Analytics for all, all in one place
Govern best-of-breed analytics with an open analytics platform
By Alison Bolen, SAS Insights Editor
Let me introduce you to some smart, analytically minded employees at a growing national bank who are each seeing positive results with analytics in their own divisions:
- James is a data scientist in the business development group who analyzes data to create customized offers for the bank’s premium customers.
- Susan is a digital marketer who tracks and influences the customer journey for prospective mortgage customers.
- Christina is a risk analyst who builds risk models for the bank’s loan portfolios.
- Marvin is a citizen data scientist in a top retail banking location who takes it upon himself to analyze data about local customers at his branch.
Between them, these enterprising employees are using a dozen different packages for analytics and data management. From Informatica and SAS® to Python and R, some of these applications are open source, some are cloud-based and some are hosted as enterprise applications.
How can the IT department at this bank make sure all of these projects are using trustworthy data, accurate models and a rigorous process of analysis that will guarantee useful results? And whose responsibility is it to stitch all of these disparate code bases and business scenarios together to trace the customer journey or find other opportunities for analytics?
How can an analytics governance platform foster innovation? Find out in this video.
A single ecosystem for analytics
What this bank needs is one place to combine data for analysis, consolidate all of its analytics endeavors for tracking and monitoring, and provide appropriate access to models across divisions. But how can one platform corral all that analytics activity, provide easy access for all levels of users, and govern open source projects?
“You want to be inclusive of analytics within the shadow IT that’s going on in the organization,” says Fiona McNeill, Analytics Product Marketing Manager at SAS. “A platform for analytics governance can move you away from doing cool stuff into delivering business value.”
McNeill recommends a single platform that is centralized and inclusive, flexible and resilient. “You need something that can adapt to your IT requirements rather than forcing your IT requirements to change.”
The system should be comprehensive, inclusive and accessible to different users, combining data management capabilities, analytics functionality and model governance for data scientists.
“Most importantly, it needs to govern all analytics, not just some,” says McNeill, especially the open source packages that don’t come with their own governance functionality.
How an analytics governance platform works
So what is an analytics governance platform? It’s software that allows you to connect all the parts of your analytics ecosystem. It works behind the scenes to help data scientists manage models and help IT audit analytics compliance. It provides information about model versioning, audit trails, model lineage and source data.
And how does an analytics governance platform work? Data scientists register models to the inventory, and business analysts apply business rules to describe the conditions in which a model should trigger. Everything is documented and tested for deployment purposes. As a result, IT understands the business context associated with every model developed. The system ensures accurate results through monitoring of inventory, performance and deployment.
For analysts, the analytics governance platform provides a centralized inventory of analytics methods and a standardized code base that can be incorporated into any deployment activity or action. Code is portable, so it can be defined once, run anywhere and scaled to solve any size problem. And you can tell easily whether a model is a champion, a challenger or auto-generated.
“It’s not unusual for data scientists to recode models in a different language and wonder why the results don’t match,” says Oliver Schabenberger, Executive Vice President and Chief Technology Officer at SAS. “With analytics governance in place, you can request analytics on the same data from different languages, and you don’t have to worry about idiosyncrasies and differences of algorithm implementation, options and data models. Consistency and standardization on the back end remove worries about differences on the front end.”
An analytics governance platform allows all data users to stand behind the best algorithm. It doesn’t matter what language you write it in, or where you drive it from – you know it will be accurate.
“With the governance in place, it’s no longer an argument between an algorithm in an R package or a Python library,” says Schabenberger. “The languages execute the same analytic code on the same data model, no matter how you trigger it. And all languages reap the benefits of multithreading, distributed computing, common data access and security models.”
Schabenberger compares analytics governance to web governance, explaining that you can pull up a webpage from multiple different laptops, tablets or smartphones and still see the same content. Web governance standards allow that to happen.
Analytics governance works the same way. You can pull in data and build models from different systems throughout the organization, and the analytics governance platform ensures consistency, accuracy and performance.
Open and enterprise analytics together in one platform
Let’s get back to the bank from the beginning of this article. Analytics governance lets this organization be open enough that James, Susan, Christina and Marvin can all use their preferred tools, and the bank can guarantee consistent enterprise-class results across all the different packages. IT now governs analytics implementation and the data to make sure everyone is working against the same data model.
When so many different users are accessing data in different ways, analytics governance is the foundation for good decisions. “If you want to deliver actual value with analytics, from concept to innovation and implementation to ROI, you need an analytics governance platform,” says McNeill.