Salesforce Analytics – Catch that WAVE

I was recently asked to lead the evaluation of Salesforce Wave for our Global Salesforce Org. With slightly over 1,100 users around the world it was an exciting opportunity and I was happy to be given the project.  I have worked on several different Business Intelligence tools in the past, so I felt very comfortable taking on this new challenge.  It involved working with our divisional team leaders to understand the business needs and identify what was working with the out of the box Salesforce reporting and where their was a void of intelligence around core business decisions metrics.

During one of our NYC Developer Meetup sessions, we had a Wave analytics consultant show us all the cool and advance things he did with the application.  It was very impressive.  What I personally took out of that meetup session was two things.  The first was that there was a Sandbox that could be created so that I could get some hands-on experience with Wave.  The second was to use Trailhead for training to understand the application better.

I would like to share with you some prep work I used to ramp me up quickly on the tool.

Free Developer Wave Environment :

Trailhead :

There are 4 components to Wave which you should understand and learn to effectively manage the tool.

  1. Apps
  2. Datasets
  3. Lenses
  4. Dashboards


An app contains dashboards, lenses, and datasets in any combination that makes sense for sharing your data analyses with colleagues. Apps are like folders. They allow users to organize their data projects—private and shared—and to control sharing.


“A dataset is a collection of related data that is stored in a denormalized, yet highly compressed form. For each platform license, your
organization can have a maximum of 250 million rows of data stored for all registered datasets combined.
Wave Analytics applies one of the following types to each dataset field:

Date – A date can be represented as a day, month, year, and, optionally, time. You can group, filter, and perform math on dates.

A dimension is a qualitative value, like region, product name, and model number. Dimensions are handy for grouping and filtering
your data. Unlike measures, you can’t perform math on dimensions. To increase query performance, Wave Analytics indexes all
dimension fields in datasets.

A measure is a quantitative value, like revenue and exchange rate. You can do math on measures, such as calculating the total revenue
and minimum exchange rate. For each dataset that you create, you can apply row-level security to restrict access to records in the dataset”

Using the pre-built Salesforce Developer Org with Analytics already on top of it, I quickly went to work and added some of my orgs custom fields and dataloaded the sample records to match my company architecture better.  I segmented the Account and Opportunities by Market, Region and other metrics that matched closely my organization.  Before you demo how something works, its important to add components that the business is use to seeing.  Just demo’ing the Salesforce sample org would have just confused my sponsor and champions of the project.



One small caveat I should mention, if you have an older Developer Org you will need a new one with the Wave application on it.  So now I have two Dev orgs, one with all my original development test code and a second one just for Wave Analytics.

If asked by our company to lead an effort or you just want to understand if Wave Analytics can be used at your company to enhance the user experience feel free to ump on the surfboard, learn and push the limits of the application.

Free Developer Wave Environment :