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.
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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

25.
Questions?

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