Rob Karel
Asset in, Garbage Out: Measuring data degradation
Following up on the discussion I started on GovernYourData.com (thanks to all who provided great feedback), here’s my full proposal on this topic:
We all know about the “Garbage In/Garbage Out” reality that data quality and data governance practitioners have been fighting against for decades. If you don’t trust data when it’s initially captured, how can you trust it when it’s time to consume or analyze it? But I’m also looking at the tougher problem of data degradation. The data comes into your environment just fine, but any number of actions, events – or inactions – turns that “good” data “bad”.
So far I’ve been able to hypothesize eight root causes of data degradation. I’d really love your feedback on both the validity and completeness of these categories. I’ve used similar examples across a number of these to simplify. (more…)
Purveyors of Dirty Data: Mystery Vendors Peddle User Lists
Since I joined Informatica over a year ago, I’ve received a daily stream of unsolicited emails from vendors selling “marketable user email/contact list databases” of myriad software and hardware technologies ranging from enterprise apps, business intelligence, Cloud computing, networking and infrastructure, etc. You get the idea – and I’m sure many of you experience a similar phenomenon on a daily basis.
My catalyst for writing a post about this is when I considered the relevance, transparency and quality requirements that data governance leaders strive for –and how these vendors seem to dismiss all of the above. (more…)
Build A Prioritized Data Management Roadmap
In my recent white paper, “Holistic Data Governance: A Framework for Competitive Advantage”, I aspirationally state that data governance should be managed as a self-sustaining business function no different than Finance. With this in mind, last year I chased down Earl Fry, Informatica’s Chief Financial Officer, and asked him how his team helps our company prioritize investments and resources. Earl suggested I speak with the head of our enterprise risk management group … and I left inspired! I was shown a portfolio management-style approach to prioritizing risk management investment. It used an easy to understand, business executive-friendly visualization “heat map” dashboard that aggregates and summarizes the multiple dimensions we use to model risk . I asked myself: if an extremely mature and universally relevant business function like Finance manages its business this way, can’t the emerging discipline of data governance learn from it? Here’s what I’ve developed… (more…)
Data Governance: How Immature Can You Be?
No organization begins to implement a data governance program from an entirely blank slate; every organization likely has some capabilities to leverage. Determining an organization’s current level of data governance maturity is a useful and necessary first step in developing a customized plan that is both relevant and executable. So how do you assess your maturity? Well throw a rock in any direction and you’re likely to hit a software vendor, consulting company or industry analyst that offers a maturity model and assessment tool to support your data management and data governance efforts. Actually don’t throw rocks, you could hurt somebody. (Yes, we offer one too – more on that below). (more…)
Introducing GovernYourData.com!
I’m excited to officially announce the public launch of www.GovernYourData.com, a new one-stop data governance resource center and online community hosted and sponsored by Informatica. This vendor-neutral site is open to all data governance stakeholders, solution providers and thought leaders (no relationship with Informatica is required) and we welcome any non-promotional content and contributions that share best practices, tips and tricks that aim to help data governance evangelists succeed. (more…)
Data Governance Program Management: Herding Chickens – Really Important Chickens!
For the final facet of our data governance framework, I’ve intentionally saved Program Management for last. I felt to fully demonstrate why your organization must invest in skilled program managers I should first introduce all the simultaneous moving parts that make up a comprehensive data governance strategy. (For links to my posts deep diving into each facet, see my blog page). (more…)
The Carrot and Stick Are Must-Haves in the Data Governance Toolbox
The next facet of our Data Governance Framework recognizes the key dependency any data governance-related effort has on change management. No matter how compelling the vision and business case, making data a trusted corporate asset on par with your financial and people assets is a major culture shift for most organizations.
Data Governance Measure & Monitor Processes Open a Window to Data and its Value
As mentioned in my post describing the major business processes that comprise a data governance function, the Measure and Monitor processes i) capture and measure the effectiveness and value generated from data governance and stewardship efforts, ii) monitors compliance and exceptions to defined policies and rules, and iii) enables transparency and auditability into data assets and their life cycle. (more…)
Data Governance Apply Processes Operationalize Human and Machine Roles
As mentioned in my post describing the major business processes that comprise a data governance function, the Apply processes aim to operationalize and ensure compliance with all the data governance policies, business rules, stewardship processes, workflows, and cross-functional roles and responsibilities captured through the Discover and Define process stages. (more…)
Data Governance Define Processes Capture Business Policies, Rules and Standards
As mentioned in my post describing the major business processes that comprise a data governance function, the Define processes documents data definitions and business context associated with business terminology, taxonomies, relationships, as well as the policies, rules, standards, processes, and measurement strategy that must be defined to operationalize data governance efforts. This process runs parallel and is iterative to the Discover process stage as Discovery drives Definition, and Definition drives more targeted focus for Discovery. (more…)
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