Companies are using data science to study their data and make better business decisions through programming, modeling, data analytics, visualization, machine learning (ML), and artificial intelligence (AI).
Based in Irvine, California, Alteryx is a software company focused on data engineering, data science processes, data analytics, reporting, and ML to help companies democratize their data across the organization.
Datamation interviewed Alan Jacobson — the chief data and analytics officer at Alteryx — who shared his perspective on the development and growth of the data science market:
Jacobson has been working in analytics for over 30 years with Virginia Tech, Ford Motor Company, and Alteryx, using his expertise in data science, digital transformation, product development, and international operations. His goal at Alteryx is to help transform companies using data science.
Datamation: How did you first start working in data science?
Jacobson: I was one of the first generations where personal computers started to exist in the home growing up. My dad was a professor at a university and had a PC at home when I was seven years old. He would have code written down and ask if I would type it into the computer. He likely thought he was teaching me to type, but over time, he could ask me to write a subroutine, and before long, I was writing programs in basic programming language. One could argue I was more fluent in a software language than in English, as I’m sure most papers I wrote at that age would have red lines all over it, but I could get programs to compile at run by age eight.
From that point forward, I tended to use programming and analytics as part of my toolkit in jobs. While my educational background was in engineering, using analytics in my various operating roles at Ford Motor Company, where I worked for over 25 years, likely separated me from my peers.
Datamation: What is your favorite thing about working at Alteryx?
Jacobson: It’s hard to narrow it down to one, but the mission to bring analytics to all is so core to my belief system. The belief that everyone can use data and analytics to impact their organizations and to drive positive change in the world gets me excited to come to work every Monday.
Datamation: What sets Alteryx’s data science approach or solutions apart from the competition?
Jacobson: I could list features and functions that allow people to go end to end, from geospatial to natural language processing (NLP,) from simple data manipulations to full process automation. But what truly separates Alteryx from every other product is the ease of use of our solutions. Alteryx enables everyone from finance to HR and IT to engineering to learn more about and use analytics to solve business problems in hours and days vs. months and years.
We also help every business user learn more about analytics and data science with education built-in to our offering. Our Alteryx Analytics Cloud portfolio includes a cloud-native, automated modeling solution that helps business users learn to use the power of machine learning to make data-driven decisions. Instead of requiring data science experts to code machine learning models, business analysts can now quickly build, validate, iterate, and explore models with a visually guided user experience. They can also learn about data science using Education Mode. In addition, our Auto Insights product provides AI-driven analysis to allow users to understand changes in their business.
In the end, allowing all your knowledge workers to harness the power of data and modern analytics to drive business outcomes separates Alteryx from the myriad of data science tools that are aimed at the Ph.D. developer.
Datamation: What is one key data science technology that particularly interests you?
Jacobson: Broadly, technologies that can unlock the complexities of analytics to enable more people to access the capabilities have the highest interest. The advances in analyzing non-structured data — from computer vision to NLP — have been wonderful to see over the past several years as large portions of enterprise data are non-structured.
Datamation: What is one data science technique that teams should implement?
Jacobson: The most important skill for data science teams to develop is problem formulation. Top analytic professionals are amazing at seeing business problems and figuring out how to leverage data to help solve these challenges. The specific algorithms and techniques are typically the easier part of the learning journey.
Datamation: What is one data science strategy that companies should implement?
Jacobson: The most important strategy for data science teams to get right is finding the balance between doing and teaching. If your data scientists are only building solutions but are not spending time helping the broader set of knowledge workers in the company become more analytically capable, results will be less than optimal. Equally, if data scientists are only teaching but are not also building solutions, the ROI also won’t be maximized.
Leading organizations get these efforts balanced, building what we frequently call a CoE2, a combination of a Center of Excellence with a Center of Enablement. This creates an environment where the team is viewed as both an organization that can directly solve hard problems for the business as well as an enabler to help others become more analytically capable.
Datamation: What is the biggest data science mistake you see enterprises making?
Jacobson: The biggest challenge most companies encounter at some point on the journey is understanding how to govern data science. Whether the analytics are performed by a data science team or the company has democratized analytics broadly, where HR, finance, and other domain experts are performing analytics, it is critically important to ensure processes are in place to scale effectively.
Datamation: What are some current trends in the data science market that are promising?
Jacobson: The most promising trend I see is the increase in data literacy and analytic capabilities of domain experts. Alteryx is being taught in over 850 universities across 45 countries, as part of the Alteryx SparkED no-cost analytics education program.
I’m also seeing finance, logistics, marketing, HR, and other professionals starting to have a much stronger base of analytic capabilities. This enables data scientists to move from doing simple business intelligence (BI) and data janitorial services to higher-level analyses that can impact the business in much stronger ways.
Datamation: What are the biggest factors that are driving change in data science?
Jacobson: The top factor changing the data science world is how prevalent it is becoming in every field. Students are learning more about analytic techniques and applying these skills as they enter the workforce. This democratization of analytics is also changing the roles of data scientists, making them both practitioners of more challenging problems and teachers of simpler ones.
Datamation: How has data science changed during your time in the market?
Jacobson: The profession of data science is relatively young, and the role of the team and understanding within an enterprise of what this team is responsible for continues to mature.
Many CDAOs still are challenged as other teams believe what they do is encroaching on their domains. IT teams see the function as doing things that once belonged in IT. Other functions might view data scientists that are supporting their teams as needing to belong within their functions. While this continues to improve over time, it is a challenge many data science leaders continue to face.
Datamation: Where do you predict the data science market will be 5 or 10 years from now?
Jacobson: Today, there really aren’t many end-to-end platforms like Alteryx that can provide analytics, data wrangling, geospatial, optimization, visualization, and the wide variety of techniques used to solve problems with analytics. In the coming 5-10 years there will be consolidation in the space enabling organizations to pick platforms that will meet their needs.
Personnel in Data Science
Datamation: What is one data science technology your team wants data professionals to know?
Jacobson: That’s easy, I’d love for them to know Alteryx. Seriously, most data science professionals haven’t discovered how to incorporate no-code/code-friendly tools into their toolkits. I find the best data scientists know when to code and when to drag and drop.
Datamation: If you could give one piece of advice to a data science professional in the beginning of their career, what would it be?
Jacobson: Be incredibly curious. Keep asking questions to understand the business, the processes, and the people. Those who out-learn their peers will outperform them and ultimately will find the best success.
The top skill of data
Datamation: With the shortage of tech talent, how is your team finding and retaining professionals to work in data science?
Jacobson: Finding top-notch analytic professionals is certainly difficult for every company. We focus on finding people who are aligned with our mission and who love helping others.
Datamation: For the greatest business impact, what should data science professionals be focusing on most in their roles?
Jacobson: Problem formulation is the most important skill of both data scientists, and I would argue business leaders. Ensuring that you’re asking the right questions and can frame the problem well is not only the most critical element in the problem-solving process, but it is also one of the hardest to master. You must have a good understanding of how analytics can help and a deep enough domain knowledge and business acumen to ask the right questions of the data.
Datamation: What is one of your top professional accomplishments?
Jacobson: Over my career, I have had the opportunity to work on products that are used around the world. I love being able to meet people and see how these products have impacted them. The automotive industry provided a rich canvas of opportunities to improve the health and safety of people, impact the environment, and have an economic impact on communities at a large scale.
I’m proud that the automotive industry recognized this work with a leadership award and inclusion in the Automotive Hall of Fame. I hope to make a similar impact in the software world with my work at Alteryx.
Datamation: What is your favorite part of working in the data science market?
Jacobson: The data science field is incredibly diverse, with people from an incredibly broad set of backgrounds. Getting to meet people that have vastly different experiences and hearing the types of problems they work on and methods they have used to solve them is so much fun.
And the best part — so much of what data science is about is helping change people’s lives, helping to change organizations, and, every now and then, helping to change the world for the better. Where else do you go from talking about supply chain challenges to football and Formula 1 analytics and then jump to tax optimization and pay equity? There is simply no way to get bored in the data science space!
Datamation: What is one of your favorite parts of the workweek? How does it encourage or inspire you? Do you have a favorite way to recharge during the workday?
Jacobson: I love getting to work with other professionals, rolling up our sleeves, and solving problems — whether it’s predicting when power outages will occur in the wake of a hurricane or optimizing F1 race cars’ performance. The more diverse the team and the more varied the problem, the more I can learn. This gets me energized on any day of the week or hour of the day.
Datamation: What are your favorite hobbies or ways to spend time outside of work?
Jacobson: One of my favorite hobbies for nearly three decades has been working as a firefighter and emergency medical provider in my hometown. It certainly grounds me in what is most important, is instantly rewarding, and allows me to take my mind away from work.