In our previous blog, we walked through the basic components of a SuiteAnalytics dataset. Knowing how to build a dataset is crucial to SuiteAnalytics. But now that you know the basics, let’s build a sample SuiteAnalytics dataset so we can see what this all looks like in practice. 

Know What You Need to Know

Before you actually begin to build your dataset, first you need to know the purpose of your dataset. In other words, why are you building the dataset? What information needs to be in it? If you know what it is that you need to know, then the small decisions you make as you build your dataset will be more obvious to you.

For our sample dataset, let’s say that we’re interested in seeing certain data about our sales. We want to analyze which sales reps are responsible for which quotes and sales orders, who those customers (and potential customers) are, and which campaigns are bringing in which quotes and sales orders. Notice that we haven’t, at this stage, explicitly determined which fields and criteria we plan to use in the dataset. We just understand the broad goal of what we want our dataset to accomplish. Taking the big picture we’ve developed, we can now get into the weeds of building this sample SuiteAnalytics dataset. 

Building a Sample SuiteAnalytics Dataset

Navigate to the Analytics tab in your center and choose the Datasets tab on that page. Then, select the New Dataset button. On this page, you need to choose the root record for your dataset. Because our dataset revolves primarily around quotes and sales orders, which are both transactions, we will choose the Transaction record type as our root record. 

Once you’ve selected the root record, you are taken directly to the Dataset Builder. Notice that several fields have already been added to your dataset by default. These fields may or may not make the final cut in your dataset, but they do give you somewhere to start. 

Setting Filters

In the above picture, notice that there are 48,938 results. We would definitely want to set some criteria on this dataset in order to restrict the amount of results! And because we know the purpose of this dataset, we know exactly what criteria we want to set. We need to set criteria on the types of transactions we get results on. Since we’re only interested in seeing quotes and sales orders, we can go ahead and restrict our results to those types of transactions. To do that, simply drag the Type field from the second column to the left and drop it in the criteria box. Then, in the popup box, move Sales Order and Quote to the right column and click the Apply button. 

After setting this criteria, our Data Grid is now showing only 8,307 results. If we wanted to, we could set even more criteria on this dataset. But for now let’s just move on to adding new fields to the Data Grid. Remember, you can always filter and sort this information in your workbook as well. 

Adding Fields

Our Data Grid already contains key information from the Transaction record. Let’s add three other Transaction fields that would be relevant to our dataset. The fields we’re going to add are Due Date, Estimated Gross Profit, and Sales Rep. To add fields to the dataset, you can either double-click on the field names or grab the fields and drag them over to the Data Grid. 

Joining Record Types 

The beauty of a dataset is that we can join multiple record types to a single dataset. Doing that gives us access to fields outside of our selected root record.  For our sample dataset, another record type we need to pull fields from is Sales Rep. We’ve already pulled the Sales Rep field to the Data Grid from the Transaction record, but if we want to see more information about our sales reps, we need to get it from the Sales Rep record. In the far left column on the dataset builder page, select Sales Rep from the list. We want to see the commission that our sales reps make, so we’ll double-click the field Commission %. Perhaps we would also like to compare the profitability of sales reps in various locations, so we could add the Location field to the Data Grid as well. 

Before saving this workbook, let’s go ahead and join a couple more record types to our dataset. First,  let’s go to the Campaigns record type and add the Campaign field to the Data Grid so we can see how various campaigns affect quotes and sales. Notice, however, that the Campaigns record is a child record of the Sales Rep record. The final record type we’re going to add is the Campaign record (not to be confused with the Campaigns record!). The Campaign record is a child record of the Campaigns record. The fields we need from this record type are Total Cost, Revenue, and Return on Investment. 

Arranging the Dataset

As you add fields to the Data Grid, you can arrange the columns simply by dragging the column headers to the locations you want them to be in. The order of the fields on your dataset don’t have a relevant effect on the appearance of your final workbook, however, so this step is more to keep you organized while building the dataset. 

Using the Dataset

In the upper righthand corner of the dataset builder, you’ll see four key options for this dataset. For one thing, you could go straight to creating a new workbook from this dataset. You can also export the information in the dataset to a CSV file. If other users in the company should have access to the dataset, you can share it with them. When you select the Share button, you can share it based on roles and/or individual employees. For example, we would probably want to share the dataset we just created with all the marketing and sales managers in our company. The final option you have is to save the dataset. When you save the dataset, be sure to give it a memorable name. 

Looking Ahead to the Workbook

It can be easy, when you’re creating a dataset, to start thinking about the dataset the way you would think about a workbook. Seeing all the information gathered in the dataset may make you want to start sorting and filtering through the data right there in the dataset builder. But don’t fall into this trap! Remember that the dataset is simply a tool that collects data so it can be used in a workbook. When you create a workbook based on your dataset, you will be able to sort, arrange, and filter the data to your heart’s content. So, yes, you do need to set criteria in your dataset that limits the results you get—but avoid narrowing the data down too much in the dataset. Make sure you keep any information that you would want to have access to in the workbook. 

That being said, nothing is absolutely set in stone with either your datasets or your workbooks. If you get to the workbook stage and realize that your dataset is missing an essential field, adding that field to your workbook is as simple as editing the source dataset. 

Conclusion

Now that you’ve created and saved your dataset, you are ready to add it to a workbook. If you followed along by building the same dataset we just did, or if you branched out and created a completely different dataset, let us know how things went for you in the comments!

In our next post, we’ll take a closer look at the workbook stage. Datasets and workbooks are just one aspect of SuiteAnalytics, however. In our SuiteAnalytics series, we’ll be covering everything from workbooks and reports to saved searches and dashboards. So if you want to keep up with our entire SuiteAnalytics series, be sure to join the SuiteRep newsletter below! 

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