Is data complexity overwhelming you?


chaosData complexity is often cited as a significant barrier to the delivery of programs such as customer experience management, master data management and advanced analytics.

Some of our clients have, for example, identified more than 1000 individual systems that hold client data.

While this level of complexity is extreme, the reality is that most large organisations have numerous systems, containing similar or related information.

in many cases, this data complexity is poorly (if at all) understood.

In one environment, we asked whether an apparently unused field in a mainframe data source could be reused for a newly identified business purpose. The answer – it would take business analysts one year to do a full assessment and impact analysis in order to understand whether this field was in fact available, or was in fact being used for something business critical.

Sourcing the correct, or most trusted, version of data – whether to support analytics, or even to populate the most recent email address for a marketing campaign can similarly consume a lot of time.

Enter the governed data catalog

The concept of a data catalog – a single source for users to find, access and share data – is simple. Yet, as discussed in a new CITO Research whitepaper Create Meaning, Stomp Out Chaos with the Collibra Catalog – delivery is difficult.

In order to succeed, the data caatlog must simplify the process of finding, understanding, defining and, finally, cataloging the data you have. The only way to get this done is to use a team approach that allows those closest to each business function to do the data governance research, create their data catalog, and then continue to improve on the results until, over time, a trusted enterprise picture is defined.

What is the value of the completed data catalog

Even when partially completed the data catalog creates order and repeatability, saving time and reducing teh risk of data related projects. At its core, the data catalog:

  • Helps users to easily find data
  • Provides context for data enabling impact assessment and reducing confusion
  • Enforces and improve data rules and standards
  • Paints an accurate picture of the data landscape and helps to identify areas for improvement

If you are struggling with data complexity you can learn more by:

 

 

 

 

 

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