Jay Hawkinson is currently a Senior Director of Data and Analytics at Lamb Weston, gathering experience from previous roles at Hawksroost, Valmont Industries, and AFL. He has gained skills in a diverse field, including leadership, IT strategy, information technology, and consulting. He holds a master’s degree in Management and Leadership from Western Governors University, Indiana.

Can you walk me through your journey in the industry over the years and what are your current roles and responsibilities at your organization?

I have garnered expertise in the manufacturing IT space for 45 years, serving numerous roles from project management to application development and infrastructure tasks. My interest shifted toward architecture, especially enterprise architecture and data analytics. I put extra effort and value into the organization, mainly dealing with data analytics, which has become business-oriented at the current time. I empower my team to prioritize business first and then technology. I have gained success at its peak, and my career has been skyrocketing over the last five years.

Flexibility is one of the core aspects that I have always stressed when creating business value. My role in Lamb Weston is Senior Director, where I am responsible for enhancing data analytics. I have collaborated across departments to empower the organization to make information easily accessible for more informed decisions.

In what ways do you foresee emerging developments in data analytics impacting the company’s ability to meet its current business requirements?

One of the potential impacts of data analytics is the proliferation of AI, which aims to operate data processing seamlessly. It helps streamline grunt tasks that are essential to treat data as an asset, like maintaining business glossaries and data catalogs. The advancement in AI is simplifying this process by consuming time and enhancing productivity. There is a growing emphasis on descriptive analytics in verbal and written form, meeting the needs of senior leaders who prioritize visual representation. These are the major emerging developments in data analytics that are anticipated to maximize value in the future.

“We believe in people and culture as vital for organizational success. Aligning with these values, we leverage technology to enhance data analytics and its operation, promoting business growth and productivity.”

Can you share one example of how data analytics have led to a strategic decision at your organization?

I can recount two major initiatives we have taken to make strategic decisions. The first one is to perform advanced analytics in manufacturing safety because it is associated with life altering events. We wanted to make the operations smooth and secure for the welfare of employees and the companies as well. From a financial angle, data and analytics were helpful in predicting the models that we produced and seeing the data visually, which has not been done before. As a result, this led to the improvement in the manual process in a very short period of time.  

The other initiative was our engagement in automation. We had a complex supply chain scenario, and data analytics helped automate a process that previously required 16 people to work 6 hours a day for a month. This saved time and resources, allowing us to make decisions faster and generate huge revenue.

What are some of the challenges in current data dynamic services that prevent you from offering optimal solutions?

The biggest challenge has always been the foundational work that is inevitable in organizational space. In data analytics, we work in a pendulum mode to maintain a balance between doing things that are fully governed and, on the other hand, working on wild activities without any governance. In this dwindling situation, tools keep on evolving, but the challenge lies in understanding technologies that are not new in real life but just rebranding or renaming old elements that we are already aware of.

People mostly focus on generic solutions in the market, overlooking the unique features of every company’s “secret sauce.” Generic solutions exclude many company-specific needs, which is a major challenge in exploring new opportunities. Therefore, it is important to oversee how people handle data to enhance analytics, making AI operations seamless and efficient according to business needs and values.

What do you think are some of the emerging data analytics trends that excite you the most?

AI has taken the world by storm, and people cannot separate themselves from it in this fast-paced world. In data analytics, the role of AI has reduced the time it takes for people to build a data foundation and improve data quality. One of the things that I have noticed over my working experience in the last three companies is that AI tools are making operations faster and more efficient. I design a mature assessment for every company I work with, as these companies are at a low level of maturity that seeks improvement. I have been successful in doing this rapidly over the last three iterations. This has been possible not because of me but because of the advancement of AI tools. For example, a tool like Informatica enhances the lineage and the quality. As time progresses, these tools will advance more with many new features and possibilities that will help reduce manual time in building data foundations. The entire operation of data analytics is expected to be in graphical representation and narrative analysis, allowing senior executives to perform better in finding solutions.

What advice would you like to give to your fellow peers and aspiring professionals in this field?

We put emphasis on tools and techniques in the fast-paced technological era, but we somehow overlook people. So, my advice is that to achieve technological proliferation, it is first important to improve the company’s culture for organizational success. Unless you address people's issues and concerns, there is no point in leveraging technology. So, the priority should always be people and culture to bring everything else on the right track.