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3 Common Analytics Use Cases for Azure Databricks (2)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 adoptions 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 data science and machine learning which can be applied to solve many common business challenges. Collaboration between data scientists, data engineers, business analysts who are working with data (structured and unstructured) from a multitude of sources is an example of one of those barriers.

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 – Recommendation Engines are becoming an integral part of applications and software products as mobile apps and other advances in technology continue to change the way users choose and utilize information. Most likely when you’re shopping on any major retail site, they are going to make recommendations to related products based on the products you’ve selected or that you’re looking at.

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

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

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

With the combination of streaming and batch technologies tightly integrated with Databricks and the Azure Data Platform, we are getting access to more real-time and static data correlations that are helping to 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 utilize Databricks to help your organization. If you have questions or would like to break down some of these barriers to adopting machine learning and data science for your business, we can help.

We are using all the Azure technologies and talking about them with our customer all the time, as well as deploying real world workload scenarios. Click the link below or contact us—we’d love to help you too.

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