Understanding Your Data

Possessing intelligent data models is only beneficial to those who understand the model. Yet often there may be times, where for efficient management, non-technical users may require accessed to metadata. This therefore introduces a critical problem – how do we ensure that a universal understanding is available to all business users?

Metadata sharing is crucial for optimising analytics, providing business users with the means to independently explore data structures at their own pace. It may seem a fruitless exercise to provide mass availability into your data structures, but at some level, everyone in your organisation whom either uses or produces data will require an underlying connection, despite how fine said connection may seem. Of course, depending on the size of your organisation manually administrating the sharing of data models may be deemed an impossible task, requiring a herculean effort to lift the project off the ground. As such, much like our data, the initial question has evolved. No longer are we asking why, but how should we simplify the process for sharing data models?

The first task is to define a comprehensive plan outlining the level of available detail required, importance of metadata security, accessibility, and desired method of interaction. After all, metadata is only as good as the processes used to harvest the data and is only useful if users can easily decipher the information.

For a largely non-technical target audience, careful consideration of the following aspects is needed:

  • Simplicity – How can we detailed information in a manner that caters for users with a varied range of learning techniques.
  • Maintenance – Extended periods of downtime is never good, but when a large proportion of an organisation is dependent on a solution minimising downtime though ease of maintenance is critical to the project becoming a long-term success.
  • Automation – Metadata is only useful if it properly represents the source database, requiring updates whenever the source is altered. This situation is where automation comes into its element, ensuring dependant systems, such as our data sharing solution, always align correctly to the source.
  • Access and security – The key objective of our project is to provide mass availability of our data models, which means mass deployment of software and presents a key issue: how do we keep our shared data models safe?
  • Management – Even once the initial deployment is completed, continual management is crucial. Are individual users managed separately, or could self-service access be used?

At first glance, one would assume a self-service system promoting individual access will only serve to enforce boundaries between business users. However, with the right tools, such as erwin Web Portal we see the opposite is true due to the implementation of persistent hyperlinks and built-in sharing features, maintaining the core concept of sharing data. Use of a browser-based portal opens the door to an array of potential deployment options without costly application deployments, as support for group-based user assignments aid in the easement long-term management.

As we look back to our initial considerations that we laid out earlier, we mentioned that one of our key considerations is simplicity and ease of use. To many this would seem to be an oversight, often resulting in limited details with no meaning full metadata. Presenting models in a vertical stack with supporting capabilities to dig down based on object ownership, such as with erwin Web Portal, provides a simplistic top-down view to improve initial impressions. As further details are required users simply navigate to the required level of information. Though we still have a blanket search covering the whole repository, beneficial when you are aware of the data you need but it’s location remains hidden, even when in flux.

Making use of a self-service metadata and data structure exploration tool is an efficient method to enhance your data modelling capabilities by provision of a common understanding to an entire organisation. The technological threshold has been lowered, paving the way for non-technical business users and stakeholders alike to explore their assets and bolster data fluency.

To learn more about how your data environment can be augmented watch our recent webinar: Enhancing Your Data Modelling Environment.

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