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daily 07/08/2016

      • Use a compare table to perform calculations in Wave Analytics.
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      • Use the Contains filter on dimensions.
    • Here’s your goal for this unit: You want to have the percentage of total sales versus  tablet sales over the past 90 days.
      • Manage date grouping, filtering, and visualization.
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      • Copy and paste results between lenses.
    • bonus contest for tablets as a sales incentive.
    • You want to see how your sales have evolved  through time for the past 90 days.
    • You want to see how tablet sales have evolved over time, so you’ll open and explore  the Product Opportunities dataset, which contains opportunity and product  information.
    • If the calendar and fiscal year differ, you can enable Wave Analytics to generate  the fiscal date fields in the dataset in addition to calendar date fields. You can  customize the data pipeline to match your company’s fiscal calendar. Learn more from  Date Handling in Datasets in the  Salesforce Help.
    • Add a filter for the closed date to show opportunities that closed 90 days ago to 90 days ahead.
    • The bar chart is a great tool for comparison, but it’s difficult to show evolution  through time. The timeline is helpful when you’re grouping by date.
    • Oh no! It looks like your lost opportunities have increased over the past month. Let’s take   a closer look to make sure what we’re seeing is accurate before jumping to conclusions.
    • Indeed, Opportunity Closed Date can be in the  future, in which case it’s the expected close date. This means you’re seeing all  opportunities that have a close date, rather than those that are already closed. We need to  refine the query a bit more.
    • Opportunity.IsClosed—is true when closed or false when open.
    • Opportunity.StageName—is Closed Lost or Closed Won when the opportunity  is closed.
      • Copy and Paste Results between Lenses

            

        To start, you’ll get the two top-selling tablets from your first exploration and copy  and paste them into your timeline as a filter. Let’s walk through it.

          

         

           

    • We’re filtering by the current top two products. But if the product rankings  change, the filter won’t be updated. You can do the filtering dynamically in a dashboard through  what we call faceting. For more details about faceting, see the help topic Facet Widgets to See Data from Multiple  Angles.
    • Group by date, which is usually done by at least two attributes: Year-Quarter,  Year-Month, Year-Week, or Year-Month-Da
    • Filter by date, whether absolute or relative to the time when you’re asking the  question
    • isplay date grouping properly through a timeline visualization and add a second  grouping to it.
      • Filter a dimension using the contains filter.
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        • Select results from an exploration to copy and paste it into another  exploration.
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    • Describe the four basic actions related to exploring data: grouping,  aggregation, filtering, and visualization.
        • Describe what measures and dimensions are, and how each can be used in  explorations.
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    • You need to understand how your sales are spread among your products in order to  choose the focus for your contest. Wave Analytics data  exploration allows you to go from one answer to another naturally, and even answer new  questions as they arise.
      • Opportunities, focused on your sales pipeline
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      • Products Opportunities, which ties your opportunities with your product data
    • Throughout the exploration, you’ll ask questions about the data. Asking a  question is what we call “running a query.” You’ll be changing the query step by step to  get closer to your exploration goal, and you’ll see this simple bar transform into a  sophisticated chart.
    • Grouping—Group the data by a certain dimension (more on dimensions in a  bit). For example, group by product name or account.
    • Aggregating—Summarize the data based on the grouping. For example, sum of  the amounts or count of rows of data, as in the initial exploration state.
    • Filtering—Filter the data to narrow your results. For example, show only  opportunities within the fiscal year.
    • Regardless of the query you use, there must be an aggregation to have a visualization.
    • A dimension is a qualitative value, like region,  product name, or model number. So it’s something you can group by.
    • But  you want to see the revenue brought in by each product family, so you need to change the  aggregation. Aggregations are typically made on measures.
    • What’s a measure? A measure is a quantitative value, such as revenue or exchange  rate. In other words, it’s a number you can do math on, such as calculating the total  revenue and minimum exchange rate.
    • The count operation is focused on dimension aggregation. If you select Product Name,  for example, it counts the number of products for each product family.
    • Can tablets catch laptops? That’s it! You need to run your  sales contest on tablets to motivate your sales reps to sell this product family.
    • You can change the dimension and measure labels using a configuration file called  extended metadata (XMD).
    • In this case, you’re looking at all the closed opportunities. Closed  opportunities can be won or lost opportunities.
    • Colors are defined from a default palette for now. You can manually choose the colors  through the extended metadata (XMD) configuration file. See Colors Section in the Salesforce online  help.
    • As a reminder, an exploration has two main parts: the query and the visualization
    • You want to identify your best sales rep when it comes to tablets, so you want to  see one bar per owner name, but keep the product name grouping. So what if you could  “stack” the values for each sales rep?
    • A lens keeps the query  (aggregation, grouping, and filtering) and the chart type, but not the results. It’s  like saving the question and not the answer, because the answer is too big, and you can  have it instantly by asking the question again.
    • A dimension is a descriptive value, whereas a measure is a value you can do math  on.
    • When you’re exploring data, you’re just mixing and matching four basic actions:  aggregating, grouping, filtering, and creating a meaningful visualization.
    • Describe the different Wave  assets like app, dashboard, lens, and dataset.
    • A fancy word for folder.
    • The Shared App is accessible by anyone who has access to Wave.  Although it’s accessible by everyone, you can still ensure stricter security on  datasets  through row-level security, which you can learn about in the Wave Analytics Security Implementation Guide.
    • If a user has access to an app, the user can see all the  datasets, lenses, and dashboards within  that app
    • To ensure  that users have access to all required assets, keep all related datasets, lenses, and dashboards in the  same app
    • Because you set  sharing at the app level,  this best practice ensures that users have access to the datasets that  support lenses and dashboards.
    • For this trail, you will create an app—called My Exploration—to store  the datasets and  lenses. You can share the app with other users in your Developer Edition org.

            

    • Lenses

        

      A saved exploration. You’ll go more in depth about lenses in the next module when  you do your first explorations.
    • When in an app, you’re viewing assets for just that app. To see all available dashboards,  lenses and so on, just click on the Wave Analytics tab:
    • Analytics Library to access the landing page that contains all  Wave documentation—a gold mine that’s worth checking often.
    • The Data Manager tool does a few things. For now, you only need it to wipe old data and   create a fresh set with dates relative to today. Run this step anytime you’ve been away from   this module long enough for the data to get a little stale.
    • When you run this step, you delete and restore the data with fresh dates.
    • Wave provides multiple ways to import data, both from Salesforce and external   sources. For example, you can use a Wave tool—like the dataflow or the dataset   builder—to import Salesforce data.
    • To import  external data, you can use the CSV Upload interface to import manually or the External Data API to  import programmatically.
    • and then select Data  Monitor.
    • In the top left corner, click the selector to change Jobs  View to Dataflow View.
    • After you start the dataflow, it continues to run  on a daily schedule to update your datasets with any new  Salesforce data.
        • Products  Opportunities—Contains closed opportunities along with product information  about each opportunity. Closed opportunities include won and lost opportunities.

            

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    • A couple of things. First, by default, the dataflow creates datasets in the Shared  App, which is accessible to all Wave users in your  org. To hide a dataset, just  put it in My Private App.
    • Permission Set License Assignments

Posted from Diigo. The rest of my favorite links are here.

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