Data Science + Domo: KMeans

Disclaimer:  Let's get it out of the way.  I am employed by Domo; however, the views and ideas presented in this blog are my own and not necessarily the views of the company.

In this post I'm going to show you how to use Domo with it's built-in data science functions to perform a KMeans clustering using the Hotels dataset from Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 3rd Edition, by Gordon S. Linoff and Michael J. A. Berry (Wiley, 2011).  Then I'll show you how you can do the same thing using Domo's platform to host the data and R to transform and analyze the data.

Why are we doing this?
From the platform perspective, my goal is to showcase how Domo can support Business Analytics and Data Science workflows.

From the business perspective, an organization may want to cluster Hotels to facilitate creating 'hotel personas'.  These personas (ex. luxury business hotel versus weekend warrior favorite) may enable the creation of marketing campaigns or 'similar hotel' recommendation systems. 
  
​Disclaimer 2:  I do not own the Hotels dataset.

The Domo Platform Organizes Datasets

Step 1:  Upload the Hotels dataset to the Domo datacenter using the file connector. 

Duration:   < 2 minutes or 8-ish clicks.

Domo enables analytics workflows by assembling all the recordsets in one place.  The data can be sourced internally from a data warehouse, POS system, Excel spreadsheet or SSAS cube or come from external sources like  Facebook, Google Analytics, or Kaggle.

Once you've uploaded data to Domo you can apply Tags to keep track of your datasets or implement security to control who has access to your data (even down to the row-level).

Additional notes for InfoSec:
Given that the largest risk in data security is user behavior, finding solutions that are preferable to personal GitHub or Dropbox accounts, USB sticks or (God forbid) the desktop remains a priority.  One of Domo's most understated highlights is its ability to surface IT governed and cleansed datasets to analysts in a single place that's highly accessible yet fortified with bulletproof (Akamai) security.

Notes for architects and engineers:
Under the hood, Domo's platform takes the best of big data / data lake architecture (distributed, high availability, etc. ) and (good) datamart recordset management techniques.  Data workers familiar with Hadoop or Amazon web services will recognize Domo as a modular yet integrated big data stack wrapped in an easy to use GUI that business users will appreciate and get value out of from day one.

When you set up a trial of Domo you effectively have a free data lake that's ready for use -- complete with the ability to store literally millions of rows and/or gigabytes of data at rates you'd expect from Amazon S3 or Microsoft Azure storage at a fraction of the cost.  Try it.

Introducing DataFlows

To the horror of every data scientist out there, in this blog, I'll skip data preprocessing, profiling or exploration and go straight to using Magic ETL to apply KMeans clustering on a set of columns in my data.

Side Note
ETL stands for Extract, Transform and Load, and Domo provides a proprietary drag-and-drop interface (Magic ETL) for data transformation.  Technically the data has already been extracted from your source system and loaded into Domo, so all we're left with is transform phase.

Double Side Note for Clarity:  
Each DataSet is stored in Domo somewhere as a separate file.  
Each DataFlow has Input DataSets and creates Output DataSets - new file(s)

You have 3 types of dataflows native to the Domo platform.

• Magic ETL (Domo's proprietary drag and drop tool)
• MySQL and Redshift.  NOTE:  Redshift is a beta feature for transforming 5 million+ row datasets.  If you need the feature 'turned on' give me a holler.
• Additionally, Domo supports data ingestion via API, ODBC, and various other SDKs.  In other words, you can transform data using virtually any platform before pushing it to Domo. -- In this blog, I'll do some data analysis using R and then push it to Domo from R.

Magic ETL has a range of user-friendly set of Data Science functions.  They are in Beta, so if you don't see them in Magic ETL, shoot me an email so we can get it enabled for you.

What is Clustering and why are we using it?
It is not my goal to make you a data scientist in a 5-minute blog post, but if you are interested, the Data Mining Techniques book I linked earlier may be a decent place to start.

Consider the following (completely fictitious) example:

"Jae, given your stay at the Threadneedles Hotel in London, you might like the Ace Hotel in Portland which has similar review rates by international guests, number of rooms and avg. room rates."  

In short, clustering allows you to group entities (hotels) by sets of attributes (in our case, number of rooms, number of domestic reviews, international reviews, whether the hotel is independent etc.).

How easy is Magic ETL? 

It's 1, 2, 3.  Drag transformation functions (1) into the workspace (2), then define the function parameters (3).

In this example, I used the previously uploaded hotels-wiley dataset as an input, then I applied a K-Means function over a subset of columns.

Note:  I explicitly defined the number of clusters to create (5).

If you don't know what KMeans does, Domo's documentation is on par with what you'd expect from any data science-lite platform.

Step 3:  Preview and Output the Dataset for visualization or further analysis

​In the final steps, we'll add a SELECT function to choose which columns to keep.  In this case, we'll only keep columns that were used by the KMeans algorithm, as well as columns to identify the hotel (hotel_code).

Lastly, we add an Output Dataset function to create a new dataset in Domo which we can later visualize or use for further analysis.

 Beware the false prophets ...

THIS IS IMPORTANT. 
Do not fall into the trap of misinterpreting your results!

In the output dataset below, you'll see we have a new column cluster which happens to be next to a column bubble_rating.  The hasty analyst might conclude: "oh hey, there appears to be a correlation between cluster and hotel ratings." ​

​And all Data Scientists in the room #facepalm while #cheerForJobSecurity.

There are 5 clusters because in an earlier step, we told the algorithm to create 5 clusters.  We could have easily created 3 or 7.  There is not necessarily a correlation between which cluster a hotel ended up in and its rating.  Keep in mind, cluster number is just an arbitrary numbering.  If you re-run the function, ideally, you'll end up with the same groupings of hotels, but they could easily have different cluster numbers.  (the hotel_cluster_3 could become hotel_cluster_4 and hotel_cluster_4 could become hotel_cluster_1)

Side Note which will become important later:
It would be nice if Domo included metrics for measuring separation between clusters or the strength of clusters.  We arbitrarily told KMeans to create 5 clusters.  But who knows, maybe there are really only 3 clusters.  There are quantitative methods for identifying 'the right' number of clusters, but they aren't available in Domo out of the box.

Domo embraces all Analytic Tools

Let's do it again.  In R.
​
As demonstrated, Domo has user-friendly data science functionality suitable for the novice data scientist; however, in real-world applications, analysts will likely use Domo's data science functions to build up and validate proof of concepts before transitioning to a 'proper' data science development platform to deliver analytics.

Domo has both Python and R packages that facilitate the easy data manipulation in full-fledged data analytics environments.

To extract data from Domo into R:

1. set up an access token & install the DomoR package
2. find the dataset ID: https://<yourDomoInstance>.domo.com/datasources/0afe1c21-18f3-496a-acb5-599e07c9e33c/details/overview

The R Code:  

#install and load packages required to extract data from Domo
install.packages('devtools', dependencies = TRUE)
library(devtools)
install_github(repo='domoinc-r/DomoR')
library(DomoR)

#initialize connection to Domo Instance
domo_instance <- ' yourDomoInstance '
your_API_key <- ' yourAPIKey '
DomoR::init(domo_instance, your_API_key)

#extract data from Domo into R
datasetID <- 'yourDataSetID '
raw_df<- DomoR::fetch(datasetID)

## PROFIT!! ##

Earlier we asked the question, was 5 'the right' number of clusters. 

Given the variables from before (number of rooms, number of domestic reviews, etc.), once NULL values have been removed and the variables scaled and centerered, it appears that 6 clusters may have been a better choice).

Side bar: READ THE MANUAL!  Unless you read the detailed documentation, it's unclear how Domo's KMeans handles NULL values or whether any data pre-processing takes place.  This can have a significant impact on results.

​With the revelation that we should have used 6 KMeans clusters, we can either adjust our Magic ETL dataflow in Domo, or we can use R to create / replace a new dataset in Domo!

DomoR::create(  cluster_df, "R_hotel KMeans")
DomoR::replace_ds('datasetID', cluster_df)

In Domo's Magic ETL, we'll bind both cluster results to the original dataset for comparison.

1. Add the R generated dataset (R_hotel_KMeans) to Magic ETL
2. JOIN the datasets on the hotel_code
3. SELECT columns to keep (remove any duplicate or unwanted columns)
4. Revel in the glory!

Wrap it up

NEXT STEPS:  Create 'Cluster Personas.'  
Typically, organizations would use clusters of attributes to define 'hotel personas'. 

1. 'Budget Business Hotel'
2. 'Luxury Business Hotel'
3. 'Backpackers Delight'
4. 'Destination for Honeymooners'
5. 'Weekend Treat'
6. 'Long-Term Stay'

Because the attributes of 'Budget Business Hotel' and 'Backpacker's Delight' are similar, with 5 clusters, maybe they get combined into 'Budget Hotel'.  Alternatively, perhaps 'Weekend Treat' combines with 'Destination for Honeymooners' into a 'Luxury Short Stay' persona.

From there, the outcome of this clustering and persona generation may influence discount packages or marketing campaigns to appeal to specific travelers.

REMEMBER:  Clustering is not intended to predict Ratings!  If you're still stuck on that, review the purpose of clustering.  

In this quick article, I give the most cursory of overviews of how Domo's native (and beta) features can enable data science workflows.  We also explored how other analytic workflows can integrate into the Domo offering.

If you're interested in finding out more, I can connect you with a Domo Sales representative (not me), or I'd love to talk to you about your analytic workflows and use case to see if Domo's platform might be a good match for your organization!

Disclaimer: the views, thoughts, and opinions expressed in this post belong solely to the author and do not necessarily reflect the views of the author's employer, Domo Inc.