Tuesday, August 01, 2017

Introducing Cortana Intelligence Solutions

It has been a couple of months since I last looked at Cortana Intelligence Solutions (CIS) in the Cortana solution gallery so I was pleasantly surprised by a number of new solution templates which have been added.  These solution templates target different problem domains in different industries and are leveraging the full breadth of the Microsoft data platform offering – some of the scenarios covered are:
  • Predictive Maintenance for Aerospace - this Predictive Maintenance solution monitors aircraft and predicts the remaining useful life of aircraft engine components. Uses Azure HDInsight, Azure Machine Learning, Azure Event Hubs, etc … the different components are outlined in a solution diagram which is provided by the solution template as well.
  • Campaign optimization with SQL Server 2016 - demonstrates how to build and deploy a machine learning model with SQL Server 2016 with R Services to recommend actions to maximize the purchase rate of leads targeted by a campaign.
  • Vehicle telemetry analytics - this solution demonstrates how car dealerships, automobile manufacturers and insurance companies can use the capabilities of Cortana Intelligence to gain real-time and predictive insights on vehicle health and driving habits.

Cortana Intelligence Solutions is just one of the pieces in the Cortana Intelligence Suite next to Power BI, Cognitive Services, Microsoft Bot Framework and a whole lot of Azure cloud components such as Azure Machine Learning, Azure HDInsight, Azure SQL Data Warehouse, Azure Data Factory and many more.

Cortana Intelligence Suite provides both a platform and  process guidance (Team Data Science Process) to perform advanced analytics from start to finish. The main goal of Cortana Intelligence Solutions  is to reduce the complexity of deploying advanced data analytics solutions by providing Azure building blocks to operationalize your data science process. By providing these building blocks, Microsoft tries to enable companies to make smarter decisions at a rapid pace without having to worry about the complexity of designing, deploying and operating scalable data architectures.
I truly believe in the potential of data to be able to truly transform a business, a point which has been backed by the Capturing the $1.6 Trillion Data Dividend  white paper which showed that data leaders have a competitive advantages over their peers. These data leaders have a number of characteristics in common in the way that they invest in data:
  • Leverage new data such as linking transactional data with customer behavior, sensor data, social data, mobile data eetc…
  • Expanded use of analytic techniques – including more predictive analytics and big data processing techniques such as Mapreduce
  • Use new metrics – e.g. new ways of looking at operations or measuring performance
  • Include new users – expand the number and type of users who have access to the organization’s data and analytics
The interesting part of the Cortana Intelligence Suite is that it also describes the Team Data Science Process lifecycle which provides a recommended lifecycle which can be used to structure data science projects (see Data science for the rest of us for a good starting point of your new to the field of data science).

A key point that I can’t stress enough is the fact that you need a solution understanding of the business context. Data science projects are typically not IT-driven, business should be in the lead. Data science is a team effort where close cooperation with business is required to understand and identify the business problems. The most complex part of the data science process is formulating the  questions that define the business goals and that data science techniques can target. Most companies agree that managing customer churn is important, but how you define churn is different in each industry and also the impact of churn of specific customers might be different e.g in telco customers are influenced by both friends within the network and friends of friends (See Using social network analysis to predict churn which used data from a Belgian telco provider as well as the research paper Mining telecommunication networks to enhance customer lifetime predictions)


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