A PRIMER FOR PROCESS-DRIVEN
DATA GOVERNANCE
For well over 15 years, the high-level concept and mission of
data governance has essentially remained unchanged. Consider
Margaret Rouse’s definition from 2007: “Data governance refers
to the overall management of the availability, usability, integrity
and security of the data employed in an enterprise. A sound
data governance program includes a governing body or council,
a defined set of procedures, and a plan to execute those
procedures”.
1Increasingly complex and disparate computing environments, the sheer volume of big data and the device-driven data-mining opportunities created by the Internet of Things (IoT), as well as seismic data center shifts resulting from mergers and acquisitions have reinforced the businesscritical need to create, govern and enrich good and consistent, sharable enterprise-wide data.
Once a company recognizes and acknowledges the potential and sometimes process-crippling downside of poor data quality, it is more apt to embrace some kind of data governance program. The “governing body or council” should be comprised of both technology and business specialists. But since data governance initiatives tend to start at the project level, as opposed to organizationally top-down or company-wide, a “defined set of procedures” needs to be repeatable and scalable.
The conventional tools for data governance
Attendees of data governance seminars and conferences know there’s always a point in the proceedings where the audience is invited to take a refreshment break in the vendor exhibition hall. There, they are encouraged to view the latest technologies available for executing data governance procedures. These tools very well might include:
TABLE OF CONTENTS 1 Introduction
2 The data governance exhibition hall 3 Typical MDM implementation challenges 4 Process-driven MDM
5 Scaling up and enabling process-driven data governance
7 Conclusion 7 About the author
1 “http://www.techtarget.com/contributor/ Margaret-Rouse”
Margaret Rouse
The Data Governance Exhibition Hall
Data modeler—Organizes business tables and columns and their relationships for operational and analytical business systems
Extract Transform Load (ETL)—Transforms data from disparate sources into a single and query-ready format
DQ matching engine—Typically associated with deduping, cleansing and standardizing customer data
Address validation—Cleanses, updates and optionally applies geocoding through verification against best-in-breed postal directories
Governance workflow—Orchestrates a data-centric and collaborative approval process to manage data standards and requested data changes
Hierarchy management—Maintains and administrates business affiliations and relationships amongst data items
Reference Data Management (RDM)—Manages the creation, enrichment and governance of internal and external code associated with master data
Metadata management—Manages the creation, enrichment and governance of metadata, typically associated with terms and definitions
Master Data Management (MDM)—Provides a centralized and holistic approach to the creation, governance and deployment of shared enterprise data
Enterprise Service Bus (ESB)—Integrates applications over a bus-like structure
The process orientation of master data governance
A versatile, multi-domain MDM solution is often referred to as a data governance tool. In actuality, however, MDM should be considered a data governance platform because of its ability to combine and orchestrate so many of the capabilities in our exhibition hall, which are essential, data-quality components.
Software AG’s MDM offering, webMethods OneData, offers these capabilities out-of-the-box. webMethods OneData offers everything you need in one MDM solution. So if you were to tour the OneData booth, you’d find:
The webMethods OneData booth
Data modeler—Configures models, DDL statements but also imports third-party schemas
Data acquisition—Interchange mapping transforms data from disparate sources into a single, data-model-governed format
Data quality matching engine—Internal to OneData, used in consolidation-style MDM Address validation—An integrated add-on, provided by webMethods Locate
Governance workflow—Native to OneData’s management of master data, reference data, metadata and hierarchies
Hierarchy management—Flexibly modeled within OneData and supported through MDM’s governance paradigm
Typical MDM implementation challenges
With so many moving parts and integrated functionality, how do you control the process of MDM? How do you consistently control and coordinate so many varying data quality tools within an MDM solution? What’s more, how do you successfully align this functionality and the data it manipulates to real-world business requirements? Typical challenges to successful MDM implementations include:
• An overly data-centric approach that underestimates process complexity and its business relationship
• Unclear project scope due to requirements not being clearly defined
• Viewing MDM projects only as an implementation process of the actual tool(s) • Unclear roles and responsibilities related to master data
Process-driven MDM
The first driver for an MDM system acquisition should be tied to process improvement. Software AG has long advocated any implementation of MDM to be tied to three process-driven MDM precepts or pillars of success:
• MDM is a business–driven discipline supporting process optimization or transformation
• MDM program scope is directly driven by process optimization needs, and MDM investments are tied to and measured by process improvement ROI
• MDM should follow a cross-disciplinary approach involving stakeholders from different functions or business areas impacted by the optimized process
It’s the responsibility of the business to pinpoint how good and consistent data quality will improve process performance, help enable major initiatives by removing bad data bottlenecks and increase the bottom line. In turn, IT is tasked to find and evaluate the best technology platform for the job.
When viewed as a technology process, an MDM solution is essentially comprised of three major integrated parts or phases:
1. Data modeling and acquisition: Includes data modeling and the ability to import, format and pull data from all relevant sourcing systems
2. Data governance: Includes excepting failed imports, monitoring external system connections, imposing authorization and stewardship controls, matching/cleansing, data-quality dashboarding and a workflow/approval process
3. Deployment: Includes (upon approval) distribution of cleansed data back to systems of origination (consolidation style), beneficiary subscribing systems (coexistence style) and research-accessible or business operational viewers (iPad®, intranet, portals or
embedding for third-party applications)
Best practice would dictate there must be clearly planned and delineated process steps that drive MDM to create its most desired result: an enterprise-accepted single version of any multi-domain master data, code sets or hierarchies.
Fundamentally, the process is selectively driven by data-management use cases (for example, code sets don’t require enrichment through geocoding and product data requires a different data quality engine than customer data). But process requirements must be executed as a series of sequential events.
For example:
Data modeling drives data imports ---> Imports drive DQ cleansing and marching –> suspect names that can’t be delegated as new (gold record), or matched to an existing gold record trigger a more manual workflow process for additional review. In webMethods OneData, workflow is its own, generic, multi-layered process: • Workflow rules are configured to create approval routes based on specific events
or event/attribute combinations
• Data-driven workflow rules route the approvals based on the data value(s) or type of change involved
• Data changes trigger internal alerts and notification through corporate email • Additional rules in terms of mandatory attributes can be required (product data
might require an upload jpg)
• Data is validated and approved by business owners per best practice
When viewed as a technology process, an MDM solution is essentially comprised of three major integrated parts or phases.
While the workflow process is a data management process, it also must be orchestrated as a business process. Consider the following simple real-world scenario with business-specific, workflow-routing and role-driven governance:
Step 1, the data management business requirement: Fictional Acme Mechanics decides to move its entire sales department from Philadelphia, Pennsylvania to corporate headquarters in Yonkers, New York.
Step 2: A data manager, filtering on all sales people in Philadelphia, performs a bulk update in the MDM hub, changing all instances of city and state fields from Philadelphia to Yonkers, and Pennsylvania to New York, respectively.
Step 3: The city and state field changes trigger a business rule for the workflow/ approval process, which notifies HR data stewards to review and validate the initial employee changes.
Step 4: Since it is unrealistic to expect that all sales personnel in Philadelphia will choose either to commute or live closer to Yonkers, HR must validate the work status of every employee. If the employee has agreed to work in Yonkers, the bulk update will be
Workflow
Update name Update Effective Dates Add Update Ragged Role Type Name Discription Effective from Data Effective to DateAND
OR
User A Anyone from Group 1 User B Anyone from Group 1 User D User C OR
User B User C User D
User C
Scaling up and enabling process-driven data
governance
webMethods OneData’s MDM system can be sized, for example, as a reference data management solution governing and deploying 10,000 codes sets or scaled up to B2C customer data management use cases, for the data governance processing of millions of records. Very large and complex enterprises, however, generating even larger data volumes in disparate formats will require scaling up the MDM solution in order to maintain timely data governance execution.
Software AG’s Digital Business Platform provides a series of business modeling and integration tools that increase the operational efficiency of MDM’s process-driven data governance.
Software AG’s Digital Business Platform provides a series of business modeling and integration tools that increase the operational efficiency of MDM’s process-driven data governance.
1. Modeling data governance process flows and org charts with ARIS
Modeling and establishing data governance’s “defined set of procedures” is a business process that should be documented and codified. Software AG’s ARIS can be used to effectively model any number of enterprise processes, such as continuous process improvements and governance, policy management, requirements management and even modeling data governance org charts.
ARIS can help tie the governing of master data to major corporate initiatives, such as Customer 360, on-boarding or supply chain optimization by further defining MDM as an integral part of organizational processes. Not only does this approach increase visibility around the importance of good and consistent data (as well as clearly identifying specific, data consumption by system location), it also provides another way of scoping out and solidifying the data management partnership between business and IT.
Consequently, this makes ARIS an ideal solution in the planning and management of processdriven MDM. By creating a process and business-driven conceptual model (and using non-technical, business-friendly terminology), ARIS helps lay the groundwork for the realization of an actual MDM-domain data model, which in turn can help propagate data governance procedures through the realization of a conceptual and physical canonical data model. The technical synergy between ARIS and webMethods OneData MDM is based on the export/import of the XML modeling schemas from ARIS into OneData, thereby by introspecting the ARIS structure into OneData data objects. By leveraging this flexible modeling interchange between the two solutions, the value of ARIS business process modeling can be readily used to build and implement OneData MDM use cases.
2. Extending MDM’s workflow governance through task-driven BPM
As illustrated in the HR workflow example, OneData’s MDM workflow is a data-centric process. webMethods BPMS has a similar mission. Software AG’s webMethods Business Process Management (BPM) Platform, in fact, is itself a governance and workflow tool that can collaboratively orchestrate, manage and monitor end-to-end business processes. By increasing agility and enabling users to dynamically react to change, BPM minimizes and even eliminates disruption to the workflow process.
When combined with MDM, BPM effectively broadens the process-power of MDM’s own workflow capabilities. OneData’s workflow is remotely leveraged by direct integration with webMethods BPMS, bringing an additional level of enterprise data enrichment. Once an external workflow has been executed in webMethods BPMS, control is then returned to OneData.
3. Large-scale MDM connectivity integration through webMethods Integration Server
Data governance is all about the relationship between MDM and its relationship to both enterprise sourcing systems of record and subscribing systems that can benefit from newly cleansed and reconciled data. Software AG’s webMethods Integration Server provides critical support through its library of adapters, providing fast data delivery and language translation between OneData’s MDM repository and popular— if proprietary—enterprise solutions.
The deployment of adapters also helps considerably shorten MDM project
implementation time, especially in complex and systematically disparate data centers. In the case of the SAP® ERP solution, for example, the webMethods SAP adapter
provides support for SAP’s Application Programming Interface (BAPI), the ability to execute SAP Remote Function Calls (RFC), as well as routing capabilities for iDocs (see Figure C). Additional webMethods adapters are available to support OneData MDM implementations in Oracle®, Peoplesoft® and JD Edwards™ ERP system environments,
as well as providing adapter support for Siebel™, Salesforce.com®, IBM® Web-Sphere®
and AS/400.
An ESB enabling enterprises to rapidly integrate systems, services, devices, processes, business partners and data is key to large-scale MDM and data governance solutions.
Conclusion
Increasingly, companies within the Global 2000 understand data governance is not just a nice-to-have but a critical necessity to maintain quality of business output and competitiveness. Data quality should not be viewed as a random application add-on but a core component to business processes and their operational efficiency.
In addition to systematic implementation, MDM is a system that requires a systematic and process-oriented execution to maximize its effectiveness.
To learn how to apply process-driven MDM to your data-quality challenges, contact your Software AG representative. Additional information is available at www.softwareag.com/mdm
About the author
Charlie Greenberg is Software AG’s Sr. Global Product Marketing Manager for webMethods
OneData MDM and has supported the OneData product since March of 2008. In addition to being a speaker and panelist at events sponsored by DAMA, IDMA, Data Management Forum and the MDM Institute, Charlie’s writings on MDM can be viewed on “Database Trends & Analysis,” ”Sand Hill,” “Dashboard Insights” and Software AG’s “Reality Check.”
