The explosion of data types, sources, and use cases makes it difficult to make the right decisions around the best data management tools for your organisation. Why do you need them? Who is going to use them? What is their value? Watch this webinar on-demand to learn how to demystify the decision making process for the selection of Data Management Tools that support: · Data governance · Data quality · Data modelling · Master data management · Database development · And more

1. “How to select data management tools”
1
© Catapult BI Pty Ltd
Delivering on the promise of
data and analytics

2. 2
© Catapult BI Pty Ltd
Your Presenter -
Data Governance
Dr Jon Gray
CEO & Chief Data Scientist, Catapult BI
jon_gray@catapultbi.com +61 438 223 056
Jon has over 30 years experience in ICT research,
teaching, and consulting.
Jon is a thought leader in the ICT industry, and he is a frequent
speaker at Australian and international conferences.
As CEO of Catapult BI, Jon provides technical and commercial
leadership for this data services brand of the Dialog group of
companies.

3. 3
© Catapult BI Pty Ltd
Catapult BI : Clients & Partners
Clients:
Your Presenter -
Data Governance
Partners:

4. WHAT DOES GOOD ENTERPRISE DATA MANAGEMENT LOOK LIKE?
CENTRAL ROLE OF METADATA MANAGEMENT
ACQUISITION OF METADATA SOFTWARE TOOLS
THE RIGHT WAY AND THE WRONG WAYS
GENERALISE THE SELECTION PROCESS TO OTHER TOOLS
4
© Catapult BI Pty Ltd
CONTENTS

5. 5
© Catapult BI Pty Ltd
DAMA
➢ International Society for Data Management Professionals
DMBOK
➢ Data Management Body of Knowledge
➢ Codifies best practice in data management skills and
knowledge
➢ Applicable to all industry sectors (Govt, Finance,
Telco, Retail, Primary Production, Etc)
➢ Widely distributed, broad readership, more familiar
than others (e.g. MIKE 2.0)
DAMA Data Management Body of Knowledge
https://www.dama.org/cpages/body-of-knowledge
WHAT DOES GOOD DATA MANAGEMENT LOOK LIKE?

6. © Catapult BI Pty Ltd
ENTERPRISE DATA MANAGEMENT
➢ We manage data because we want it to contribute
to improvements in business outcomes.
➢ An integrated, enterprise-wide approach to the
practice of data management is a better way of
using data to improve business outcomes.
➢ Starting with a common vocabulary and practice
guidelines from DMBOK, we can develop EDM
Frameworks tailored to enterprise-specific
business drivers and business context.
➢ These EDM Frameworks describe the specific
policies, processes, roles and competencies, and
software tools, needed to implement an
integrated approach within a given enterprise.
DAMA DMBOK “wheel”

7. © Catapult BI Pty Ltd
SOFTWARE IS ONLY ONE FACET OF CAPABILITY
Policy
nd to nd
ocess
s ness ea
Building a better Data Management CAPABILITY for the enterprise
includes:
✓ Policy – fini i ns f wh “g ” i l ks lik m s
written, approved, and communicated
✓ People – need to have the rights skills and understand their roles
✓ Knowledge – all organisations have essential information about
themselves and their business ecosystem that is understood and
used by People
✓ Process – how are these tasks performed? Who does what, when,
how, why and to whom? Processes must be defined, executed, and
measured.
✓ Automation (Software Tools) – l n’ h v ll h w k,
some of it can be performed by software.
Data Quality
tool
(Vendor D)

8. FUNDAMENTAL DATA MANAGEMENT PRACTICES
Some aspects of data management play a
fundamental role in the successful
implementation of integrated data
management. These fundamental practices are:
• Data Governance;
• Metadata Management;
• Data Quality;
• Reference & Master Data.
Cannot ignore the other practices areas in the
“wh l”, l k f g ss in h s f n
impair overall progress in data management.

9. FUNDAMENTAL EDM PRACTICES
On h “s in ” l y s w king g h high
level of competency, the rest of the team works effectively.
But.. still need all 13 players on the field, switched-on, and
working together.
nalogy: ”Sp ne” of the Foot e team

10. STARTING WITH THE FUNDAMENTAL PRACTICES
But.. just like the the footie team, you still need to keep an eye on all the
other practice areas and get them working together.

11. M is h “ h siv ”
that binds practice and
automation together.
All data management
practices produce
and/or consume
metadata.
There are many different
kinds of metadata, and the
management of this through
an enterprise-wide approach
is essential.
IMPORTANCE OF METADATA

12. METADATA TOOLS - WHAT DO GARTNER AND FORESTER HAVE TO SAY?
Magic Quadrant for Metadata Management
Solutions (November 2020)
• Need for Better Data Governance is a key
driver of Data Catalogue deployments
• Concerns over Provenance and Lineage of
data
• Desire for Self-service analytics by
Business consumers
• Ability to work effectively across data
silos (Big Data Fabric)
• Significant increase in R&D by major
vendors
• AI and ML as a differentiator and
accelerator

13. CASE STUDY: METADATA MANAGEMENT TOOLS
SAP
Database
Oracle
Database
SQL Server
Database
Document
Man.
System.
(TRIM)
Spreadsheets
File Systems
Cloud
Storage
Sharepoint
Content
Man.
System.
Metadata
Metadata
Metadata
Metadata
Metadata Metadata
Metadata
Metadata
Metadata
In many different places!
Wherever there is Data of some kind,
s lly y ’ll fin M .
METADATA – WHERE DO WE FIND IT IN THE DIGITAL WORKPLACE?

14. ENTERPRISE DATA CATALOGUE TOOLS
Enterprise Metadata Management Solutions (“Data
Catalog es”) are available from many tool vendors. A core
functionality of these products is the ability to connect to many
different kinds of data technologies, automatically “s n” h s
systems, and capture the metadata present in them.
Depending on the size and number
of systems that the scanners are
connected to, a cataloguing tool
may capture very large quantities
of raw metadata, corresponding to
00,000’s v n milli ns f
objects (source data and data work
products).

15. MANY DIFFERENT KINDS OF METADATA

16. CHALLENGES WITH BUILDING METADATA CAPABILITIES
➢ Software product vendors tend to focus on tools, not capabilities
➢ V n ’s m s f n iv f m specific function:
▪ Data Dictionary of a DBMS
▪ Repository of Data Domain Definitions in a Data Quality tool
▪ Data Transform Definitions in an ETL tool
▪ Workflow Definitions for Data Governance Workflows
▪ Model Repository for Process and/or Data Modelling tools
▪ Metadata Repository for Standard Definitions of Business terms (Meteor, Aristotle)
➢ No metadata management product from any of the major vendors supports ALL the
functionality required for Enterprise Data Management
➢ Some vendors provide a suite of products that has good coverage of the fundamental
EDM capabilities (DG, Meta, DQ, Modelling, Ref Data)

17. THERE IS NO “ONE TOOL TO RULE THEM ALL”

18. DATA SERVICES ENABLED BY TYPES OF METADATA
• M s sin ss s s n’ n s
metadata directly through metadata tools
• Metadata is an enabling technology for Data Services,
and the consumers of these services may not directly
see the underlying metadata (e.g. Google search)
Data Service
Consumers

19. COVERAGE OF THE TOOLS

20. f
g h
i
k l m
n
s
nd to nd
ocess
nd to nd
ocess
ns m
ns m ns m
ns m
ns m
ns m
ns m
s ness ea s ness ea
s ness ea
THE RIGHT WAY TO ACQUIRE SOFTWARE TOOLS
KNOW YOUR BUSINESS:
Start With The Business Processes that Consume and Produce Data

21. STEPS IN THE SELECTION PROCESS

22. SOME WRONG WAYS: THE REVERSE JOURNEY

23. SOME WRONG WAYS: VENDOR/CIO GROUP DRIVEN

24. SOME WRONG WAYS: “LET’S TAKE THE SHORT CUT”

25. GENERAL APPROACH – APPLICABLE TO MANY TOOLS
➢ Focus for this presentation is mainly on Metadata Management tools.
➢ However, the overall approach is the same for other data management tools
(e.g. Data Governance and Data Quality tools):
1) Understand Business Needs and pain points
2) Identify the Data Service(s) [Use Cases] to satisfy those Needs
3) Map the Data Services to tool Functionality (and its associated metadata)
4) Select which tools are candidates, understand their coverage of
functionality and integration capability with other tools
5) Evaluate candidates, choose the most appropriate
6) Deploy tools, configure capabilities, and publish the data service(s)
n’ ly n singl in f g nis i n l x is h i y in h
s l i n ss. n’ k sh s!
➢ Remember, its not just the tools… A ABILITIE in l :
Policy, People, Knowledge, Process, Automation (Software Tools)

26. EXAMPLE: DATA QUALITY CAPABILITIES - NOT JUST THE TOOLS

27. 27
© Catapult BI Pty Ltd
STARTING THE JOURNEY - IMPORTANCE OF GOOD GUIDANCE

31. •
•
•
•

32. The five elements of data integrity
Data Data Data Location Data
Integration Governance Quality Intelligence Enrichment
Connect today's Define the policies, Understand your data Analyze location data for Power enhanced decision
infrastructure with processes, rules, roles and ensure it is accurate, enhanced and making with expertly
tomorrow's technology and responsibilities to consistent and complete actionable business curated, up-to-date
to unlock the potential of manage, use and protect for confident insights that drive business, location, and
all your enterprise data your data business decisions superior outcomes consumer data
• • • • •
DATA INTEGRITY

38. •
•
•
•

40. Improve the ability to find, trust and use critical data
such as provider and patient data
Establish and monitor adherence to policies including
privacy etc.
Visualise how data flows through the organisation and
the business processes that use it
Determine which data assets can be trusted based on a
set of criteria
Understand what data is available internally and
externally for real-time analytics

44. -Gartner
Business-first use including
3D Data Lineage
Embedded data quality and
data analysis
Easily configured & flexible
metamodel, dashboards
and workflow
Ability to harvest metadata
from external systems as a
native part of the platform
Precisely
The Precisely Data360 Difference: Data Governance

45. •
•
•
•
•
•
•
•