One of the biggest challenges we’ve been hearing from customers lately is that they need help operationalizing analytics to extend the value of their modeling efforts. In many cases, they’ve hired smart data analysts who can transform data and create sophisticated models, but their work is used for a single purpose and then it sits on a hard drive somewhere.

It’s difficult for most organizations to figure out how to move models from a single machine into widespread operational use. What steps can they take to make sure this critical work is used widely to transform the organization?

What do customers ask about operationalizing analytics?

Specifically, I’ve heard customers ask questions like:

• How can we bring this model into our ERP system?
• How can we bring analytics into our production control systems?
• How can we embed advanced analytics into our customer intelligence systems?
• How can we share these models with other teams who could benefit from similar analyses?

The answers are multifaceted, involving culture change, technology adoption and process adjustments – but the results can be truly transformational. If you can embed analytics into your operational systems, you can automate more decisions and further spread the use of data.

For example, GE Transportation has embedded analytics into its EdgeLINC application, which collects locomotive data while a train is traveling. Using event stream processing from SAS, the data can be analyzed in near real time to automate decisions for individual trains – and to make broader decisions about the entire fleet.

So instead of using that IoT data for a single command-and-control purpose, the data also augments the work of other systems to improve operational efficiencies, optimize crew performance and to consolidate the management of applications across the fleet.

How to operationalize analytics in your organization

How can you operationalize your models and achieve results similar to those at GE Transportation? I recommend these seven tips:

1. Know your data

Deploying and operationalizing analytics more broadly requires that you have a good handle on your data, where it comes from and how it’s organized. This step is essential so you can be confident you’re making decisions on the most relevant data.

2. Use a flexible modeling environment

It should be easy to share and collaborate on modeling efforts in an environment that is flexible, fluid and creates a sense of community development.

3. Apply governance and model management

When working across systems, be prepared to integrate disparate code, processes and information into one hub that provides consistent delivery of information – regardless of its source or destination.

4. Partner with IT and the business

IT operations, business units and analytics teams need to work together at all levels of the organization. In particular, your chief information officer, chief analytics officer and business leads should be meeting to establish shared goals.

5. Work towards zero latency

Data from sensors and IoT devices is everywhere. If you can use APIs and event stream processing to bring analytics to the data, you can automate decisions on the spot, as the data is created. When latency is reduced, the speed to decision is improved.

6. Learn new modeling techniques

The increased use of deep neural networks provides more accurate decisions – and more applications of analytics than ever before. Computer vision, rare-event modeling and natural language processing are just a few of the latest advances.

7. Establish a heterogeneous IT environment

Your data scientists need the ability to work with multiple tools and code bases – and to deploy those results in multiple ways, through APIs, voice-to-text and other technologies that move far beyond standard reporting. You should be able to consume analytics from anywhere and deliver answers to anyone or anything.

The benefits of operationalizing analytics

As these tips indicate, operationalizing analytics involves the entire analytics life cycle – from data to discovery to deployment. If data scientists can easily share data, ideas and models between teams and operational systems, the results of their work can be multiplied exponentially.

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