Businesses are not realizing the full potential of their data scientists, as most analytic models never get deployed

SAS develops a new offering to help organizations overcome “last mile of analytics” obstacles

According to IDC, only 35% of organizations indicate that analytical models are fully deployed in production.* This results in wasted effort and wasted money. With organizations investing approximately $189.1 billion in analytics this year alone conquering that last mile – deployment of analytical models and generating value from data – is a common challenge worldwide. SAS, the lea

Available now, SAS^® ModelOps, is a new packaged offering combining SAS Model Manager software and services. The offering streamlines the management, deployment, monitoring, retraining and governance of both SAS and open source analytical models. To jumpstart success, it provides the added benefit of tailored consulting services. Additionally, SAS is introducing a new standalone service, ModelOps Health Check Assessment, intended to help organizations understand how to optimize deployment.

“The inability to put analytics into action is one of the biggest challenges across industries,” said Dan Vesset, Group Vice President of Analytics and Information Management at IDC. “Many organizations adopt a data-driven culture but struggle to actually apply changes that the data suggests. The finish line is to generate real business value from analytics investments, but many businesses are never reaching it, or struggle with the so-called ‘last mile’ of implementing, operationalizing and putting analytics to work.”

“As the founder of an analytics company, I take this last-mile problem personally,” said Jim Goodnight, CEO of SAS. “This is because data doesn’t drive an organization, decisions do. And we know analytically-driven decisions are better. Analytical models can detect credit-card fraud, manage banking risk, improve marketing accuracy, identify promising drug therapies and so much more. SAS knows how to work with companies to finish this last mile and put their analytics, AI and data investments to work.”

Running the last mile with SAS

As organizations accelerate adoption of AI and machine learning, analytical assets and models are rapidly multiplying. In recent years model development has become democratized with access to open-source software and automated machine-learning capabilities, but deployment and governance remains the final hurdle. SAS helps organizations across the entire analytics lifecycle – from transparency and automated model development that is not only transparent, but customizable, to model explainability in plain English, to model deployment.

“Most companies struggle to move past the experimentation phase to unlock real value.  Our research tells us that implementation challenges, integrating AI into the company’s roles and functions, data issues (e.g., data privacy, accessing and integrating data), cost of AI technologies/solution development and lack of skills are the top challenges faced by early adopters.  To help clients accelerate the adoption of AI, Deloitte has made significant investments, including establishing a SAS Center of Excellence, to educate, deliver, scale and manage AI and Analytics solutions in a cost-effective manner,” said Nat D’Ercole, partner at Omnia AI, Deloitte Canada’s Artificial Intelligence practice.

Norwegian telecommunications company Telenor has also seen success with fast, effective model deployment with SAS. Serving Scandinavia and Asia, Telenor has an incredible amount of customer data. But the company needed help using this data to create a personalized customer experience. Together with SAS, Telenor Norway uses 10-20 predictive models to calculate the customers likelihood to buy relevant offers. Armed with this analysis, SAS and Telenor Norway developed and implemented a guided tool, called Automated Sales Tips (AST). AST puts analytics into action, determining in half a second the best offers for each customer contacting Telenor utilizing the scores from the predictive models. The models are managed and monitored using SAS Model Manager, providing a tool to monitor the quality of the models over time and a report used as a basis for their monthly model management meeting.

SAS also enabled the marketing division of Germany’s Commerzbank to deploy data-driven models that improved the customer experience. While analytics is used daily in business, now it can be integrated in all customer-centered decisions – inbound and outbound – for every touchpoint with Commerzbank’s customers, in real-time, at scale.

Customer-centricity is equally important in an evolving retail landscape. Connect Financial Services (CFS), a subsidiary of the JD Group and Pepkor, the largest non-grocery retailer in South Africa, use SAS to gain the competitive edge. The JD Group, with more than 850 stores operating across four countries, receives a large number of customer credit applications each day. By automating much of the credit decisioning process with machine learning and advanced analytics, CFS can make relevant offers to customers while still mitigating risk to the retailer.

“SAS allows us to make intelligent decisions,” said Eugene Ehlers, CFS Credit Executive at Pepkor. “Internally, we refer to ‘SAS as the brain.’ We can quickly deploy changes and additions to our credit decisioning models in real-time, which allows us to ensure that the right amount of credit is offered to the customer when and where the customer needs it.”

Moving organizations forward with ModelOps

According to McKinsey, the total annual value generated by analytics and AI is between $9.5 trillion and $15.4 trillion. But without the ability to push analytical models into production, much of this potential value is lost. ModelOps is where analytical models move from the data science “lab” into IT production, with a regular cadence of updates and deployments as these models are managed, scaled, monitored and retrained as needed. In the race to realize value from analytics, ModelOps is a winning ingredient that only a few companies are using.

SAS ModelOps meets the need for model management software paired with consulting that can be tailored to meet a customer’s specific requirements. The offering will help customers to jumpstart their implementation, use and adoption of SAS Model Manager so that they can operationalize analytics in a consistent, continuous manner. SAS ModelOps also enables customers to monitor the performance of all champion models to ensure relevance as data and market conditions changes over time.

SAS believes that to finish the last mile, analytics needs to emulate the applications-development community’s approach to collaboration – DevOps – and adopt practices that will accelerate model creation and deployment. The ModelOps Handbook, available for download later this year, is a technology-agnostic handbook that helps organizations accelerate the analytics life cycle through repeatable best practices. With a focus on building collaboration and processes to facilitate the handoff between the development and deployment phases, the goal of the ModelOps Handbook is to reduce the time to deployment and increase organizational capacity to create, train and refine analytical models.

Because deployment of analytical models is both challenging and valuable, SAS is also introducing a new service, ModelOps Health Check Assessment. Through an on-site workshop, organizations can determine their level of maturity and readiness to successfully deploy and manage analytical models. The assessment also provides recommendations to move the company forward to make better decisions.

To learn more about how SAS can help organizations conquer the last mile of analytics, visit sas.com/discover or follow the conversation on social media using the hashtag #DiscoverSAS.

* IDC’s Advanced and Predictive Analytics survey and interviews, n = 400, 2017 – 2019.

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