formats
• No enterprise standard for data
quality
• Moving to the cloud
Benefits Achieved
• Trusted data for faster, better
strategic and operational decision
making
• More effective marketing and
better customer service
Solution
• Precisely Trillium Discovery
• Precisely Trillium Quality

22. Looking at the Next 90 Days…
• Make profiling actionable (you don’t know what you don’t know until you
profile)
• Keep the 5 key questions top of mind!
Visit us to learn more about Cloud Transformation:
https://www.precisely.com/campaigns/cloud-transformation

23. QA