Saturday, 21 February 2015

Getting Started with Salesforce Wave

Getting Started with Salesforce Wave

Introduction

Salesforce Wave, aka the Analytics Cloud, was the major new product launched at Dreamforce 14. Over the last month or so I’ve had a chance to try it out, thanks to the MVP program and the fact that I work for BrightGen, a Salesforce Platinum Partner here in the UK.

Setting Up

Setting up Wave is straightforward, mostly setting up and assigning permission sets, and covered in detail in the Salesforce Help.

Loading Data

Obviously its rather difficult to use an analytics product without data, and Wave provides a number of mechanisms to import your data.  For the purposes of this post I’m ignoring the ETL providers (Informatica, Jitterbit, Mulesoft etc), instead I’m focusing on those provided by the platform or tools created by Salesforce.

The product is still pretty new, so there isn’t a particularly fancy UI for loading data yet, although I’m sure this will be coming shortly.  Personally, this is the way I prefer things - I’d much rather get access to the functionality as soon as I can, rather than waiting for a funky user interface. 

Importing a CSV File 

The simplest way to load data is to import a CSV file. A good source of sample data is the Government Statistical data sets page. In this post I’m using the Land Registry Price Paid current month data, which provides 87,000 rows of data (Data produced by Land Registry © Crown copyright 2014). This only contains the raw data, so I need to add a header row at the top of the file based on the column headers. The first few column headers are shown below:

Screen Shot 2015 02 21 at 14 20 37

 

 Navigate to the Analytics app home page and click the Create button and choose Dataset from the drop down (there might be other options depending on your permissions):

Screen Shot 2015 02 21 at 11 44 04

from the resulting page, choose the CSV option:

Screen Shot 2015 02 21 at 11 49 48

and select the file to upload :

Screen Shot 2015 02 21 at 11 59 48

Note that the upload section also mentions a JSON Metadata File - this is optional, but without it each column in the file will be assumed to be a dimension, which means that I’ll only be able to show the record count matching particular criteria, whereas I’m more interested in prices - for example, the total amount paid per county or the average for postcodes near me.  Providing a schema file allows me to specify that the price column contains a measure by setting its type to ’Numeric’:

Screen Shot 2015 02 21 at 14 26 27

 

The full schema file can be accessed here. (I didn’t create this by hand, in case you were wondering, it was auto generated by a nifty tool that I’ll cover in a later blog post once the Analytics Cloud External API is out of pilot).

Finally, I specify the name for the dataset and the application (essentially a folder) to store it in - I’ve called this ‘December_Price_Paid’ and stored it in ‘My Private App’ - which is totally private to me, much like the private workspace in CRM Content.. Clicking Create Dataset uploads the file and queues it for processing - the message says that this can take up to an hour, but much depends on what other activity is taking place. I uploaded this on a Saturday morning and it took about 3 minutes to be processed.

Exploring a Dataset

Once the data set is loaded, it appears in the list of datasets for the specified application:

Screen Shot 2015 02 21 at 14 30 23

Clicking on the dataset takes me to the basic view, which displays the total number of records:

Screen Shot 2015 02 21 at 14 32 28

Nothing particularly interesting so far, but simply clicking on the ‘Measure’ and changing this to the sum of price paid:

Screen Shot 2015 02 21 at 14 33 43

gives me the total spend for the month - nearly 23 Billion (did I mention the UK housing market is crazy?):

Screen Shot 2015 02 21 at 14 35 10

now I can start to see the real power of Wave - simply creating a grouping by County allows me to view the total spend per county in a couple of clicks and see the total spend for Essex, where I live:

Screen Shot 2015 02 21 at 14 37 21

I can then drill into Essex by district and change the measure to show the average price paid per property to find out how much people are prepared to pay to live here:

Screen Shot 2015 02 21 at 14 41 07

Now I could probably load this data into Salesforce and build out reports and dashboards to show the same data, but it would take me a lot longer than the few minutes I spent setting up this dataset and exploring it, plus if I wanted to look at the data slightly differently I’d need to build a new set of reports/dashboards, whereas with Wave I just return to the base dataset view and change my measures, grouping and filters.  

What the screen shots above don’t do justice to is the speed of Wave, so here’s a real time video showing the steps I’ve described above - its pretty impressive, I’m sure you’ll agree, especially as this is taking place over an internet connection of 4.7 Mbps:

 Note that all of the querying takes place on the server, so this involves genuine round trips - a testament to the power of the Wave query engine and the data compression that is achieved.

5 comments:

  1. Can you let me know which nifty tool you have used?

    ReplyDelete
    Replies
    1. Unfortunately the API that the tool requires is still in pilot, so I can't blog any more details until it goes beta/GA.

      Delete
  2. +1 for sharing the nifty tool!

    ReplyDelete
    Replies
    1. Unfortunately the API that the tool requires is still in pilot, so I can't blog any more details until it goes beta/GA.

      Delete
  3. Hi Bob,

    I need to add a selector/drop down in my wave analytics dashboard. But I could not found any component for this. Can you help please.

    Thanks
    Raghvendra

    ReplyDelete