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The State-Of Machine Learning Adoption in the Enterprise

Sangeeta Edwin, Vice President, Data, Analytics & Insights, Rockwell Automation
Sangeeta Edwin, Vice President, Data, Analytics & Insights, Rockwell Automation

Sangeeta Edwin, Vice President, Data, Analytics & Insights, Rockwell Automation

The field of machine learning continues to evolve. And along with evolution comes a rise in demand and importance. In an interview with CIOReview, Sangeeta, the VP of Rockwell Automation, shares her insights on the importance of adopting machine learning technology. Sangeeta is a leader with over 20 years of professional experience in diverse industries like Industrial Automation, Manufacturing, and Healthcare.

In your experience what are the trends and challenges you’ve witnessed happening in the machine learning space?

Machine learning adoption is undergoing exponential growth. More specifically, machine learning techniques and algorithms are becoming more user friendly. Significant automation has been added to the process in the form of AI/ML-based frameworks that have reduced the dependency on data scientists. Machine learning libraries with well-defined interfaces and documentation are becoming more accessible and therefore facilitating its adoption. 

As far as challenges go, there have been too many technology offerings, data acquisitions and contextualization, data prep and quality, machine learning solution maintenance and identifying the right deployment model, as in cloud versus edge.

Could you talk about your approach to identifying the right partnership providers?

The key elements to be observed in the selection of providers are:

• Aligning with a strategic roadmap – Typically we look at the provider space where we see affinity with our internal business/ technology development roadmap. A provider that is in alignment but adds orthogonal dimensions to the process are also good candidates.

• Comprehension of where we are, what we have already achieved in order to help us solve the problems in front of us.

 ​Machine learning libraries with well-defined interfaces and documentation are becoming more accessible and therefore facilitating its adoption  

• Mature technology – Providers with proven change management and agile methodologies have typically been able to consolidate their reference architectures and frameworks. Some advanced providers add tools that fully automate and assemble critical aspects of the process.

What are some of the points of discussion that go on in your leadership panel?

Just to name a few, we review: Voice of Customer, Business Case Evaluation, Availability and Quality of Data or Data Trust, Business Domain Expertise, Upgrade the Skill Level (Citizen Data Scientist).

What are the strategic points that you go by to steer the company forward?

We rely on Cross Functional Team setup (Develop Advocates), focus on Transformational or Disruptive solutions, Customer Pain points/Solutions and Communication/Marketing.

How do you see the evolution of machine learning within the next few years with regard to some of its potential disruptions and transformations?

Industry is still navigating throughout the ML hype. There are a few ML-based applications that have been successfully deployed and added to applications found in the marketplace. For example, voice and image recognition, service brokering and matchmaking, consumer forecast, etc. have found their place in the domestic use but these are still far away from truly becoming disruptions in the industrial space.

Critical aspects in the success of ML evolution are:

• The reduction in complexity for mapping the domain expertise to ML-based solutions. Today there is no straightforward path to transfer domain knowledge to the data scientists where there is still a high dependency on.

• As we continue to mature and descend from the ML hype, we will soon realize that not all industrial processes are suited for ML. This aspect still needs to be settled.

• Provide greater access to ML automation.

• Legacy systems (server/IaaS based) are decelerators in the ML evolution. These tools need to undergo structural upgrades to be able to cope with the new wave of data and analytics requirements (scalability, volume, speed, multitenancy, etc.). New data and compute frameworks are going to be needed to reduce complexity while increasing automation.

• Agile change management cycles.

• ML-model management soon to become a critical-path need. • A workforce skillset aligned with the know-how to map ML to domain expertise is precious.

What would be the single piece of advice that you could impart to a fellow or aspiring professional in your field embarking on a similar venture or professional journey along the lines of your service and area of expertise?

Think outside the box. Protect a portion of your resources allocated to transformation.

Use Open Source technologies and university partnership and internship programs to pilot solutions to prove out ROI. Companies have been restricting development to their internally conceived software solutions. However, it is now understood that no single player will be able to provide all the pieces of the overall solution. Therefore, there is value in looking for potential partnerships that would increase the chances to success.

Make IP solutions accessible to the industry and let other ideas into the internal design process. This implies the need for a cultural transformation. Look at effective business and pricing models. Perhaps one can achieve more effective business by partnering accordingly. And lastly, using resources to create an all-encompassing solution hinders the ability of a company to rapidly adapt to a fast-pace technology evolution. So develop while you’re small and then grow.  

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