Data governance (DG) is vital to the success of master data management (MDM) projects. Unfortunately, the DG market remains fragmented and unfocused on the requirements of large MDM initiatives.
Contemporary solutions are essentially “reactive DG” due to their approach as “data steward consoles” of downstream data quality issues. The much anticipated solution would be “active DG” wherein both upstream and downstream lifecycle processes are integrated end-to-end via workflow such that evergreening of the DG rules is a continuous process improvement cycle benefitting the enterprise. Sadly, many of the currently marketed DG solutions consist primarily of white papers and demo-ware, or “passive aggressive DG”; that is they purport to deliver a certain capability, yet provide otherwise.
Enterprise-level DG that includes entire master data lifecycle (creation, promotion, archiving, …) is extremely difficult to execute for a number of reasons – organizationally and technically. Yet increasingly this is being mandated as a core deliverable of large-scale MDM projects.
Through 2009-10, both major systems integrators and MDM boutique consultancies will focus on productizing their DG frameworks/methodologies while MDM software providers struggle to link upstream DG processes with downstream MDM hubs.
By 2011-12, all mega vendor MDM solutions will evolve from “passive aggressive DG” mode to “active DG” wherein they provide the capabilities to capture business rules which in turn are propagated into an MDM.
By 2012, both corporate and line-of-business data stewards will be a common position as Global 5000 enterprises formalize this function amidst increasing de facto and de jeure recognition of information as a corporate asset.; moreover, governmental compliance mandates will require that financial services organizations provide “proof of data governance” as a proactive measure.
In 2H2009, the MDM Institute conducted a multi-client survey of early adopters and evaluators of DG initiatives across a broad range of MDM projects. Based on an increasing dictate for process rigor regarding governance of customer data, and increasing recognition of the necessity to treat DG as a prerequisite for large scale or enterprise MDM programs, clearly Global 5000 enterprises are now recognizing the opportunity to take a more strategic view of DG.
This “best practices” session will provide insight into these key issues: • What are the business drivers for enterprise-strength DG? • What are the technology challenges in implementing DG for enterprise magnitude business problems? • Why is “active” DG superior to “passive” or “passive-aggressive” DG? • How are large enterprises justifying and catalyzing their DG processes? • Which are the “desirable” vs. “essential” DG solution criteria – e.g. GUI sizzle for hierarchy management vs. end-to-end team-based governance for the data steward function • How does an organization organize and execute through the four stages of DG maturity: anarchy (basic), IT feudalism (foundational), business monarchy (advanced), and federalism (acme)
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What are the business drivers for enterprise-strength DG? What are the technology challenges in implementing DG
for enterprise magnitude business problems? Why is “active” DG superior to “passive” or “passive-
aggressive” DG? How are large enterprises justifying and catalyzing their
DG processes? Which are the “desirable” vs. “essential” DG solution
criteria – e.g. GUI sizzle for hierarchy management vs. end-to-end team-based governance for the data steward function
How does an organization organize and execute through the four stages of DG maturity: anarchy (basic), IT feudalism (foundational), business monarchy (advanced), and federalism (acme)
Enterprise-level DG that includes entire master data lifecycle (creation, promotion, archiving, …) is extremely difficult to execute for a number of reasons – organizationally & technically. Yet increasingly this is being mandated as a core deliverable of large-scale MDM projects.
Through 2009-10, both major systems integrators & MDM boutique consultancies will focus on productizing their DG frameworks/methodologies while MDM software providers struggle to link upstream DG processes with downstream MDM hubs.
By 2011-12, all mega vendor MDM solutions will evolve from “passive aggressive DG” mode to “active DG” wherein they provide the capabilities to capture business rules which in turn are propagated into an MDM.
Integrate processes across the enterprise – including corporate technology, all LOBs, functional areas & geographic regions
Engage all levels of management & adjudicate between centralized vs. decentralized data stewardship
Evolve key stakeholders from “data ownership” to “data stewardship”
Overcome lack of process integration in current “DG for MDM” offerings
Based on recognition of issues at hand, an improving economy, & increasing regulatory requirements, businesses are now recognizing oppty to take more strategic view of enterprise data governance
During 2009, most enterprises will struggle with cross-enterprise DG scope as they initially focus on customer, vendor, or product; enterprise-level DG that includes entire master data lifecycle will be mandated as core phase 0/1 deliverable of large-scale MDM projects
Through 2010, major SIs & MDM boutiques will focus on productizing DG frameworks while MDM software providers struggle to link governance process with process hub technologies; concurrently G5000 enterprises struggle to evolve enterprise DG in cost-effective & practical way from “passive” to “active” DG modes
By 2011-12, vendor MDM solutions will finally move from “passive-aggressive DG” mode to “active DG”
MDM MILESTONE
Data governance will remain problematic during 2009-10
Data governance (DG) is vital to success of MDM projects – both initially & ongoing
During 2009-10, Global 5000 enterprises will increasingly mandate that 'no MDM program be funded without pre-requisite DG framework’
Moreover, market-leading vendors will come to market in late with own active DG frameworks to take back the lucrative DG business currently defaulting to SIs
Corollary is few MDM vendors will be able to market their solutions without integrated active DG capability – one that embeds a workflow engine with metadata support for both structured & unstructured info
Where will that leave the SIs – as partners, competitors or both?
Given lack of DG solutions from MDM platform vendors, SIs have had market largely to themselves; 2010 operative word = “coop-etition”
1. In some countries, DG will become regulatory requirement & companies will have to demonstrate DG practices to regulators as part of regular audits. This will likely affect Financial Services industries first, & will emerge as a growing trend worldwide.
2. Value of data will be treated as an asset on balance sheet & reported by the CFO while quality of data will become technical reporting metric & key IT performance indicator. New accounting & reporting practices will emerge for measuring & assessing value of data to help organizations demonstrate how DQ fuels business performance.
3. Calculating risk will become an IT function. Today in most organizations, risk calculation is done by a select group of individuals using complicated processes. In future, risk calculation will be automated allowing companies to more easily examine past exposure, forecast risk they face in future, & set aside capital to self-insure to cover risk.
Predictions of IBM Data Governance Council - continued
4. Role of CIO will change making this corporate officer responsible for reporting on DQ & risk to Board of Directors. CIO will have mandate to govern use of info & report on quality of info provided to shareholders.
5. Individuals will be required to take more responsibility for recognizing problems & participating in governance process to facilitate greater operational transparency & identification of risk. They will be aided by new categories of operational software that will demonstrate common DG problems & allow employees to self-govern; sponsor & vote on new policies; provide feedback on existing ones & participate in dynamic DG.
IBM DG Council established right approach – assessing DG from a maturity perspective across 11 categories with "Entry Points" to enable organization
to embrace more pressing needs while being able to tackle other aspects when ready
“Anarchy” (basic) – Application-centric approach; meets business needs only on project-specific basis
“Feudalism” (foundational) – IT policy-driven standardization on technology & methods; common usage of tools & procedures across projects
“Monarchy” (advanced) – business-driven, rationalized data with data & metadata actively shared in production across sources
“Federalism” (distinctive) – SOA (modular components), integrated view of compliance requirements, formalized organization with defined roles & responsibilities, clearly defined metrics, iterative learning cycle
0%
10%
20%
30%
40%
50%
Basic Foundational Advanced Distinctive
FSP
Non-FSP
Source: MDM Institute survey of 100+ Global 5000 IT organizations
Fin Svc providers are leading the way – in spend & discipline; technology can only achieve so much as organization must be prepared to continually adapt & treat data as enterprise asset above project level;
100 organizations who receive unlimited MDM advice to key individuals, e.g. CTOs, CIOs, & MDM project leads
Representative Members
3M Bell Canada Caterpillar Cisco Systems Citizens Communications COUNTRY Financials Educational Testing Svcs EMC GE Healthcare Honeywell IHS Intuit Loblaw McKesson Medtronic
Microsoft Motorola National Australia Bank Nationwide Insurance Norwegian Cruise Lines Novartis Polycom Roche Labs Rogers Communications Scholastic Stryker SunTrust Sutter Health Westpac Weyerhaeuser Woolworths