This session shows via live demonstration the use of Integration Services, Data Quality- and Master Data Services to create a closed loop information management solution, which cleans, standardize, merge and purges data all with the new data curation tools of SQL Server 2012. The session will also cover principals and best practises for each of the technology used.

1. Closed Loop in Enterprise
Information Management
Oliver Engels & Tillmann Eitelberg

2. Who we are
 Oliver:
 CEO of oh22data AG, German MS Gold Partner
 SQL MVP, Microsoft vTSP
 Tillmann:
 CTO of oh22information services GmbH
 Both:




PASS Germany Board Members
Regional Mentors for Germany
SQL Information Services Advisory Board Members
Data Quality Maniacs

3. Our Sponsors:

4. What Are Your Professional Development
Goals?
I want to take
the path from
DBA to Data
Analytics Guru
I want to
upgrade
my skills
I want to
give my
career a
competitive
edge
I want to expand
my network in the
business analytics
industry
Sound familiar? Get a head start and join us today at:
www.passbaconference.com
#passbac
Enjoy $150 off registration: use code CHM2D

5. Upcoming SQL Server events:
XXXIII Encontro da Comunidade SQLPort
Data Evento: 23 Abril 2013 - 18:30
Local do Evento: Auditório Microsoft, Parque das Nações, Lisboa
18:30 - Abertura e recepção.
19:10 - "Analyzing Twitter Data" - Niko Neugebauer (SQL Server MVP, Community Evangelist –
PASS)
20:15 - Coffee break
20:30 - "First Approach to SQL Server Analysis Services" - João Fialho (Consultor BI Independente)
21:30 - Sorteio de prémios
XXXIV Encontro da Comunidade SQLPort
Data Evento: 7 Maio 2013 - 19:00
Local do Evento: Porto
18:30 - Abertura e recepção.
19:00 - «Apresentação para Developers» - para definir
20:15 - Coffee break
20:30 - «Apresentação para definir» - para definir
21:30 - Sorteio de prémios

6. Volunteers:
 They spend their FREE time to give you this
event. (2 months per person)
 Because they are crazy.
 Because they want YOU
to learn from the BEST IN THE WORLD.
 If you see a guy with “STAFF” on their back –
buy them a beer, they deserve it.

7. Paulo Matos:

8. Paulo Borges:

9. João Fialho:

10. Bruno Basto:

11. Niko Neugebauer:

12. Take aways
 EIM. SQL IS. Data Curation…. what? Give
some explanations
 Understanding of the building bricks of EIM in
the Microsoft BI Stack: SSIS, DQS, MDS
 Closed loop: Bring’em all together
What’s possible
 If time allow: First impressions on Selfservice
ETL: Data Explorer Preview

13. Agenda




Definitions
Data Quality Services
Master Data Quality Services
Closed loop: SSIS, DQS, MDS Team up!

14. The situation

15. Definition:
 EIM: Enterprise Information Management
 Wiki:
 Enterprise information management combines
business intelligence (BI) and enterprise content
management (ECM)
 Where BI and ECM respectively manage structured
and unstructured information, EIM does not make this
"technical" distinction.
 It approaches the management of information from
the perspective of enterprise information strategy,
based on the needs of information workers.

16. Definition: SQL Information Services
 SQL Information Service charter:
Enrich enterprise data with the world’s data
Empower developers to build new services and
applications
Connecting with the
world’s data to turn data
into action
Vibrant marketplace ecosystem for the world’s
data
SQL Information Services

17. IT Pro
Knowledge Worker
Surface all
information as a
service to the
organization,
while maintaining
the right level of
control
Enable any user to find
reliable, trusted
information needed
to do their job
discover
secure
create
govern
clean
curate
publish
operationalize
recommend
transform
analyze
Developer
Immediate access to
the data and services
they need to build new
services and applications
Data Analyst
Democratize the broad
adoption of advanced
analytics to empower
businesses

18. SQL Information Services Portfolio
 Building the tools for Enterprise Information
Management
Integration
Services
BizTalk
Master
Data Services
Data
Quality Services
Data Explorer
Big Data
Azure
Data Market
Stream
Insight
Other
IS Tools

19. Data curation
 Data curation components for EIM
Data Quality Services
Master Data Services
Manage
Cleanse
SSIS/BizTalk
Integrate

20. Discover and
Access Data
and Services
PoC: Role definiton
Mash, Improve
Quality, Enrich
and Analyze
Share and
Collaborate
Information
Worker
Simplified, trusted
consumption of data
Data Steward
Data Management
ITProfessional
Service
Management
Provision, Deploy,
Maintain SLA
Publish
Add data sources to source
catalog
Investigate
Identify Data usage
Artifacts and data relations issues
Monitor usage
Govern
Assess, configure and oversee
Respond to
incidents
Manage Assets
Usage and Policy
Improve Quality of
Data and Metadata
Cleanse, Enrich, Curate
Build the plumbing,
Connect the assets
to the service

21. Enterprise Information Management
DQS: DATA QUALITY SERVICE
22

22. Data curation

23. DQS: Data Quality Services
 Main driver for data quality: Costs!
Data quality cost
Costs because of
bad data quality
Cost of optimizing
data quality
Direct
Prevention
Indirect
Discovery
Cleansing

24. DQS: Data Quality Services
 Microsoft's DQM approach:
Data Quality Services (DQS)
is a Knowledge-Driven data quality solution
enabling data stewards to easily improve the
quality of their data
 Easy = Information Worker Driven
 Knowledge driven =
 Capturing knowledge of good and bad
data in knowledge base
25

25. DQS: Data Quality Services
 Domain concept
 Domain (e.g. Street) has
 Domain values
(List of correct and incorrect values)
 Reference data
(External data references, e.g. D&B)
 Rules
(Proofing if data is valid or invalid)
 Termbased Relations
(Change abreviations)
26

26. Data Quality Services
DEMO
27

27. DQS: Data Quality Services
 Domain values
 List of values
 By Excel Import
 By knowledge
discovery
 By hand
 Correction values
 Invalid values

28. DQS: Data Quality Services
 Domain rules
 Regular Expressions
 Logical expressions
 Matching values
 Termbased relations
29

29. DQS: Data Quality Services
 Reference data (RDS)
 External cloud or on premise data streams
with enrichment functions

30. DQS: Data Quality Services
 Reference data (RDS)
 DQS delivers the address, RDS Service
delivers the correction or the geocode
 DQS delivers the name and address RDS
service delivers the new address if moved
 All kind of services available
 Exchange rates, Translations, Geocoding,
Gender definition

31. DQS: Data Quality Services
 Knowledge base (KB)
Knowledge Discovery
Domain 1
Reference
Data
Domain 6
Domain 2
Domain
Values
Domain 7
Domain 8
Rules
Termbased
Relations
Domain 9
Domain 3
Domain 4
Domain 5
Matching Policy
Composite
domain 1
CP Domain 2
CP Domain 3

32. DQS: Data Quality Services
 Matching
 Second functionality in DQS. Detection of
redundant data. After the cleaning values are
standardized and good for comparison processes
 No simple comparison! Comparison will be
handled through complex fuzzy algorithms based
on matching policies the data steward will test
and setup
33

33. DQS: Data Quality Services
 Matching policies:

34. DQS: Data Quality Services
Uncleaned
data
Standardized, structure
and enrich
Discover
redundancy
Classified
data
Monitoring
Azure
Discovery
Reference
Data
Domain
Values
Uncleaned
data
Matching
Cleansing
Rules
Knowledge
Base (KB)
Termbased
Relations
Cleaned
data
Policy
Classification
Profiling & Notifications

35. Enterprise Information Management
MDS:
MASTER DATA SERVICES
37

36. Master Data Management
CRM
ERP
WWW
Customer
Customer
Customer
Product
Product
Product
DWH

37. MDS: Master Data Services
 Problem in EIM
 Heterogenic system environment with several line
of business application [LOB] who produce and
consume data from identical business entities
 Core identities
 Customer
 Product
 Chart of accounts etc.
 Operational and Analytical Problem:
39

38. MDS: Master Data Services
CRM
ERP
WWW
MDM [operational]
Customer
Customer
Customer
Product
Product
Product
MDM [analytical]
DWH

39. MDS: Master Data Services
 Operational MDM
 LOB‘s write and read from MDM to achieve a
single point of trouth
 MDM enforcing the single point of truth [SPOT]
through rules, security, versioning
 LOB systems provide and consume the SPOT of
an entity and the related attributes
 Open interfaces for data exchange
 All by an LOB indipendend UI
41

40. MDS: Master Data Services
 Analytical MDM
 Instead of loading the data from different LOBs to
the DWH landing area and standardize it in the
stage the MDM solution is the gatekeeper
 The gatekeeper function of MDM will be achieved
through rules, standardized hierarchies,
versioning, approvals workflows, dimension
modeling (SCD etc.)
 All by an LOB indipendend UI
42

41. MDS: Master Data Services
CRM
ERP
MDM [operational]
Customer
Product
MDM [analytical]
DWH
WWW

42. MDS: Master Data Services
 Basic Model

43. Master Data Services
DEMO
45

44. MDS: Master Data Services
 Administration Screen

45. MDS: Master Data Services
 Excel Add in for Information Worker
47

46. MDS: Master Data Services
 Central hierachy management
48

47. MDS: Master Data Services
 Collection

48. MDS: Master Data Services
 Business Rules:
 Allows Data Owners to validate data without
writing T-SQL
 Compiled into Stored Procedures
 Uses IF..THEN Structures
 Can use AND & OR Logical Operators, to create
Complex Rules up to 7 levels
 Rules using OR Logical Operator can be broken
down into simpler rules
 Applied to Attribute Members for it’s validation
50

49. MDS: Master Data Services
 Business rules accommodate various
requirements
 Connecting data sources and set overrides
 Multi-level processes
 Workflow and approval – internal (Master Data
Services) and external (Service Broker > SharePoint)
 Multiple or compound business rules provide for more
complex requirements
 Logical operators (AND / OR)
 Control priority of activation
 Enable/disable rules
51

50. MDS: Master Data Services
Rolebased user access
for master data stewards
 Stream
Excel Add In
Silverlight UI
MDS App
LOB [1-n]
DWH
LOB
SSIS
BizTalk
MDS DB
SQL
Views
Stage
Table
Subscription
Views

51. Enterprise Information Management
CLOSED LOOP: TEAM UP

52. EIM: Closed Loop
 Combine MDS and DQS Functionalities
 Use Integration Services to build a closed
loop workflow:
 DQS Knowledge base for cleaning
 MDS Model for standardization and audit
 SSIS for data import, control flow and export

53. EIM Closed Loop
 Demo case:
 Sample available as download from MS for
everybody to play with (
 Today using new SSDT 2012
)
http://www.microsoft.com/en-us/download/details.aspx?id=35462

54. EIM Closed
 Business case:
 Supplier Data List from External
 Need to be checked if new suppliers are available
 New data need to be proofed against data quality
standards set up by the Data Steward
 Correct/Corrected data need to be validated
against Master Data Management to apply
business rules and add new data to the master

55. EIM Closed Loop
 Version 1 (Simple version)
Correct /Corrected
Source
Cleaning
with DQS KB
Fuzzy
Grouping
Filter
Duplicates
Incorrect
Audit
MDS
Stage

56. EIM Closed Loop
 Version 2 (Advanced version)
Cleaning
with DQS KB
Source
Split
Union
for
MDS
Review by
MDS Data
Steward
Union
for
DQS
Correct
Review by
DQS Data
Steward
New
Lookup Up
MDS via ID
Corrected
No
Match
Lookup
corrected
MDS
Yes
Union
Data stream
Yes
>= Confidence
No
Match
Split
< Confidence
Stage

57. Obrigado!