You can’t read the tech press without seeing news of exciting advancements or opportunities in data science and advanced analytics. We sat down with two of our own Microsoft Data Scientists to learn more about their role in the field, some of the real-world successes they’ve seen, and get their perspective on today’s opportunities in these evolving areas of data analytics.
If you want to learn more about predictive analytics in the cloud or hear more from Val and Wee Hyong, check out their new book, Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes.
First, tell us about your roles at Microsoft?
And how did you get here? What’s your background in data science?
[Val] I started in data science over 20 years ago when I did a PhD in Artificial Intelligence. I used Artificial Neural Networks to solve challenging engineering problems, such as the measurement of fluid velocities and heat transfer. After my PhD, I applied data mining in the environmental science and credit industry: I did a year’s post-doctoral fellowship before joining Equifax as a New Technology Consultant in their London office. There, I pioneered the application of data mining to risk assessment and marketing in the consumer credit industry. I hand coded over ten machine learning algorithms, including neural networks, genetic algorithms, and Bayesian belief networks in C++ and applied them to fraud detection, predicting risk of default, and customer segmentation.
[Wee Hyong] I’ve worked on database systems for over 10 years, from academia to industry. I joined Microsoft after I completed my PhD in Data Streaming Systems. When I started, I worked on shaping the SSIS server from concept to release in SQL Server 2012. I have been super passionate about data science before joining Microsoft. Prior to joining Microsoft, I wrote code on integrating association rule mining into a relational database management system, which allows users to combine association rule mining queries with SQL queries. I was a SQL Server Most Valuable Professional (MVP), where I was running data mining boot camps for IT professionals in Southeast Asia, and showed how to transform raw data into insights using data mining capabilities in Analysis Services.
What are the common challenges you see with people, companies, or other organizations who are building out their data science skills and practices?
[Val] The first challenge is finding the right talent. Many of the executives we talk to are keen to form their own data science teams but may not know where to start. First, they are not clear what skills to hire – should they hire PhDs in math, statistics, computer science or other? Should the data scientist also have strong programming skills? If so, in what programming languages? What domain knowledge is required? We have learned that data science is a team sport, because it spans so many disciplines including math, statistics, computer science, etc. Hence it is hard to find all the requisite skills in a single person. So you need to hire people with complementary skills across these disciplines to build a complete team.
The next challenge arises once there is a data science team in place – what’s the best way to organize this team? Should the team be centralized or decentralized? Where should it sit relative to the BI team? Should data scientists be part of the BI team or separate? In our experience at Microsoft, we recommend having a hybrid model with a centralized team of data scientists, plus additional data scientists embedded in the business units. Through the embedded data scientists, the team can build good domain knowledge in specific lines of business. In addition, the central team allows them to share knowledge and best practices easily. Our experience also shows that it is better to have the data science team separate from the BI team. The BI team can focus on descriptive and diagnostic analysis, while the data science team focuses on predictive and prescriptive analysis. Together they will span the full continuum of analytics.
The last major challenge I often hear about is the actual practice of deploying models in production. Once a model is built, it takes time and effort to deploy it in production. Today many organizations rewrite the models to run on their production environments. We’ve found success using Azure Machine Learning, as it simplifies this process significantly and allows you to deploy models to run as web services that can be invoked from any device.
[Wee Hyong] I also hear about challenges in identifying tools and resource to help build these data science skills. There are a significant number of online and printed resources that provide a wide spectrum of data science topics – from theoretical foundations for machine learning, to practical applications of machine learning. One of the challenges is trying to navigate amongst the sea of resources, and selecting the right resources that can be used to help them begin.
Another challenge I have seen often is identifying and figuring out the right set of tools that can be used to model the predictive analytics scenario. Once they have figured out the right set of tools to use, it is equally important for people/companies to be able to easily operationalize the predictive analytics solutions that they have built to create new value for their organization.
What is your favorite data science success story?
[Val] My two favorite projects are the predictive analytics projects for ThyssenKrupp and Pier 1 Imports. I’ll speak today about the Pier 1 project. Last spring my team worked with Pier 1 Imports and their partner, MAX451, to improve cross-selling and upselling with predictive analytics. We built models that predict the next logical product category once a customer makes a purchase. Based on Azure Machine Learning, this solution will lead to a much better experience for Pier 1 customers.
[Wee Hyong] One of my favorite data science success story is how OSIsoft collaborated with the Carnegie Mellon University (CMU) Center for Building Performance and Diagnostics to build an end-to-end solution that addresses several predictive analytics scenarios. With predictive analytics, they were able to solve many of their business challenges ranging from predicting energy consumption in different buildings to fault detection. The team was able to effectively operationalize the machine learning models that are built using Azure Machine Learning, which led to better energy utilization in the buildings at CMU.
What advice would you give to developers looking to grow their data science skills?
[Val] I would highly recommend learning multiple subjects: statistics, machine learning, and data visualization. Statistics is a critical skill for data scientists that offers a good grounding in correct data analysis and interpretation. With good statistical skills we learn best practices that help us avoid pitfalls and wrong interpretation of data. This is critical because it is too easy to unwittingly draw the wrong conclusions from data. Statistics provides the tools to avoid this. Machine learning is a critical data science skill that offers great techniques and algorithms for data pre-processing and modeling. And last, data visualization is a very important way to share the results of analysis. A good picture is worth a thousand words – the right chart can help to translate the results of complex modeling into your stakeholder’s language. So it is an important skill for a budding data scientist.
[Wee Hyong] Be obsessed with data, and acquire a good understanding of the problems that can be solved by the different algorithms in the data science toolbox. It is a good exercise to jumpstart by modeling a business problem in your organization where predictive analytics can help to create value. You might not get it right in the first try, but it’s OK. Keep iterating and figuring out how you can improve the quality of the model. Over time, you will see that these early experiences help build up your data science skills.
Besides your own book, what else are you reading to help sharpen your data science skills?
[Val] I am reading the following books:
- Data Mining and Business Analytics with R by Johannes Ledolter
- Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) by Ian H. Witten, Eibe Frank, and Mark A. Hall
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die by Eric Siegel
[Wee Hyong] I am reading the following books:
- Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart by Ian Ayres
- Competing on Analytics: The New Science of Winning by Thomas H. Davenport and Jeanne G. Harris.
Any closing thoughts?
[Val] One of the things we share in the book is that, despite the current hype, data science is not new. In fact, the term data science has been around since 1960. That said, I believe we have many lessons and best practices to learn from other quantitative analytics professions, such as actuarial science. These include the value of peer reviews, the role of domain knowledge, etc. More on this later.
[Wee Hyong] One of the reasons that motivated us to write the book is we wanted to contribute back to the data science community, and have a good, concise data science resource that can help fellow data scientists get started with Azure Machine Learning. We hope you find it helpful.