3 Common Analytics Use Cases for Azure Databricks

Pragmatic Works is considered to be experts in the Microsoft Data Platform, both on-premises and in Azure. That being said, we often get asked many questions like, how can a certain technology benefit my company? One technology we are asked about a lot is Azure Databricks. This was released over a year ago in preview in the Azure portal and we’re starting to see some massive adoption by many companies, but not everyone is ready to delve into data science and deep analytics, so they haven’t had much exposure to what Databricks is and what it can do for their business.

There are some barriers preventing organizations from adopting datascience and machine learning which can be applied to solve many commonbusiness challenges. Collaboration between data scientists, dataengineers, business analysts who are working with data (structured andunstructured) from a multitude of sources is an example of one of thosebarriers.

In addition, there’s a complexity involved when you try to do things with these massive volumes of data. Then add in some cultural aspects, having multiple teams and using consultants, and with all these factors, how do you get that one common theme and common platform where everybody can work and be on the same page? Azure Databricks is one answer.

Here’s an overview of 3 common use cases that we’re beginning to see and how they can benefit your organization:

1. Recommendation Engines – RecommendationEngines are becoming an integral part of applications and softwareproducts as mobile apps and other advances in technology continue tochange the way users choose and utilize information. Most likely whenyou’re shopping on any major retail site, they are going to makerecommendations to related products based on the products you’veselected or that you’re looking at.

2. Churn Analysis – Commonly known ascustomer attrition; basically, it’s when we lose customers. UsingDatabricks, there are ways to find out what some of the warning signsare behind that. Think about it, if you get ways to correlate the datathat leads to a customer leaving your company, then you know that youhave a better chance to possibly save that customer.

And we all know that keeping a customer and giving them the service they need or the product they want is significantly less costly than having to acquire new customers.

3. Intrusion Detection – This is needed tomonitor networks or systems and activities for malicious activity orpolicy violations and produce electronic reports to some kind ofdashboard or management station or wherever that is captured.

With the combination of streaming and batch technologies tightlyintegrated with Databricks and the Azure Data Platform, we are gettingaccess to more real-time and static data correlations that are helpingto make faster decisions and try to avoid some of these intrusions.

Once we get triggered that there is a problem, we can shut if off very quickly or use automation options to do that as well.

Today I wanted to highlight some of the ways that you can utilizeDatabricks to help your organization. If you have questions or wouldlike to break down some of these barriers to adopting machine learningand data science for your business, we can help.

We are using all the Azure technologies and talking about them withour customer all the time, as well as deploying real world workloadscenarios.