Tag Archives: Information Lifecycle Management
A few months ago, while addressing a room full of IT and business professional at an Information Governance conference, a CFO said – “… if we designed our systems today from scratch, they will look nothing like the environment we own.” He went on to elaborate that they arrived there by layering thousands of good and valid decisions on top of one another.
Similarly, Information Governance has also evolved out of the good work that was done by those who preceded us. These items evolve into something that only a few can envision today. Along the way, technology evolved and changed the way we interact with data to manage our daily tasks. What started as good engineering practices for mainframes gave way to data management.
Then, with technological advances, we encountered new problems, introduced new tasks and disciplines, and created Information Governance in the process. We were standing on the shoulders of data management, armed with new solutions to new problems. Now we face the four Vs of big data and each of those new data system characteristics have introduced a new set of challenges driving the need for Big Data Information Governance as a response to changing velocity, volume, veracity, and variety.
Before I answer this question, I must ask you “How comprehensive is the framework you are using today and how well does it scale to address the new challenges?”
While there are several frameworks out in the marketplace to choose from. In this blog, I will tell you what questions you need to ask yourself before replacing your old framework with a new one:
Q. Is it nimble?
The focus of data governance practices must allow for nimble responses to changes in technology, customer needs, and internal processes. The organization must be able to respond to emergent technology.
Q. Will it enable you to apply policies and regulations to data brought into the organization by a person or process?
- Public company: Meet the obligation to protect the investment of the shareholders and manage risk while creating value.
- Private company: Meet privacy laws even if financial regulations are not applicable.
- Fulfill the obligations of external regulations from international, national, regional, and local governments.
Q. How does it Manage quality?
For big data, the data must be fit for purpose; context might need to be hypothesized for evaluation. Quality does not imply cleansing activities, which might mask the results.
Q. Does it understanding your complete business and information flow?
Attribution and lineage are very important in big data. Knowing what is the source and what is the destination is crucial in validating analytics results as fit for purpose.
Q. How does it understanding the language that you use, and can the framework manage it actively to reduce ambiguity, redundancy, and inconsistency?
Big data might not have a logical data model, so any structured data should be mapped to the enterprise model. Big data still has context and thus modeling becomes increasingly important to creating knowledge and understanding. The definitions evolve over time and the enterprise must plan to manage the shifting meaning.
Q. Does it manage classification?
It is critical for the business/steward to classify the overall source and the contents within as soon as it is brought in by its owner to support of information lifecycle management, access control, and regulatory compliance.
Q. How does it protect data quality and access?
Your information protection must not be compromised for the sake of expediency, convenience, or deadlines. Protect not just what you bring in, but what you join/link it to, and what you derive. Your customers will fault you for failing to protect them from malicious links. The enterprise must formulate the strategy to deal with more data, longer retention periods, more data subject to experimentation, and less process around it, all while trying to derive more value over longer periods.
Q. Does it foster stewardship?
Ensuring the appropriate use and reuse of data requires the action of an employee. E.g., this role cannot be automated, and it requires the active involvement of a member of the business organization to serve as the steward over the data element or source.
Q. Does it manage long-term requirements?
Policies and standards are the mechanism by which management communicates their long-range business requirements. They are essential to an effective governance program.
Q. How does it manage feedback?
As a companion to policies and standards, an escalation and exception process enables communication throughout the organization when policies and standards conflict with new business requirements. It forms the core process to drive improvements to the policy and standard documents.
Q. Does it Foster innovation?
Governance must not squelch innovation. Governance can and should make accommodations for new ideas and growth. This is managed through management of the infrastructure environments as part of the architecture.
Q. How does it control third-party content?
Third-party data plays an expanding role in big data. There are three types and governance controls must be adequate for the circumstances. They must consider applicable regulations for the operating geographic regions; therefore, you must understand and manage those obligations.
Today’s data pours into your business from more sources than ever before. It arrives in in all shapes, sizes and colors, changing every department and discipline along the way. Trying to manage it can feel like playing a gazillion simultaneous games of pong.
To get a handle on this, you have to put data first. This requires data-centric mindset, which will will help you:
- Embrace data volume and put it to use
- Fuse data flows in meaningful ways
- Inject data into dynamic business intelligence
- Explode silos and utilize your data assets across all departments and apps
To help with this, we’ve created a guide called The New Data Dynamics. This eBook explains the mindset shift needed to thrive. Discover the new data dynamics and learn:
- Why you need to think “data first,” not just application-centric
- What it looks like to put data first
- How to benefit from data that’s ready for anything, including:
- Combining with new data sources
- Integrating with new applications
- Feeding analytics and business intelligence
Data has changed forever. Embrace the new mindset and exploit the potential of your most powerful business asset yet. Download The New Data Dynamics eBook and learn how.
If you haven’t already, check out the Potential at Work for Information Leaders site. We’ve just posted three great new articles designed to help you be more successful:
- “Driving value without locking down your data” Securing your data doesn’t mean inhibiting its use – far from it. Did you know that effective data masking practices allow information leaders to optimize the value data delivers to the organization while ensuring its security? Some forward-thinking information leaders are doing this and getting great results.
- “How fresh is your data?” Simply delivering data is not good enough anymore. You must get it to the right people at the right time while it is still fresh enough to be useful. Find out how to do it right.
- “Turn an application data migration initiative into a data governance pilot” A data migration effort can accomplish so much more than simply transferring data. Think about using it as an opportunity to improve the quality of existing data and apply new, higher standards to the information powering your organization.
Don’t miss out on topics that are key to your success. Please join the Potential at Work for Information Leaders community today. Available in nine languages, this site will continue to feature fresh, new ideas to promote the value of information management from a variety of top technology leaders.Sign up now!
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…)
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…)
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…)
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…)
As one of the founders of Informatica’s Smart Partitioning capability, I am constantly asked, “Why can’t we just use (insert DB vendor here) tools to accomplish the same thing?” What a great, simple, straightforward question…and what a nuanced answer! Instead of talking about how great our technology is or walk through all the features and functionality, I thought it would be best to answer the actual question, “Why can’t we do this on our own?” In this two part series, we will explore the manual process of implementing Oracle database partitioning and compression in complex OLTP applications. (more…)
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…)