How to Organize Operational Data for Maximal Insights Fin Analytics

How to Organize Operational Data for Maximal Insights


In this post, we will offer a framework for how to think about categorization and tagging of workflows for maximal insights.


Many operations teams struggle with how to effectively categorize and tag workflows in order to best understand and analyze the data. Depending on the team and the type of work performed, some organizations have just 1-2 tags per task, whereas others have hundreds. What are the best practices for tagging and categorization of workflows? How can we eliminate the ‘noise’ and better understand workflow data?

In this post, we will offer a framework for how to think about categorization and tagging of workflows for maximal insights.

1) Assign each task a MECE category

Mutually exclusive, collectively exhaustive, or MECE, means that each task, conversation, or piece of work your team performs has one and only one category associated to it.

For example, if Refunds are a workflow your team handles, a refund conversation would be tagged as “Refund.” Even though, often a given conversation may have four or five other components involved, you would still only assign it one MECE category.

This kind of forced bucketing becomes very valuable later when you want to analyze the data or look for opportunities across categories and are able to more accurately size and compare them. Without MECE tagging, you’re left with noisy data that is so intersected that you aren’t able to distinguish or add them together to get the overall set of opportunities to then prioritize.

Can this type of forced bucketing create fuzziness in the data? Yes. But the ability to segment and add up your data to identify opportunities is incredibly valuable and eliminates the noise you otherwise get from complex tagging schemes.

2) Only tag what you’ll want to analyze later

This may seem obvious, but very often, operational leaders, in an effort to understand the work being done, stray into a world of too many tags, and subsequently, too much noise in the data. Most organizations don’t have more than a dozen tags or concepts on a given conversation, but some have upwards of one hundred tags for a single workflow. How many tags is too many, or too few? If you find that your organization overall truly has hundreds of tags that are ‘relevant’ you might want to focus on more organizational alignment.

Each organization has different needs and priorities, so there isn’t a single answer to this question, but we can offer a filter to help simplify and clarify the tags you should keep versus pitch. Simply, when setting up workflows and associated tags, ask if these are things that you’re going to want to analyze later. If you end up in a world where you’re spending a ton of effort and time categorizing and setting up data you’re not going to use later, you’re better off not doing it.

Conclusion

Every organization is different and will require a different approach to data categorization, but our recommendation would be to first, employ MECE categorization to your workflows, even though the forced categorization will create some fuzziness in the data, and second, when adding tags and organizing work, aim for enough data depth to allow yourself to analyze what you want to analyze without drifting into a world of spending time on things you aren’t going to use. If you wind up feeling like you need to add more tags later, ask yourself whether that task is too big and can be subdivided into smaller categories that can be more accurately measured.


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