Data Reuse Pitfalls: Dataset Access Chaining

Data is valuable in business intelligence, and how it gets accessed and shared can impact the effectiveness of your data reusability. Companies often run into issues around dataset access chaining - the complex web of access permissions that can crop up when data is shared and results in generating new derived reports or datasets.

In this post, we will break down what this pitfall entails and some strategies to overcome it.

Understanding Dataset Access Chaining

Dataset A gets shared with Team B. Someone on Team B uses it to create a new report, which they share with Team C. Now, the remaining question is- who can access the original data? 

Team C may hit roadblocks trying to use the report because they didn't have direct access to Dataset A in the first place. This chaining effect happens with live connections and direct queries as well.

The big takeaway is that all teams must have access to the underlying datasets, such as Dataset A, for an effective utilization of derived contents. Failing to address chaining can lead to substantial costs, wasted efforts, and broken reporting.

Beyond the technical issues, access chaining can also lead to missed opportunities to collaborate and share knowledge. If Team C can't access the original Dataset A, they may struggle to build on any insights from derived reports. In return, it can limit innovation, collaboration, and additional value across teams - which can hurt competitiveness and growth.

How can companies avoid these data access chain pitfalls? 

Here are some tips:

• Treat datasets as critical assets, not just reports, to allow broader access.
• Have clear data ownership by giving one person responsibility for managing access.
• Use row-level security to carry permissions from original datasets to derived ones.
• Promote accountability around access to ensure compliance and data quality.

The bottom line is that addressing dataset access chaining helps organizations fully tap into the value of their data through collaboration and innovation. By seeing datasets as assets, clarifying data ownership, and promoting accountability, companies can avoid chaining pitfalls and pave the way for effective data use.

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