Your Next Steps After Registering a Dataset

After registering a dataset in CRM Analytics, there are steps you should take to make working with this new dataset easier for you (and your team and users).

This post will explain to you the next steps you should take after pulling in data to make a dataset.

Registering Dataset Best Practice

Looking At Your Data

After registering a dataset, the first step is to make sure it is correct and clean. Check the data to make sure that all of the needed fields are filled in and that there are no missing or duplicate records. For useful insights and accurate assessments, you need data that is accurate and clean.

You need to understand the structure, trends, and relationships in your dataset. Since the dataset is new (and unprotected), you can create a quick lens or dashboard to get a quick peek. You can move on to the next step if the data seems accurate and the customer confirms it.

Clean the Dataset

After you have determined the data looks good, your next step is to clean the dataset. Sometimes, especially if it’s your first time registering the dataset, you may find items that you should clean up. For example, the dataset may have awkward-looking field names like Opportunity.Account.OwnerId.Name which should just be labeled Account Owner. To simplify your field names in the dataset, you can follow the steps outlined in the Help documents.

Another item that you should clean up is either duplicate fields or fields that have similar data values that could substitute for one another. You need to give your team or end users a simpler choice and get rid of confusion from the beginning. If you have a field called Opportunity Closed Date and another field called Date Opportunity Closed then you can see how it can be confusing. You have a couple of choices. Either name the fields so that it is easier for a user to choose without questioning, or you can hide one of the fields.

Something else that you can do in the beginning is format your data. Even though you could format a measure within a lens or dashboard, why wouldn’t you just do it at the dataset level. This helps reduce the extra steps that you would need to take every time you add this measure to your dashboard.

Setting the Table Default Fields

When you create a dashboard, there will be a time when you will need to add a values table to the dashboard. Most of the time, it will seem that users will just go to your dashboard to download the data from a table that you present. When you first create a lens with a values table, CRM Analytics will present a table with a random order of fields from the first 100 rows.

The best thing you can do for your sanity is to define default fields for your dataset. This way, whenever you or someone else creates a value table using the dataset, the table will initially display these default fields.

Keeping the Data Secure

After registering the dataset, verifying the data, cleaning the data, and setting the default fields, the final thing you should do is to secure the data. If the data is meant to be open to all users, then you will not have to do anything.

But if you need to secure the data, you can do it in two places: at the app (folder) level or at the dataset level. To secure it at the app level, you will just need to assign the users or groups that can have access to the folder by sharing it. The other way to protect the dataset is to add a security predicate. (There will be a post to help you understand how to set that up.)

Datasets from CRM analytics can lead to useful business insights. You can get the most out of your CRM dataset by validating, cleaning, and securing it. Prioritize data security and privacy to maintain trust and protect your data assets above all.

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