More Ways to Maintain Data Quality that Nobody Talks About

By Mark Ross | Thu, Nov 05, 2015

And now for something completely different.

Ok, well, it’s the same completely different as last time, because we have more suggestions for how to help you maintain your data quality!

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The OTHER Ways to Maintain Data Quality That Nobody Talks About

By Mark Ross | Tue, Nov 03, 2015

And now for something completely different.

If you’ve got a data quality problem, chances are you’ve read an article or three about how to improve that quality. However, through the course of that research, you’ve probably found that many of the articles are very much alike.

Now that’s not necessarily a bad thing; articles can only be so long, and it’s important to capture the most pertinent points first. Data deduplication, validation rules, reports; these are all potent weapons in the ongoing battle for data quality. Unfortunately, this means that other important things often don’t get mentioned, but let us not leave these soldiers behind! They can be just as important, and in some cases, more so.

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3 Essentials for Great Data Management Processes

By Marc Aubin | Mon, Oct 06, 2014

Let’s take a deep dive into what makes a good manual data management process. A good manual data management process gives your company’s Salesforce users visibility, context, and actionability into your data. Relational databases can be confusing. This is especially true for folks that think that relational data management is the same as dealing with single table views. The following considerations are how to nail it in a relational context.

 

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How Data Sources Affect Salesforce Data Quality

By Marc Aubin | Sun, Sep 07, 2014

We at AppBuddy care about data quality, and so do you. As you work with Salesforce, one of the things you’ll notice is that your Salesforce data is affected by external data sources, automations that act on data, and data management processes. These sources provide a whole lot of data into your system, which for the most part is a good thing. The downside of inserting data from external sources and data automations is that some of the data is bad. Additionally, data management processes that are poorly designed means a higher likelihood that users don’t update data regularly and it is incomplete or becomes out of date.

As we pointed out in “Conquering Salesforce Data Quality Problems over the Data Lifecycle,” this data can include: wrong close dates, wrong Opportunity stages, wrong project statuses, and data that is missing from half the fields on your custom objects. Taking all these factors together, data can truly become a mess.

Let’s talk about how external data sources, automations, and manual data management processes can affect data quality in your Salesforce org so that you can identify data quality problems yourself.

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Conquering Salesforce Data Quality Problems over the Data Lifecycle

By Marc Aubin | Thu, Aug 21, 2014

We at AppBuddy don’t like bad data one bit, and neither do you. If you’ve been working with Salesforce for any length of time, you probably have bad data. Wrong close dates. Wrong Opportunity stages. Wrong project statuses. Data missing from half the fields on your custom object. It’s a mess. We understand.

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