Maximize Return On Data Through Data Governance

Are you delivering measurable business value (e.g., compliance/risk reduction; efficiencies/cost reduction; growth; strategic differentiation) from data management programs and investments?   Hopefully many of you can say that yes, through traditional investment in data management best practices, skilled resources and enabling technologies you have provided business value.  But for many, the business value delivered is often less than promised or anticipated – and it’s even more difficult to get the necessary funding and prioritization to scale the solution to deliver greater value. Why does this business value ceiling exist and why is it so difficult to break through? 

Prevalent data management initiatives (i.e., data integration, data quality, data archiving, data masking, master data management, data warehousing, business intelligence, analytics) when managed as tactical, IT-driven efforts often deliver solid returns within the targeted environment or business area.  But efforts to scale these solutions to support cross-enterprise objectives often crash and burn.  To break through this business value ceiling – and maximize your return on data – senior business leaders must finally accept accountability and establish sustainable data governance practices within the organization.    

The Times They Are A Changin’

Data Governance!? Nice and cliché – easier said than done, right? I can guess what you’re thinking: “That’s all well and good, but where do we start?  Who owns it?  Who PAYS for it?  How do we build a business case for it and measure its business impact?  How does it survive shifts in priority and business leaders’ short attention spans?”   

All these questions and more must be answered – especially since the inability to answer them is usually the reason many organizations don’t even bother trying.   But as Bob Dylan says, “The times they are a changin’.” With today’s fast pace of innovation, organizations can no longer maintain competitive differentiation on high quality products or services alone – that’s table stakes.  To ensure customer loyalty and market leadership, organizations must differentiate on the processes that run your business and optimize the customer experience – all of which require trusted, secure data.  So ignoring data governance is no longer a viable option.

In addition, if you struggle to demonstrate promised value in your mature, traditional data management environments, how can you possibly exploit the tremendous potential from the emerging data capture and usage megatrends of cloud, social, mobile and big data? Your organization may not be focusing on all of these megatrends right now, but I’d be willing to bet that your CIO and CEO have had some serious discussions about the implications of at least one or two of them. 

But beware, there should be no separate competency for “Big Data Governance” or “Master Data Governance” separate from Data Governance – unless you want to create new silos.  No matter whether your data lives in an ERP system, a data warehouse, a Hadoop environment, an MDM Hub, or in the Cloud, it needs to be governed by the same organizational data policies, standards and processes.  To create separate data governance organizations based on the usage scenarios and persistence methods misses the point entirely and will create a host of new problems when you want to share and integrate the data across these environments.

Data Governance and Stewardship

We want to help our customers answer these and other tough questions, mitigate inhibitors and navigate the rough data governance waters in the optimal way for their organizations.  I’ll be posting an ongoing series of blogs sharing Informatica’s data governance best practices, frameworks, process guidelines, tips and tricks, and assessment tools.  In addition, I’ll share how the Informatica platform will continue to support data stewardship capabilities spanning data governance maturities, use cases and vertical scenarios.

To kick off this first-of-many data governance discussions, I’d like to share some level-setting definitions:

Data Governance

The functional coordination and decision-making framework for the definition of policies, processes, standards, technologies, roles and responsibilities across the organization to manage data as a corporate asset  to ensure the availability and controlled growth of accurate, consistent, secure and timely data for better decision making, reduced risk and improved business processes.

Data Stewardship

The set of front-line responsibilities performed collaboratively between business and IT subject matter experts to discover, define, apply, measure and monitor enterprise data in compliance with defined enterprise data governance standards and policies.








The ultimate vision should be to use data governance and data stewardship best practices to scope, enable and measure the delivery of secure, trusted data across the most valued business objectives spanning an organization’s functional, business line, data and application silos – including the ability to scale to next generation data challenges from social, cloud, mobile, big data and beyond.

Informatica 9.5 Release Enables Data Governance

Here at Informatica, we support data governance objectives by enabling data stewardship processes throughout our platform.  In addition to the robust capabilities already built in to our existing products, some of the new and exciting data stewardship capabilities being enabled and enhanced as part of the upcoming Informatica 9.5 platform release include: 

  • Embeddable Scorecards in Informatica Data Explorer (IDE) and Informatica Data Quality (IDQ) quickly and easily embeds contextual data scorecards in any Web application.
  • Centralized Management Console within Informatica’s data privacy solution enables centralized, role-based global data privacy management.
  • Data Steward Inbox and Workflow in IDQ delivers data steward dashboard to facilitate the easy and fast resolution of data-related issues and provides comprehensive task workflows to streamline the resolution of data quality problems.
  • Domain and Enterprise Discovery in IDE infers functional meaning within enterprise data, automates identification of key master data elements, and automates data profiling processes across enterprise data environments.
  • Common data quality and master data stewardship user interface promotes broader deployments and increased collaboration across DQ and MDM efforts.

I look forward to sharing many of the core concepts behind our data governance methodology with you, and welcome comments on what we can do to better support your data governance efforts to ensure your organization can maximize its return on data!

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