What could be measured, need not be measured. With social media and the web being the prima touch point for today’s customers, the deluge of data generated per customer has been growing exponentially. Tracking customer activity to generate worthy insights has become an art as much as the science involved.

 

Why couldn’t anybody take advantage of the smartness the data offers? Is it limited to those who have the monetary muscle power or the technical knowhow? What about challenges posed such as data volume, data quality, data types etc.?  With the emergence of cloud based managed services, ANYONE can leverage analytics for their business. Major cloud and analytics vendors like Amazon, Microsoft, SalesForce and SaS provide analytics as a managed service. Cloud based analytics services makes adoption a lot easier. They provide an integrated platform for data processing, modeling, computing, visualization and data sharing.

 

This two-part blog series covers how Cloud can be leveraged for customer analytics, specifically Microsoft Azure. The first blog (this) would show you the Azure services available for analytics, and the second one will focus on how these services can be used for customer analytics.

 

Azure offers a range of services to work with large data, building predictive models and visualizing data. This article introduces some of these services and how they integrate together.

 

Azure cloud services

Azure Data Factory (ADF): Fully managed service for composing data storages, processing, and movement services into streamlined, scalable, and reliable data production pipelines. ADF acts as an ETL tool for analytics on cloud. It lets you easily work with perse data storage and processing systems. You can transform data using HDInsight  compute power.

 

Azure Machine Learning (AML): Azure ML provides an easy-to-use and powerful set of cloud-based data transformation and machine learning tools. AML reduces complexity of machine learning and brings ML to broader audience. AML includes many algorithms which Microsoft uses for products like Xbox and Bing. It also exposes APIs using which, applications can consume models easily.

 

Azure Data Lake: Supports an enterprise wide repository of every type of data collected in a single place. Data of all types can be arbitrarily stored in the data lake prior to any formal definition of requirements or schema for the purposes of operational and exploratory analytics.

 

Power BI: Power BI is a collection of online services and features that enables you to find and visualize data, share discoveries, and collaborate in intuitive new ways. With Power BI, you ask questions in natural language and get the right charts and graphs as your answer.

 

All these services help organizations overcome some of the challenges like handling big data, data transformation at scale, building and deploying models etc. With pay-per-use models, these services pretty much democratize the adoption of Analytics – irrespective of their size or budget.

 

This was just an introduction. Watch this space for more, where we would literally pe into the data and create real results about your customers using Microsoft Azure services.