Getting Data Quality Right
High quality data is important for organizational success, but achieving good data quality requires a programmatic approach. Data quality challenges are often the root cause of IT and business failures. To improve, organizations need to take a systems thinking approach, understand data issues over time, and not underestimate the role of culture. Developing repeatable data quality capabilities and expertise can help organizations identify problems, determine causes, and prevent future issues. Effective data quality engineering provides a framework for utilizing data to support business strategy and goals.
3. 2
Consider the following
features as we demonstrate
them in action…
How would you
manage this
company’s
data?
The data is created by your front office…
• Sales representatives record the transactions
• They have flexibility in their own software
The data is in multiple places…
• Your “controls” separated the data
• You have sales data and financial transactions
Everyone up to CEO needs that data…
• What could possibly go wrong?
• How will you discover it?
Human Error
Technical Error
No Error
4. Collibra | Confidential 3
Is our data
quality getting
better or worse
over time?
How do I identify
sensitive data
before running DQ
checks?
Where are my
data
inconsistencies?
Who owns
this data
issue...
How can I
trust this
report or
dashboard?
Our data producers
and data consumers
operate in silos...
What is the
quality of the
data behind my
models and
reports?
Problem: Managing enterprise-scale data quality is a challenge
BI Analyst/
Data
Scientist
Everyday
Employee
Management/
Leadership
5. Collibra | Confidential
4
50-70% 15-25%
lost revenue for
most companies
due to bad data.
average time spent
on manual rule
writing and
management.
Sources:
IDC and Gartner 2020, Tech Jury forecast, 2021
Assessing Data Quality: A Managerial Call to Action (May 2020, HBR)
Seizing Opportunity in Data Quality (MIT Sloan Management Review, 2017)
$1.9B
projected data
quality spend
in 2021.
Problem: This leads to real costs across your organization
47%
of recently created
data records have
at least one
critical error.
6. 5
Collibra Data Quality & Observability Joy of automation
• Discover data
quality issues
• “Feels” statistical,
not code
• Scales as easy a
identify, scan,
discover, train
Intelligent
Auto-generate
explainable and
adaptive DQ
rules
Scalable
Scan large and
diverse databases,
files and
streaming data
Self-service
Empower users
with a unified
scoring system and
flexible rule
management
7. Workflow
Good Notifications
Standardize Rules
Good Scale
Automatically detect change
Good Rule Logic
Profile your data consistently
Good Metadata
Integrate across
multiple data sources
Good Data
minimal viable
data trust
Data Quality &
Observability
'Stack’
Collibra Data Quality &
Observability automates
static observations and
technical rules while
scaling complex business
rules. Consider solutions
in the market that
combine the three
trending features in
modern DQ - advanced
autometrics, rule
templating, and data
discovery/enforcement.