US Partner Technology Strategist
If you are a partner with a data-oriented practice—data platform, data analytics and insights, big data, machine learning, etc.—you have likely met data scientists who use R language for their statistical analysis and computing. R is a widely used open source language for statistics, machine learning, and data mining, and is the choice for many data scientists, with more than 2 million users worldwide. Data scientists are a relatively new customer audience that partners have not typically worked with.
Last year, Microsoft acquired Revolution Analytics, the leading provider of commercial software and services built on R. This functionality is now available as part of the Microsoft data platform. SQL Server R Services is an option in SQL Server 2016 (currently available as a release candidate), and Microsoft R Server is an enterprise-class, big data analytics platform that’s available today.
If you think about the research that is done by students, scientists, and people working in R&D departments, you now have new opportunities to bring advanced analytics to their institutions and businesses.
In this post, I’ll explain the basics of these new offerings that include R, provide some use cases and examples, and share resources to learn more.
How predictive analytics addresses business problems across industries
Industries that rely heavily on utilizing their data for forecasting, predicting, or anticipating behaviors, results, or trends need predictive analytics capability. With any business solution where you start with a set of data and ingest, cleanse, structure, and analyze that data, and then use that analysis to draw conclusions, it’s highly likely the data scientist is using R Analytics or Python. Here are examples of industries where advanced analytics are critical:
- Financial services. Many forward-thinking financial institutions have replaced their statistical models with analytics and machine learning. They are able to do risk analysis that provides input around which customers are more likely to default on their loans or cancel their service. These insights help a financial institution to make policy decisions. Similarly, portfolio managers use R packages to generate insights into complex data sets, visualize trends, determine key variables, calculate the potential ROI of campaigns, and help clients make better business decisions.
- Healthcare. My brother, who works with a healthcare company, told me about a chip that was implanted in 50-year old man’s heart, significantly improving this man’s quality of life. The chip sends out data about various vitals. A recommendation system not only monitors this and alerts doctors, it also adjusts the medication based on what the system has learned. The recommendation system could be using analytics to “learn,” and then creating a model to recommend the medication options.
- Logistics and manufacturing industries. One interesting use case is analyzing the reliability of an elevator, and determining how best to maintain it to help prevent failure. Starting with available data, like the model and make of the elevator, the oldest cable used in the elevator, the last time it was serviced, the number of incidents reported with that elevator, you can create a vector. You then feed that vector to a machine learning system that has been trained with some historical data. This artificial intelligence system provides insights that can be used for building a predictive maintenance schedule. Read the Thyssen Krupp Elevator case study
SQL Server R Services
SQL Server R Services is a new feature in SQL Server 2016 that supports enterprise-scale data science. Customers get scalable in-database analytics that can be deployed on-premises, in the cloud, or in a hybrid environment. It comprises three components:
- Advanced Analytics Extensions – A new feature in SQL Server 2016 that lets SQL Server call an R runtime and execute R code on the server
- Microsoft R Open – A set of open source R tools and packages that are used to develop and test R solutions
- Revolution R Enterprise – A set of enhanced packages and providers that support high-performance, in-database analytics with SQL Server 2016
Microsoft R Server
Microsoft R Server is an advanced analytics platform. Microsoft offers several deployment options to bring next-generation advanced analytics to your customer’s business.
Here is the key value proposition and discussion points when talking to data scientists:
- Microsoft R Server is now available for Red Hat and SuSE Linux, Hadoop, and Teradata environments
- R Server for Windows will ship as R Services in SQL Server 2016
- Microsoft R Server delivers R-based analytics to where your data is, whether on-premises or cloud
- Processing analytics in-place eliminates data movement, reducing latencies and operational costs
- Microsoft R Server scales R analytics to Big Data sizes with parallelized algorithms and distributed processing, delivering world-class performance with unmatched flexibility
Get hands-on with SQL Server R Services and Microsoft R Server
- Download SQL Server 2016 Release Candidate 1
- Visual Studio with MSDN benefit for competency and Action Pack partners
- MSDN subscriber downloads
- Sign up for Visual Studio Dev Essentials
- Provision the Microsoft Data Science Virtual Machine
Developer resources for data and analytics
On-demand training courses
- Data Science and Machine Learning Essentials
- SQL Server R Services tutorials
- Introduction to Microsoft R Open
- Best practices for using Microsoft R Server with Hadoop
- Using Microsoft R Server to address scalability issues in R
- Data mining with Microsoft R Server
- Using Microsoft R Server to operationalize your analytics
- Data preparation with R
Discussions about data, analytics, and machine learning
- Data Platform and Advanced Analytics Partner Yammer group
- Machine Learning Yammer group (Azure Advisors external network)
- Cortana Analytics Yammer group (Azure Advisors external network)