SlideShare ist ein Scribd-Unternehmen logo
1 von 26
Downloaden Sie, um offline zu lesen
Keeping the Pulse
of Your Da ta :
Why You Need Data
Observa bility to
Improve Da ta Qua lity
Spea kers
Julie Skeen
Sr. Product Marketing Manager
Micha el Sisola k
Principa l Sa les Engineer
Agenda
• Introduction to data observability
• How data observability works
• Use case examples
• Q&A
3
47%
of newly crea ted
da ta records ha ve a t
lea st one critica l error
68%
of orga niza tions sa y
dispa ra te da ta nega tively
impa cts their orga niza tion
84%
of CEOs sa y tha t they a re
concerned a bout the integrity of the
da ta they a re ma king decisions on
Precisely Da ta Trends Survey Forbes
Ha rva rd Business Review
Da ta integrity is a business impera tive
Introduction to Da ta
Observa bility
Business Challenges
• Data downtime disrupts critical data
pipelines and processes that power
downstream analytics and operations
• Lack of visibility around health of data
reduces confidence in business decisions
• Traditional manual methods do not scale,
are error-prone, and are resource intensive
5
Everything old is new a ga in
• “W. Edwards Deming The Father of Quality Management” started the
observability concept 100 years ago
• Observability is a key foundational concept of SPC, Lean, Six Sigma and
any process dependent on building quality into repetitive tasks
Applying the same principles to data = data observability
• Using statistical methods to control complex processes to ensure quality
data products over time
Wha t is Da ta Observa bility?
6
IDC; Phil Goodwin a nd Stewa rt Bond, “IDC Ma rket Gla nce: Da ta Ops, 2Q21”(June 2021)
Ga rtner, Hype Cycle for Da ta Ma na gement, 2022, Melody Chien, Ankush Ja in, Robert Tha na ra j, June 30, 2022
Why Now?
7
• Businesses a re more da ta -driven
tha n ever
• Problema tic events a re infrequent
but ca n be ca ta strophic
• User’s da ta expertise ha s evolved
a long with expecta tions to do
more with it
• Da ta prolifera tion a nd technology
diversifica tion
• AI ha s evolved to support the
complexity of the problem
Da ta Observa bility is proa ctive, not rea ctive
8
Da ta Integrity
a nd Qua lity
QA is done at the
time of development
Ra ndom issues a re
surfa ced
Users find a nd
report defects
9
9
Typica l Da ta Products a nd Pipelines
Tra ditiona lly, the qua lity of a da ta product or pipeline is ensured during the
development process a nd not throughout the opera tiona l lifecycle.
Da ta Product(s)
X
Da ta Source #1
?
Da ta Source #2
?
Da ta Source #3
?
Da ta Source #4
?
Crea te a nd/ or
Source The Da ta
Tra nsform
Da ta
Enrich / Blend /
Merge Da ta
Publish a n
Expose Da ta
P
r
o
c
e
s
s
10
10
Da ta Pipelines with Observa bility
Da ta Observa bility tools observe the performa nce of da ta products a nd processes in order to
detect significa nt va ria tions before they result in the crea tion of erroneous work product in reports,
a na lytics, insights a nd outcomes.
Da ta Source #1 Da ta Source #2 Da ta Source #3
!
Da ta Source #4
Crea te a nd/ or
Source The Da ta
Tra nsform
Da ta
Enrich / Blend /
Merge Da ta
Publish a n
Expose Da ta
P
r
o
c
e
s
s
Observing ea ch sta ge in the pipeline
Issues identified a nd resolved prior to fina l product
O
b
s
e
r
v
e
Da ta Product(s)
11
Da ta
Observa bility
Impa ct of
Unexpected
Da ta
Da ta a noma lies ha ve downstrea m impa cts, but not every
issue impa cts the process in the sa me wa y.
The sooner you ca n detect a noma lies, the sooner you
ca n a ssess the impa cts a nd effectively remedia te.
EXAMPLE
How Da ta Observa bility Works
Discovery Ana lysis Action
Intelligent Ana lysis Identifies Anoma lies
13
AI identifies
trends tha t
tra ditiona l
methods
ca nnot
ea sily find
Ra ndom Noise Upwa rd Trend Downwa rd Trend
Step Cha nge 2 Step Cha nge 1 Sudden Jump Up
Da ta Observa bility a nd Qua lity
14
Rules
Metadata
Time
Data Quality
Management
Da ta Observa bility Focused Ca pa bilities
• Alerts a nd da shboa rds for overa ll da ta hea lth
trending a nd threshold a na lysis
• Anoma ly detection ba sed on volume, freshness,
distribution a nd schema meta da ta
• Predictive a na lysis simula ting huma n intelligence
to identify potentia l a dverse da ta integrity events
“Observa bility is the missing piece toda y to give our da ta stewa rds a ccess
to da ta discovery insights without ha ving to go to IT for queries or reports”
- Jea n-Pa ul Otte, CDO, Degroof Peterca m
Alerts a nd Impa cts
15
Volume Alert
Impacts
Use Ca se Exa mples
17
Da ta
Observa bility
Impa ct of
Unexpected
Va lues
An incorrect currency type in the order crea ted a n
infla ted revenue a mount which would ha ve resulted in
the incorrect tota l revenue a mount.
The error wa s ca used beca use the currency conversion
ta ble wa s not upda ted.
The Da ta Observa bility solution would notify the
Da ta Ops tea m of the da ta drift so tha t they could
quickly resolve the issue a nd prevent it from impa cting
downstrea m a na lytics a nd rela ted decisions.
EXAMPLE
18
Da ta
Observa bility
Unexpected
da ta volumes
impa ct
opera tions
A single-da y spike of 500% in the dolla r a mount of orders
ca used beca use the compa ny expa nded into a new
geogra phy without notifying a ll a ffected a rea s within the
compa ny.
Da ta stewa rd would receive a volume a lert which a llows
them to quickly investiga te the issue before it impa cts
downstrea m a na lytics a nd rela ted decisions.
EXAMPLE
Use Ca se Reca p
19
• Da ta a noma ly impa cted
downstrea m processes
• Impa ct of Unexpected Va lues
ca used by a n inva lid currency type
• Unexpected data values ca used by
la ck of communica tion interna lly
Understa nd the hea lth of your data with continuous measuring and monitoring
Obta in visibility into your da ta la ndsca pe a nd dependencies with intuitive
self-discovery ca pa bilities
Receive a lerts when outliers a nd a noma lies a re identified using a rtificia l intelligence
Resolve da ta drift a nd shift when identified by intelligent a na lysis
1
2
3
4
Enable quick remediation when issues occur by understanding the cause of
the issue
5
Da ta Observa bility benefits
20
Da ta Observa bility
Proactively uncover data
a noma lies a nd ta ke a ction
before they become costly
downstrea m issues
For trusted da ta ,
you need da ta integrity
Data integrity is data with maximum
a ccura cy, consistency, a nd context for
confident business decision-ma king
Da ta
Integrity
The modular, interoperable Precisely Data
Integrity Suite conta ins everything you need
to deliver a ccura te, consistent, contextua l
da ta to your business - wherever a nd
whenever it’s needed.
23
7 strong modules deliver exceptiona l va lue
Da ta
Integra tion
Da ta
Observa bility
Da ta
Governa nce
Da ta
Qua lity
Geo
Addressing
Spa tia l
Ana lytics
Da ta
Enrichment
Break down
da ta silos
by quickly
building
modern da ta
pipelines tha t
drive
innova tion
Proa ctively
uncover da ta
a noma lies a nd
ta ke a ction
before they
become costly
downstrea m
issues
Ma na ge da ta
policy a nd
processes with
grea ter insight
into your da ta ’s
mea ning,
linea ge, a nd
impa ct
Deliver da ta
tha t’s a ccura te,
consistent, a nd
fit for purpose
a cross
opera tiona l
a nd a na lytica l
systems
Verify,
sta nda rdize,
clea nse, a nd
geocode
a ddresses to
unlock va lua ble
context for more
informed
decision ma king
Derive a nd
visua lize spa tia l
rela tionships
hidden in your
da ta to revea l
critica l context
for better
decisions
Enrich your
business da ta
with expertly
cura ted da ta sets
conta ining
thousa nds of
a ttributes for
fa ster, confident
decisions
Questions?
Tha nk you
Lea rn more a bout Da ta Observa bility
https://www.precisely.com/product/data -integrity/ precisely-da ta -integrity-suite/ da ta -observa bility

Weitere ähnliche Inhalte

Was ist angesagt?

Data-Ed: Data-centric Strategy & Roadmap
Data-Ed: Data-centric Strategy & RoadmapData-Ed: Data-centric Strategy & Roadmap
Data-Ed: Data-centric Strategy & RoadmapData Blueprint
 
Data Governance and Metadata Management
Data Governance and Metadata ManagementData Governance and Metadata Management
Data Governance and Metadata Management DATAVERSITY
 
Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
 
Architecting Modern Data Platforms
Architecting Modern Data PlatformsArchitecting Modern Data Platforms
Architecting Modern Data PlatformsAnkit Rathi
 
Demystifying data engineering
Demystifying data engineeringDemystifying data engineering
Demystifying data engineeringThang Bui (Bob)
 
Data Observability.pptx
Data Observability.pptxData Observability.pptx
Data Observability.pptxSonaSamad1
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
 
Data Quality as a prerequisite for you business success: when should I start ...
Data Quality as a prerequisite for you business success: when should I start ...Data Quality as a prerequisite for you business success: when should I start ...
Data Quality as a prerequisite for you business success: when should I start ...Anastasija Nikiforova
 
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...DATAVERSITY
 
Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability Precisely
 
Data Catalogues - Architecting for Collaboration & Self-Service
Data Catalogues - Architecting for Collaboration & Self-ServiceData Catalogues - Architecting for Collaboration & Self-Service
Data Catalogues - Architecting for Collaboration & Self-ServiceDATAVERSITY
 
Modern Data architecture Design
Modern Data architecture DesignModern Data architecture Design
Modern Data architecture DesignKujambu Murugesan
 
Modern Data Warehousing with the Microsoft Analytics Platform System
Modern Data Warehousing with the Microsoft Analytics Platform SystemModern Data Warehousing with the Microsoft Analytics Platform System
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
 
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...DATAVERSITY
 
Data Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationData Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
 
Data Mesh at CMC Markets: Past, Present and Future
Data Mesh at CMC Markets: Past, Present and FutureData Mesh at CMC Markets: Past, Present and Future
Data Mesh at CMC Markets: Past, Present and FutureLorenzo Nicora
 
Data Governance Best Practices
Data Governance Best PracticesData Governance Best Practices
Data Governance Best PracticesDATAVERSITY
 
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...DATAVERSITY
 
Building a modern data warehouse
Building a modern data warehouseBuilding a modern data warehouse
Building a modern data warehouseJames Serra
 

Was ist angesagt? (20)

Data-Ed: Data-centric Strategy & Roadmap
Data-Ed: Data-centric Strategy & RoadmapData-Ed: Data-centric Strategy & Roadmap
Data-Ed: Data-centric Strategy & Roadmap
 
Data Governance and Metadata Management
Data Governance and Metadata ManagementData Governance and Metadata Management
Data Governance and Metadata Management
 
Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?Data Catalogs Are the Answer – What is the Question?
Data Catalogs Are the Answer – What is the Question?
 
Architecting Modern Data Platforms
Architecting Modern Data PlatformsArchitecting Modern Data Platforms
Architecting Modern Data Platforms
 
Demystifying data engineering
Demystifying data engineeringDemystifying data engineering
Demystifying data engineering
 
Data Observability.pptx
Data Observability.pptxData Observability.pptx
Data Observability.pptx
 
Data at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and GovernanceData at the Speed of Business with Data Mastering and Governance
Data at the Speed of Business with Data Mastering and Governance
 
Data Quality as a prerequisite for you business success: when should I start ...
Data Quality as a prerequisite for you business success: when should I start ...Data Quality as a prerequisite for you business success: when should I start ...
Data Quality as a prerequisite for you business success: when should I start ...
 
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
Data Architecture Strategies: Building an Enterprise Data Strategy – Where to...
 
Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability Keeping the Pulse of Your Data:  Why You Need Data Observability 
Keeping the Pulse of Your Data:  Why You Need Data Observability 
 
Data Catalogues - Architecting for Collaboration & Self-Service
Data Catalogues - Architecting for Collaboration & Self-ServiceData Catalogues - Architecting for Collaboration & Self-Service
Data Catalogues - Architecting for Collaboration & Self-Service
 
Modern Data architecture Design
Modern Data architecture DesignModern Data architecture Design
Modern Data architecture Design
 
Modern Data Warehousing with the Microsoft Analytics Platform System
Modern Data Warehousing with the Microsoft Analytics Platform SystemModern Data Warehousing with the Microsoft Analytics Platform System
Modern Data Warehousing with the Microsoft Analytics Platform System
 
Data Governance for Enterprises
Data Governance for EnterprisesData Governance for Enterprises
Data Governance for Enterprises
 
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
Data Architecture, Solution Architecture, Platform Architecture — What’s the ...
 
Data Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationData Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital Transformation
 
Data Mesh at CMC Markets: Past, Present and Future
Data Mesh at CMC Markets: Past, Present and FutureData Mesh at CMC Markets: Past, Present and Future
Data Mesh at CMC Markets: Past, Present and Future
 
Data Governance Best Practices
Data Governance Best PracticesData Governance Best Practices
Data Governance Best Practices
 
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
Webinar: Decoding the Mystery - How to Know if You Need a Data Catalog, a Dat...
 
Building a modern data warehouse
Building a modern data warehouseBuilding a modern data warehouse
Building a modern data warehouse
 

Ähnlich wie Keeping the Pulse of Your Data – Why You Need Data Observability to Improve Data Quality

Keeping the Pulse of Your Data: Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data:  Why You Need Data Observability to Improve D...Keeping the Pulse of Your Data:  Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data: Why You Need Data Observability to Improve D...Precisely
 
7 key problems Water Industry need to solve
7 key problems Water Industry need to solve7 key problems Water Industry need to solve
7 key problems Water Industry need to solveDaniel Cardelús
 
Webinar on Big Data Challenges : Presented by Raj Kasturi
Webinar on Big Data Challenges : Presented by Raj KasturiWebinar on Big Data Challenges : Presented by Raj Kasturi
Webinar on Big Data Challenges : Presented by Raj KasturioGuild .
 
Why Data Science Projects Fail
Why Data Science Projects FailWhy Data Science Projects Fail
Why Data Science Projects FailSense Corp
 
Why Data Science Projects Fail
Why Data Science Projects FailWhy Data Science Projects Fail
Why Data Science Projects FailSense Corp
 
Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field Domino Data Lab
 
Big Data Analytics: The Move Toward Rapid Experimentation
Big Data Analytics: The Move Toward Rapid ExperimentationBig Data Analytics: The Move Toward Rapid Experimentation
Big Data Analytics: The Move Toward Rapid ExperimentationBrillio
 
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...Genpact Ltd
 
My business processes are deviant! What should I do about it?
My business processes are deviant! What should I do about it?My business processes are deviant! What should I do about it?
My business processes are deviant! What should I do about it?Marlon Dumas
 
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA
 
Foundational Strategies for Trust in Big Data Part 3: Data Lineage
Foundational Strategies for Trust in Big Data Part 3: Data LineageFoundational Strategies for Trust in Big Data Part 3: Data Lineage
Foundational Strategies for Trust in Big Data Part 3: Data LineagePrecisely
 
Kythera BioPharma Commercial Infrastructure 2015 05 28 final
Kythera BioPharma Commercial Infrastructure 2015 05 28 finalKythera BioPharma Commercial Infrastructure 2015 05 28 final
Kythera BioPharma Commercial Infrastructure 2015 05 28 finalMichael W. Hughes
 
Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...
Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...
Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...Raleigh ISSA
 
Taking Splunk to the Next Level - New to Splunk
Taking Splunk to the Next Level - New to SplunkTaking Splunk to the Next Level - New to Splunk
Taking Splunk to the Next Level - New to SplunkSplunk
 
5 Single Shift CI Projects (1)
5 Single Shift CI Projects (1)5 Single Shift CI Projects (1)
5 Single Shift CI Projects (1)Jaime Alboim
 
Big Data Tools PowerPoint Presentation Slides
Big Data Tools PowerPoint Presentation SlidesBig Data Tools PowerPoint Presentation Slides
Big Data Tools PowerPoint Presentation SlidesSlideTeam
 
Introducing data driven practices into sales environments
Introducing data driven practices into sales environmentsIntroducing data driven practices into sales environments
Introducing data driven practices into sales environmentsBarry Magee
 
The Analytic Trifecta: Abstraction, the Cloud, and Visualization
The Analytic Trifecta: Abstraction, the Cloud, and VisualizationThe Analytic Trifecta: Abstraction, the Cloud, and Visualization
The Analytic Trifecta: Abstraction, the Cloud, and VisualizationBirst
 
Group 2 Handling and Processing of big data.pptx
Group 2 Handling and Processing of big data.pptxGroup 2 Handling and Processing of big data.pptx
Group 2 Handling and Processing of big data.pptxsalutiontechnology
 

Ähnlich wie Keeping the Pulse of Your Data – Why You Need Data Observability to Improve Data Quality (20)

Keeping the Pulse of Your Data: Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data:  Why You Need Data Observability to Improve D...Keeping the Pulse of Your Data:  Why You Need Data Observability to Improve D...
Keeping the Pulse of Your Data: Why You Need Data Observability to Improve D...
 
7 key problems Water Industry need to solve
7 key problems Water Industry need to solve7 key problems Water Industry need to solve
7 key problems Water Industry need to solve
 
Webinar on Big Data Challenges : Presented by Raj Kasturi
Webinar on Big Data Challenges : Presented by Raj KasturiWebinar on Big Data Challenges : Presented by Raj Kasturi
Webinar on Big Data Challenges : Presented by Raj Kasturi
 
Why Data Science Projects Fail
Why Data Science Projects FailWhy Data Science Projects Fail
Why Data Science Projects Fail
 
Why Data Science Projects Fail
Why Data Science Projects FailWhy Data Science Projects Fail
Why Data Science Projects Fail
 
Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field Managing Data Science | Lessons from the Field
Managing Data Science | Lessons from the Field
 
Big Data Analytics: The Move Toward Rapid Experimentation
Big Data Analytics: The Move Toward Rapid ExperimentationBig Data Analytics: The Move Toward Rapid Experimentation
Big Data Analytics: The Move Toward Rapid Experimentation
 
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
Continuous Transaction Monitoring Detect and analyze anomalous transactions t...
 
My business processes are deviant! What should I do about it?
My business processes are deviant! What should I do about it?My business processes are deviant! What should I do about it?
My business processes are deviant! What should I do about it?
 
8 d corrective actions
8 d corrective actions8 d corrective actions
8 d corrective actions
 
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
 
Foundational Strategies for Trust in Big Data Part 3: Data Lineage
Foundational Strategies for Trust in Big Data Part 3: Data LineageFoundational Strategies for Trust in Big Data Part 3: Data Lineage
Foundational Strategies for Trust in Big Data Part 3: Data Lineage
 
Kythera BioPharma Commercial Infrastructure 2015 05 28 final
Kythera BioPharma Commercial Infrastructure 2015 05 28 finalKythera BioPharma Commercial Infrastructure 2015 05 28 final
Kythera BioPharma Commercial Infrastructure 2015 05 28 final
 
Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...
Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...
Raleigh ISSA: "Optimize Your Data Protection Investment for Bottom Line Resul...
 
Taking Splunk to the Next Level - New to Splunk
Taking Splunk to the Next Level - New to SplunkTaking Splunk to the Next Level - New to Splunk
Taking Splunk to the Next Level - New to Splunk
 
5 Single Shift CI Projects (1)
5 Single Shift CI Projects (1)5 Single Shift CI Projects (1)
5 Single Shift CI Projects (1)
 
Big Data Tools PowerPoint Presentation Slides
Big Data Tools PowerPoint Presentation SlidesBig Data Tools PowerPoint Presentation Slides
Big Data Tools PowerPoint Presentation Slides
 
Introducing data driven practices into sales environments
Introducing data driven practices into sales environmentsIntroducing data driven practices into sales environments
Introducing data driven practices into sales environments
 
The Analytic Trifecta: Abstraction, the Cloud, and Visualization
The Analytic Trifecta: Abstraction, the Cloud, and VisualizationThe Analytic Trifecta: Abstraction, the Cloud, and Visualization
The Analytic Trifecta: Abstraction, the Cloud, and Visualization
 
Group 2 Handling and Processing of big data.pptx
Group 2 Handling and Processing of big data.pptxGroup 2 Handling and Processing of big data.pptx
Group 2 Handling and Processing of big data.pptx
 

Mehr von DATAVERSITY

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...DATAVERSITY
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data LiteracyDATAVERSITY
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for YouDATAVERSITY
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?DATAVERSITY
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling FundamentalsDATAVERSITY
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectDATAVERSITY
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at ScaleDATAVERSITY
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?DATAVERSITY
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsDATAVERSITY
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayDATAVERSITY
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise AnalyticsDATAVERSITY
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best PracticesDATAVERSITY
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?DATAVERSITY
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best PracticesDATAVERSITY
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
 
Empowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business IntelligenceEmpowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business IntelligenceDATAVERSITY
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureDATAVERSITY
 

Mehr von DATAVERSITY (20)

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...
 
Exploring Levels of Data Literacy
Exploring Levels of Data LiteracyExploring Levels of Data Literacy
Exploring Levels of Data Literacy
 
Building a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business GoalsBuilding a Data Strategy – Practical Steps for Aligning with Business Goals
Building a Data Strategy – Practical Steps for Aligning with Business Goals
 
Make Data Work for You
Make Data Work for YouMake Data Work for You
Make Data Work for You
 
Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?Data Catalogs Are the Answer – What Is the Question?
Data Catalogs Are the Answer – What Is the Question?
 
Data Modeling Fundamentals
Data Modeling FundamentalsData Modeling Fundamentals
Data Modeling Fundamentals
 
Showing ROI for Your Analytic Project
Showing ROI for Your Analytic ProjectShowing ROI for Your Analytic Project
Showing ROI for Your Analytic Project
 
How a Semantic Layer Makes Data Mesh Work at Scale
How a Semantic Layer Makes  Data Mesh Work at ScaleHow a Semantic Layer Makes  Data Mesh Work at Scale
How a Semantic Layer Makes Data Mesh Work at Scale
 
Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?Is Enterprise Data Literacy Possible?
Is Enterprise Data Literacy Possible?
 
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...
 
Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?Emerging Trends in Data Architecture – What’s the Next Big Thing?
Emerging Trends in Data Architecture – What’s the Next Big Thing?
 
Data Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and ForwardsData Governance Trends - A Look Backwards and Forwards
Data Governance Trends - A Look Backwards and Forwards
 
Data Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement TodayData Governance Trends and Best Practices To Implement Today
Data Governance Trends and Best Practices To Implement Today
 
2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics2023 Trends in Enterprise Analytics
2023 Trends in Enterprise Analytics
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best Practices
 
Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?Who Should Own Data Governance – IT or Business?
Who Should Own Data Governance – IT or Business?
 
Data Management Best Practices
Data Management Best PracticesData Management Best Practices
Data Management Best Practices
 
MLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive AdvantageMLOps – Applying DevOps to Competitive Advantage
MLOps – Applying DevOps to Competitive Advantage
 
Empowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business IntelligenceEmpowering the Data Driven Business with Modern Business Intelligence
Empowering the Data Driven Business with Modern Business Intelligence
 
Enterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data ArchitectureEnterprise Architecture vs. Data Architecture
Enterprise Architecture vs. Data Architecture
 

Kürzlich hochgeladen

Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改yuu sss
 
modul pembelajaran robotic Workshop _ by Slidesgo.pptx
modul pembelajaran robotic Workshop _ by Slidesgo.pptxmodul pembelajaran robotic Workshop _ by Slidesgo.pptx
modul pembelajaran robotic Workshop _ by Slidesgo.pptxaleedritatuxx
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBoston Institute of Analytics
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhijennyeacort
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingsocarem879
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 217djon017
 
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degreeyuu sss
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...Amil Baba Dawood bangali
 
Thiophen Mechanism khhjjjjjjjhhhhhhhhhhh
Thiophen Mechanism khhjjjjjjjhhhhhhhhhhhThiophen Mechanism khhjjjjjjjhhhhhhhhhhh
Thiophen Mechanism khhjjjjjjjhhhhhhhhhhhYasamin16
 
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一z xss
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesTimothy Spann
 
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryJeremy Anderson
 
Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作ys8omjxb
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxMike Bennett
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfBoston Institute of Analytics
 
Identifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanIdentifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanMYRABACSAFRA2
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queensdataanalyticsqueen03
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Seán Kennedy
 
SWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptxSWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptxviniciusperissetr
 

Kürzlich hochgeladen (20)

Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
 
modul pembelajaran robotic Workshop _ by Slidesgo.pptx
modul pembelajaran robotic Workshop _ by Slidesgo.pptxmodul pembelajaran robotic Workshop _ by Slidesgo.pptx
modul pembelajaran robotic Workshop _ by Slidesgo.pptx
 
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis ProjectBank Loan Approval Analysis: A Comprehensive Data Analysis Project
Bank Loan Approval Analysis: A Comprehensive Data Analysis Project
 
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝DelhiRS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
RS 9000 Call In girls Dwarka Mor (DELHI)⇛9711147426🔝Delhi
 
INTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processingINTRODUCTION TO Natural language processing
INTRODUCTION TO Natural language processing
 
Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2Easter Eggs From Star Wars and in cars 1 and 2
Easter Eggs From Star Wars and in cars 1 and 2
 
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
办美国阿肯色大学小石城分校毕业证成绩单pdf电子版制作修改#真实留信入库#永久存档#真实可查#diploma#degree
 
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
NO1 Certified Black Magic Specialist Expert Amil baba in Lahore Islamabad Raw...
 
Thiophen Mechanism khhjjjjjjjhhhhhhhhhhh
Thiophen Mechanism khhjjjjjjjhhhhhhhhhhhThiophen Mechanism khhjjjjjjjhhhhhhhhhhh
Thiophen Mechanism khhjjjjjjjhhhhhhhhhhh
 
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
办理(UC毕业证书)堪培拉大学毕业证成绩单原版一比一
 
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming PipelinesConf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
Conf42-LLM_Adding Generative AI to Real-Time Streaming Pipelines
 
Defining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data StoryDefining Constituents, Data Vizzes and Telling a Data Story
Defining Constituents, Data Vizzes and Telling a Data Story
 
Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作Ulm U学位证,乌尔姆大学毕业证书1:1制作
Ulm U学位证,乌尔姆大学毕业证书1:1制作
 
Semantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptxSemantic Shed - Squashing and Squeezing.pptx
Semantic Shed - Squashing and Squeezing.pptx
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
 
Identifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanIdentifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population Mean
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queens
 
Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...Student Profile Sample report on improving academic performance by uniting gr...
Student Profile Sample report on improving academic performance by uniting gr...
 
SWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptxSWOT Analysis Slides Powerpoint Template.pptx
SWOT Analysis Slides Powerpoint Template.pptx
 

Keeping the Pulse of Your Data – Why You Need Data Observability to Improve Data Quality

  • 1. Keeping the Pulse of Your Da ta : Why You Need Data Observa bility to Improve Da ta Qua lity
  • 2. Spea kers Julie Skeen Sr. Product Marketing Manager Micha el Sisola k Principa l Sa les Engineer
  • 3. Agenda • Introduction to data observability • How data observability works • Use case examples • Q&A 3
  • 4. 47% of newly crea ted da ta records ha ve a t lea st one critica l error 68% of orga niza tions sa y dispa ra te da ta nega tively impa cts their orga niza tion 84% of CEOs sa y tha t they a re concerned a bout the integrity of the da ta they a re ma king decisions on Precisely Da ta Trends Survey Forbes Ha rva rd Business Review Da ta integrity is a business impera tive
  • 5. Introduction to Da ta Observa bility Business Challenges • Data downtime disrupts critical data pipelines and processes that power downstream analytics and operations • Lack of visibility around health of data reduces confidence in business decisions • Traditional manual methods do not scale, are error-prone, and are resource intensive 5
  • 6. Everything old is new a ga in • “W. Edwards Deming The Father of Quality Management” started the observability concept 100 years ago • Observability is a key foundational concept of SPC, Lean, Six Sigma and any process dependent on building quality into repetitive tasks Applying the same principles to data = data observability • Using statistical methods to control complex processes to ensure quality data products over time Wha t is Da ta Observa bility? 6 IDC; Phil Goodwin a nd Stewa rt Bond, “IDC Ma rket Gla nce: Da ta Ops, 2Q21”(June 2021) Ga rtner, Hype Cycle for Da ta Ma na gement, 2022, Melody Chien, Ankush Ja in, Robert Tha na ra j, June 30, 2022
  • 7. Why Now? 7 • Businesses a re more da ta -driven tha n ever • Problema tic events a re infrequent but ca n be ca ta strophic • User’s da ta expertise ha s evolved a long with expecta tions to do more with it • Da ta prolifera tion a nd technology diversifica tion • AI ha s evolved to support the complexity of the problem
  • 8. Da ta Observa bility is proa ctive, not rea ctive 8
  • 9. Da ta Integrity a nd Qua lity QA is done at the time of development Ra ndom issues a re surfa ced Users find a nd report defects 9 9 Typica l Da ta Products a nd Pipelines Tra ditiona lly, the qua lity of a da ta product or pipeline is ensured during the development process a nd not throughout the opera tiona l lifecycle. Da ta Product(s) X Da ta Source #1 ? Da ta Source #2 ? Da ta Source #3 ? Da ta Source #4 ? Crea te a nd/ or Source The Da ta Tra nsform Da ta Enrich / Blend / Merge Da ta Publish a n Expose Da ta P r o c e s s
  • 10. 10 10 Da ta Pipelines with Observa bility Da ta Observa bility tools observe the performa nce of da ta products a nd processes in order to detect significa nt va ria tions before they result in the crea tion of erroneous work product in reports, a na lytics, insights a nd outcomes. Da ta Source #1 Da ta Source #2 Da ta Source #3 ! Da ta Source #4 Crea te a nd/ or Source The Da ta Tra nsform Da ta Enrich / Blend / Merge Da ta Publish a n Expose Da ta P r o c e s s Observing ea ch sta ge in the pipeline Issues identified a nd resolved prior to fina l product O b s e r v e Da ta Product(s)
  • 11. 11 Da ta Observa bility Impa ct of Unexpected Da ta Da ta a noma lies ha ve downstrea m impa cts, but not every issue impa cts the process in the sa me wa y. The sooner you ca n detect a noma lies, the sooner you ca n a ssess the impa cts a nd effectively remedia te. EXAMPLE
  • 12. How Da ta Observa bility Works Discovery Ana lysis Action
  • 13. Intelligent Ana lysis Identifies Anoma lies 13 AI identifies trends tha t tra ditiona l methods ca nnot ea sily find Ra ndom Noise Upwa rd Trend Downwa rd Trend Step Cha nge 2 Step Cha nge 1 Sudden Jump Up
  • 14. Da ta Observa bility a nd Qua lity 14 Rules Metadata Time Data Quality Management Da ta Observa bility Focused Ca pa bilities • Alerts a nd da shboa rds for overa ll da ta hea lth trending a nd threshold a na lysis • Anoma ly detection ba sed on volume, freshness, distribution a nd schema meta da ta • Predictive a na lysis simula ting huma n intelligence to identify potentia l a dverse da ta integrity events “Observa bility is the missing piece toda y to give our da ta stewa rds a ccess to da ta discovery insights without ha ving to go to IT for queries or reports” - Jea n-Pa ul Otte, CDO, Degroof Peterca m
  • 15. Alerts a nd Impa cts 15 Volume Alert Impacts
  • 16. Use Ca se Exa mples
  • 17. 17 Da ta Observa bility Impa ct of Unexpected Va lues An incorrect currency type in the order crea ted a n infla ted revenue a mount which would ha ve resulted in the incorrect tota l revenue a mount. The error wa s ca used beca use the currency conversion ta ble wa s not upda ted. The Da ta Observa bility solution would notify the Da ta Ops tea m of the da ta drift so tha t they could quickly resolve the issue a nd prevent it from impa cting downstrea m a na lytics a nd rela ted decisions. EXAMPLE
  • 18. 18 Da ta Observa bility Unexpected da ta volumes impa ct opera tions A single-da y spike of 500% in the dolla r a mount of orders ca used beca use the compa ny expa nded into a new geogra phy without notifying a ll a ffected a rea s within the compa ny. Da ta stewa rd would receive a volume a lert which a llows them to quickly investiga te the issue before it impa cts downstrea m a na lytics a nd rela ted decisions. EXAMPLE
  • 19. Use Ca se Reca p 19 • Da ta a noma ly impa cted downstrea m processes • Impa ct of Unexpected Va lues ca used by a n inva lid currency type • Unexpected data values ca used by la ck of communica tion interna lly
  • 20. Understa nd the hea lth of your data with continuous measuring and monitoring Obta in visibility into your da ta la ndsca pe a nd dependencies with intuitive self-discovery ca pa bilities Receive a lerts when outliers a nd a noma lies a re identified using a rtificia l intelligence Resolve da ta drift a nd shift when identified by intelligent a na lysis 1 2 3 4 Enable quick remediation when issues occur by understanding the cause of the issue 5 Da ta Observa bility benefits 20
  • 21. Da ta Observa bility Proactively uncover data a noma lies a nd ta ke a ction before they become costly downstrea m issues
  • 22. For trusted da ta , you need da ta integrity Data integrity is data with maximum a ccura cy, consistency, a nd context for confident business decision-ma king Da ta Integrity
  • 23. The modular, interoperable Precisely Data Integrity Suite conta ins everything you need to deliver a ccura te, consistent, contextua l da ta to your business - wherever a nd whenever it’s needed. 23
  • 24. 7 strong modules deliver exceptiona l va lue Da ta Integra tion Da ta Observa bility Da ta Governa nce Da ta Qua lity Geo Addressing Spa tia l Ana lytics Da ta Enrichment Break down da ta silos by quickly building modern da ta pipelines tha t drive innova tion Proa ctively uncover da ta a noma lies a nd ta ke a ction before they become costly downstrea m issues Ma na ge da ta policy a nd processes with grea ter insight into your da ta ’s mea ning, linea ge, a nd impa ct Deliver da ta tha t’s a ccura te, consistent, a nd fit for purpose a cross opera tiona l a nd a na lytica l systems Verify, sta nda rdize, clea nse, a nd geocode a ddresses to unlock va lua ble context for more informed decision ma king Derive a nd visua lize spa tia l rela tionships hidden in your da ta to revea l critica l context for better decisions Enrich your business da ta with expertly cura ted da ta sets conta ining thousa nds of a ttributes for fa ster, confident decisions
  • 26. Tha nk you Lea rn more a bout Da ta Observa bility https://www.precisely.com/product/data -integrity/ precisely-da ta -integrity-suite/ da ta -observa bility