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Data Science Student Pursues Data Analysis in Anticipation of ‘Industry 4.0’ Smart Factories

January 10, 2017 By UW Data Science Team Leave a Comment

Editor’s note: Nicholas completed the UW Data Science program in 2018. He is now the director of information technology at Steel King Industries, Inc.

Though only halfway finished with his University of Wisconsin Master of Science in Data Science, Nicholas Sondelski already knows his future lies in data-driven manufacturing.

Nick is the IT manager at Pointe Precision, a Plover, Wis. manufacturing company, where he’s witnessed the dawn of Industry 4.0—or, the evolution of “smart factories.” Today, these facilities produce a tremendous amount of data that can be used to automate and improve manufacturing technologies.

“Data is exploding right now,” he says, “but manufacturing needs more professionals who can study and interpret this data.” And Nick sees opportunities here—for manufacturing and for his career.

A Start in IT

Nick earned bachelor’s degrees in computer information systems and business administration from UW-Stevens Point, the same university he’s attending online to earn his master’s degree in data science.

For the last 15 years, he’s been at Pointe Precision, moving up from intern to IT manager. Nick loves his work. The company manufactures mission-critical components for surgical instruments, fuel delivery systems on jet engines, and firearms—all of which need to withstand high heat and are vital to the end-use application.

Pointe Precision’s leaders recognized the potential for data to provide insight into processes and, ultimately, better business decisions. Thus, they supported Nick’s decision to pursue a master’s degree in data science.

“Every day, I see opportunities for improvements in a variety of business processes from the shop floor to the front office. Using data analysis techniques gives us additional insight that we may not have seen in the past. That’s what drove me to the UW Data Science master’s program. I hope to become a data analyst so I can help the business make even more strategic decisions.”

A Data Pioneer

Nick is carving a path for data science in manufacturing. And he was one of the first students to sign up for the 12-course, 36-credit UW Data Science program, which is offered online through six University of Wisconsin campuses.

UW Data Science students are a diverse group—they come from backgrounds in business, science, and many other disciplines. (“This makes class discussions very interesting,” Nick adds.) That’s because data science applies to just about every industry.

“I had been leaning toward an MBA program until the data science opportunity came along. Once I started taking classes, everything clicked. I knew this is exactly what I wanted to do.”

Plus, the career outlook alone was a major draw. “Pointe Precision is a growing, successful company, and I love working there. But it’s also comforting to know that analytics professionals are in demand everywhere.”

A Challenging, Useful Education

“Before I started the UW Data Science program, I was unsure of the online format. But the courses are more challenging, fun, and useful than I expected.”

In his current position, Nick has used his education to look at data, cleanse and manipulate it, and extract insights. And he now has hands-on experience with programming languages Python, R, AWS on Amazon’s cloud, along with Hive and Pig on the Hadoop file system used for big data.

The UW Data Science curriculum includes a balance of technical subjects—such as databases and statistics—and courses focused on building management and communications skills. At a recent conference, Nick gave a presentation and used some of the persuasive presentation skills he learned in his data science communication course.

In class, he created a data visualization demonstrating how fast new parts make it through Pointe Precision’s engineering department. “I was able to share a unique perspective with the executive team at Pointe Precision, and they wanted to see more. The VP of engineering was impressed to see data presented in such a different way.”

Industry 4.0 and Beyond

Nick expects to graduate with his Master of Science in Data Science from UW-Stevens Point in 2018. He wants to be more involved in a data science role, leading his own team of Industry 4.0 data professionals. Nick is committed and passionate about Pointe Precision’s mission—but he’s also interested in changing the business and the manufacturing industry as a whole.

“As a data scientist, I have the opportunity to make an impact every day. So far, I’ve only had a taste of what it would be like to apply my data science knowledge. Whether I get to help modify a process, save costs, or make better business decisions, I know it will be a rewarding career.”

Find out more about being a data scientist by visiting the “What Do Data Scientists Do?” page. Or, if you have questions about the University of Wisconsin Data Science master’s degree, an adviser would be happy to help. Call 608-800-6762 or email learn@uwex.wisconsin.edu.

Data Scientist Jobs: What a Search on LinkedIn Reveals

Why Combining Psychology, Research Methods and Statistics Can Lead to a Powerful Data Science Career

The Story of Moneyball Proves Importance of Both Big Data and Big Ideas

UW Data Science Student Works Toward Master’s Degree to Expand His Future Career

Data Scientist Jobs: What a Search on LinkedIn Reveals

December 15, 2016 By UW Data Science Team Leave a Comment

A quick search on LinkedIn reveals more than 312,000 data scientist jobs nationwide. More than 220,000 of these jobs were posted in the last month. More than 19,000 were posted in the last 24 hours alone, suggesting that those who pursue a data science degree can literally (and figuratively) bank on job security.

Data scientists in demand

Data scientists work in virtually every industry, including healthcare, computer science, information technology, retail, marketing, manufacturing, transportation, communication, education, insurance, finance, science, security, government, nonprofit, and law enforcement just to name a few. Companies such as Facebook, Amazon, IBM, Kayak, Capital One, The New York Times, and others continue to clamor for those with data science training who can drive business intelligence using voluminous and complex data.

What exactly are these and other companies looking for? Following are just a few snippets taken verbatim from data science job openings posted recently on LinkedIn:

Use big data tools (e.g., Hadoop, Spark, H2O, and AWS) to conduct the analysis of billions of customer transaction records.
Establish scalable, efficient, and automated processes for large-scale data analyses that will tie into production systems.
Use predictive analytics and machine learning to create new products or drive business decisions.
Apply expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products.
Interpret, document, and present/communicate analytical results to multiple business disciplines, providing conclusions and recommendations based on customer-centric data.

Finding the right data scientist training

Employers want to hire data scientists who don’t just know the technology but who know what to do with it; agile communicators who are well-rounded can remain flexible as new challenges and business needs arise. If you’ve thought about pursuing a career in data science, look for a program that provides not only a solid foundation rooted in statistics and computer programming but also one with instruction in economics, business management and communication.

Aligning your education with the top 12 most in-demand data science skills

The interdisciplinary nature of the University of Wisconsin Data Science program caters directly to the types of skills that employers seek. Many employers require a master’s degree in data science or a comparable discipline (e.g., statistics, computer science, or mathematics) as well as the ability to perform these 12 most-common tasks:

Create and manage simple databases in Access and SQL Server.
Write and execute SQL statements to retrieve and manage data.
Analyze data to solve basic analytics problems using Excel and R.

You’ll learn all of this and more, including how to use other well-known data science tools (e.g., PowerBI and Access) in the course, Foundations of Data Science.

Use Python and R to analyze real-world data.
Use an application programming interface (API) to collect real-world data from social media.
Clean and format data for analysis.

You’ll learn all of this and more in the course, Programming for Data Science.

Use tools and software such as Hadoop, Pig, Hive, and Python to compare large data-processing tasks using cloud-computing services.

You’ll learn this and more about big data analysis in the course, Big Data: High-Performance Computing.

Help non-technical professionals visualize, explore, and act on data science findings.

You’ll master technical, informational, and persuasive communication in the course, Communicating about Data.

Create effective visuals to maximize readability, comprehension, and understanding of complex datasets.

You’ll learn this and more in the course, Visualization and Unstructured Data Analysis.

Use data and predictive analytics to inform the decision-making process.

You’ll learn this and more, including optimization, decision analysis, game theory, and simulation in the course, Prescriptive Analytics.

Transform findings from data resources into actionable business strategies.
Explain how data assets can be used to develop competitive advantage.

You’ll learn this and more about how to obtain decision-making value from an organization’s data assets in the course, Data Science and Strategic Decision Making.

In addition, the University of Wisconsin partners with an industry advisory board dedicated to helping students bridge the gap between classroom learning and real-life data science challenges. Their continual input helps ensure course content meets today’s employer needs in this rapidly evolving field. This makes for a smooth transition into the workforce.

Giving employers what they need and want

Upon completion of the University of Wisconsin master’s in data science program, individuals are prepared to:

Identify organizational questions for which data science can provide answers.
Collect and manage data to devise solutions.
Select, apply, and evaluate models to solve data science tasks.
Interpret data science analysis outcomes.
Effectively communicate data science-related information in various formats and for various audiences.
Value and safeguard the ethical use of data.
Transform findings from data resources into actionable business strategies.

Increase your value to employers seeking savvy, well-rounded data scientists by earning a University of Wisconsin master’s degree in data science. To learn more, download a program guide at https://datasciencedegree.wisconsin.edu/.

Why Combining Psychology, Research Methods and Statistics Can Lead to a Powerful Data Science Career

September 19, 2016 By UW Data Science Team Leave a Comment

In psychology, data is everything. Without data, it’s difficult to quantify findings that lead to more effective care and positive outcomes. Making data actionable requires the insights of a savvy psychologist with a background in data science who can help others understand what data is truly saying about human behavior—and what it could mean going forward.

Data science—particularly research methods and statistics—enables psychologists to dig deeply into important challenges we face in today’s society. To drive change effectively, psychologists must know how to conduct laboratory or field experiments. Doing so creates data to support evidence-based strategies that help people tackle barriers and live more comfortable and satisfying lives.

However, data’s reach extends far beyond traditional psychological applications. Consider the psychologist who analyzes data to design more appropriate teaching methods for students with learning disabilities. Or one who conducts research to understand the best marketing strategy for a business. Or perhaps one who examines human responses to natural and technological hazards to better understand why people do or do not conserve resources or why and how diseases spread. Without data, developing solutions to address these challenges would be nearly impossible.

Advancing Your Psychology Career Using Data Science

Data science skills in psychology are not only in-demand, but they also yield lucrative salaries. Consider the following high-paying psychology jobs that benefit from a degree in data science:

Industrial/organizational (I/O) psychologist. These individuals use data analyses to help companies make more informed decisions. They provide insight not only into trends and patterns in the data but also human behavior related to that data, helping to address workplace issues that affect individuals, teams, and organizations. The national average salary for an I/O psychologist is $105,000, according to AllPsychologySchools.com. In 2021, the Bureau of Labor Statistics reported a mean salary of $113,000 for I/O psychologists. Those who are self-employed consultants earn the most, followed by those working in pharmaceuticals and private sector healthcare.

Research psychologist. These individuals collect and manipulate data to investigate psychological issues related to perception, memory, learning, personality, and cognitive processes. They also analyze test results using statistical techniques and collaborate with other scientists to formulate theories. According to Glassdoor.com, the national average salary for a research psychologist is $103,000; ZipRecruiter.com reports it at $86,000.

Neuropsychologist. These individuals study behavior and brain function using imaging techniques and behavioral assessments. According to Indeed.com, the national average salary range for a neuropsychologist is $87,000 to $237,000.

Forensic psychologist. These individuals conduct research related to the criminal justice system, helping with jury selection or mental state examinations of criminal defendants. Often times, forensic psychologists are focused on determining why a certain type of person commits crimes and how to prevent those crimes from happening in the future. According to Indeed.com, the national average salary for a forensic psychologist is $104,000.

Intersection of Data Science and Psychology

Big data has permeated many academic disciplines, and psychology is no exception. As data continues to proliferate due to technological advances, psychologists with a background in data science, research methods, and statistics can help articulate why certain patterns and trends may occur. They may also be able to explain how technology continues to shape human behavior.

For instance, take the study of Computers as Persuasive Technology, better known as “Captology.” Coined by Dr. B.J. Fogg, this term describes the manner in which we “…use computers to interact with people and attempt to influence them to change their attitudes or behaviors” (Fogg, 2002). With technology now occupying every corner of the world, we are all susceptible to its overreach.

In an article from Trust Technical Services, Issam Wadi writes that technology such as this permeates our politics, education, organizations, behavioral sciences, and more. We also see this on a micro-level in our day-to-day lives: advertisements persuading you to purchase that soap you never liked but will now buy because your favorite celebrity endorses it, or your favorite video game making you think differently about how you see the world through their lens. Through the intersection of technology and persuasion, there is an indefinite change in human behavior and attitudes toward the world around us.

The Need for Data Science Professionals in Psychology

Data science professionals in psychology are essential. These professionals can help ensure data governance, proper data mapping, and suitable data application. They will increasingly be in demand as employers seek to capitalize on the inherent insights of big data. What are the trends, and why? How can various business sectors respond accordingly? Psychologists with data science skills can help find the answers.

For more information about data science, explore the University of Wisconsin Master of Science in Data Science website or speak with an enrollment adviser at 608-800-6762 or learn@uwex.wisconsin.edu.

The Story of Moneyball Proves Importance of Both Big Data and Big Ideas

August 24, 2016 By UW Data Science Team Leave a Comment

Picture this: The bases are loaded at the bottom of the ninth inning. A batter steps up to the plate, scuffs his sneakers against the mud-caked marker that signals home, and glares at the pitcher’s mound. The crowd roars as the pitcher nods to the catcher and winds up for the throw. In the blink of an eye, the fastball slices through the air at nearly 90 miles per hour. Will the batter get a hit and enable at least one run? Or will he miss and disappoint his team?

To answer this question, you might choose to focus on the usual statistics—batting average, RBIs, hits, and stolen bases. The player’s overall past performance is usually an indicator of his future success, right?

What if these statistics aren’t the best predictors of whether a player can perform under pressure? This is the premise behind moneyball theory, which emphasizes only two important data points as predictors of a player’s abilities:

Slugging percentage: Total bases divided by at-bats.
On-base percentage: The rate at which a batter gets on base for any reason excluding fielding errors, fielder’s choice, fielder’s obstruction, or catcher’s interference.

Essentially, moneyball theory seeks to answer these two very basic questions: Can the player hit? Can the player create runs? If the answer to one or both of these questions is “yes,” chances are you’ve got a good pick on your hands—and one who can perform when duty calls.

At least that’s what Oakland A’s general manager Billy Beane believed in 2002 when he used moneyball theory to pick a team of undervalued players that would eventually go on to achieve a 20-game winning streak and clinch the American League West. These moneyball-inspired picks came in the wake of Beane losing three highly valuable players—Jason Giambi, Johnny Damon, and Jason Isringhausen—to free agency.

Moneyball Theory in Action

Using data analytics and moneyball theory, Beane hired the best players he could with an extremely limited budget for payroll. With approximately $41 million in salary, the Oakland A’s ultimately competed with larger market teams such as the Yankees, who spent over $125 million in payroll during the 2002 baseball season.

Exactly how did he do it?

Beane performed data mining on hundreds of individual players, ultimately identifying statistics that were highly predictive of how many runs a player would score. These statistics weren’t necessarily numbers that baseball scouts traditionally valued. Instead of competing for high-priced home-run hitters with high batting averages, he sought lower-cost players with high on-base percentages. His theory was that players with a higher on-base percentage would be more valuable than those with lower on-base percentage even when those with the lower percentage ultimately hit more home runs and were faster and even stronger. He also encouraged players to focus on walks, thereby forcing pitchers to throw strikes to ensure an out.

Effects of Moneyball Theory on Baseball

Between 2000 to 2006, the Oakland A’s went on to average 95 wins, capture four American League West titles, and make five playoff appearances. Although baseball scouts and general managers initially scoffed at moneyball principals, they slowly began to realize the validity of the theory and sought to take advantage of it. The Red Sox tried to hire Billy Beane but were unsuccessful. Instead, they hired Bill James—the creator of sabermetrics on which moneyball theory is based—in an advisory capacity.

Over the years, Beane’s moneyball theory has had a lasting legacy in baseball, allowing teams with significantly lower budgets to choose players that would allow them to successfully compete with big-market teams such as the Red Sox and Yankees.

According to a 2013 article on MLB.com, “Moneyball has played a role in 15 of 30 teams getting into at least one postseason series—not a Wild Card Game, but a postseason series—the last three years. Moneyball may also be why nine franchises have won the World Series the last 13 seasons.”

What Data Scientists Can Learn from Moneyball

Today, the story of Billy Beane and moneyball theory is famous. It was the subject of Michael Lewis’ book Moneyball and the film of the same name. But according to Forbes, “What’s interesting is that as widely-familiar as the story is, it is almost as widely misunderstood.”

Analyzing data was nothing new to baseball in 2002. Data on baseball players has been available since the 1800s and data analytics used since the ’70s.

The reason Beane’s strategy was ground-breaking is because he “had the courage to use the insight gleaned from data analytics to drive the way he ran his business… ‘Moneyball’ succeeded for the Oakland A’s not because of data analytics but because of Beane, the leader who understood the analytics’ potential and changed the organization so it could deliver on that potential.”

His story should resonate with data scientists. It speaks to the advantage of making data science part of an organization’s DNA, but just as importantly, it highlights how a big idea about big data can translate to serious business gains.

Interested in the online 36-credit University of Wisconsin Master of Science in Data Science? Start exploring the degree program here.

Can I be a Data Scientist? Healthcare Analytics in Health Information Management and Technology

July 19, 2016 By UW Data Science Team Leave a Comment

You’re in the health information field, but you want to expand on your expertise or explore other fields… Are you the type to geek out over numbers, equations, and statistics? Do you frequently question existing assumptions and processes? If the answer to either or both of these questions is ‘yes,’ then data science—an interdisciplinary field that focuses on the use of data to inform decisions and make new discoveries—may be a good fit for you.

What exactly is a data scientist?

“A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization,” says Anjul Bhambhri, vice president of big data products at IBM.

Data scientist is one of the five best technology jobs, according to the 2022 US News Best Jobs report. Not only is the job in demand, but it’s also highly lucrative.

According to the U.S. Bureau of Labor Statistics, it is projected that the employment of data scientists will grow 36 percent from 2021 to 2031, approximately creating 13,500 job openings. And increasingly, these companies are in the healthcare sector. As big data continues to grow commensurate with electronic health records, data scientists are the ones who utilize healthcare analytics to manage and merge data sources, create visualizations, build mathematical models, and present insights—all with the goal of producing answers, predictions, and calculations as quickly and accurately as possible.

A simple search for “healthcare data scientist” on LinkedIn generates more than 300,000 jobs nationwide, many of which require previous healthcare experience and knowledge of patient data.

According to Glassdoor, data scientists earn an average annual salary of $114,067. The need for big data skills continues to lead to pay increases— approximately 20 percent as of 2021, according to Burtch Works. It’s probably fair to assume that those working in healthcare—and who possess a formal health information management education—may earn even more due to their specialized knowledge.

Advancing your career

According to AHIMA’s Career Map, many employers actually require—or strongly prefer—a master’s degree for certain types of data-related positions such as informatics researcher, data analyst, research and development scientist, project manager, director of clinical informatics, or chief clinical informatics officer. A master’s degree in data science certainly fulfills these requirements.

Even if you don’t intend to advance your career in the immediate future, a degree in data science can make you a more effective health information management professional. By building a solid foundation in healthcare analytics, computer science and applications, communication, modeling, statistics, analytics and math, those with a working knowledge of data science are able to:

Collect and report on complex data
Communicate findings to both business and IT leaders
Apply data to business problems

Relevant healthcare topics in data science

The following data science coursework is also particularly helpful for individuals currently working in health information management:

Data warehousing. Health information management professionals can use data warehousing skills to collect, clean, and prepare data stored in the electronic health record and various other electronic systems. Being able to evaluate data in terms of its source, volume, frequency, and flow is an important part of ensuring effective health information exchange.

Big data computing. Health information management professionals can use big data computing skills to answer important questions, such as: How can the organization reduce readmissions or prevent hospitalizations? What is the root cause of certain denials? Can the organization justify expanding a particular service line? Should it join an Accountable Care Organization or purchase a physician practice?

A 2011 study by MGI and the McKinsey Global Institute found that there will be four to five million jobs in the United States requiring data analysis skills by 2018. The report also found that the healthcare sector could create more than $300 billion in value annually if it were to use big data and healthcare analytics to creatively and effectively drive efficiency and quality. The ability to perform big data computing puts health information management professionals at the forefront of the big data revolution.

Data communication. Health information management professionals can use data communication skills to educate decision makers about the nature, structure, and interpretation of coded data—including ICD-9 vs. ICD-10 as well as a whole host of other clinical and non-clinical data.
Data mining. Knowledge of data mining can benefit health information management professionals frequently who frequently generate reports necessary for clinical and operational improvement. Accessing and analyzing unstructured data helps health information management professionals create a more well-rounded and insightful clinical picture.
Ethics of data science. Being able to discuss the privacy, intellectual property, security, and integrity of data is important aspect data management, release, and exchange in an increasingly electronic health information management environment. Those who are well-trained in the ethics of data science can contribute intelligently to these types of conversations and drive change within their organizations.

Finding the time to benefit your field

Obtaining a master’s degree in data science will invariably help health information management professionals tackle these ongoing challenges within the industry:

Raise awareness of the intersection between health information management and big data
Justify a seat at the decision-making table
Capitalize on the power of healthcare analytics in the electronic health record

As a working adult, though, it can be hard to find the time to pursue a graduate degree. Fortunately, the University of Wisconsin Master of Science in Data Science was designed with adult learners in mind. It’s 100 percent online, making it easier to fit into an already-busy schedule. Visit the UW MS in Data Science homepage to learn more about this degree and find out if it’s right for you.

New Video Showcases UW Data Science Online Learning Experience

April 12, 2016 By UW Data Science Team Leave a Comment

Wake up. Let the dog out. Drop the kids off at school. Work from nine to six. Pick the kids up from practice. Go to the gym. Get dinner on the table. Clean up. Their schoolwork.[Your schoolwork.] Orchestrate bedtime. Sleep. Repeat.

Is earning a degree or certificate actually realistic for a fast-paced life like yours?

Yes, absolutely. No matter what your daily schedule involves.

Online learning through the University of Wisconsin Master of Science in Data Science program is convenient and flexible enough to fit even the busiest schedule. A simple Web interface makes it easy to access courses, connect with instructors and classmates, check your grades, and get support from student services when you need it. Clear course expectations help you stay on track and plan your studies around work and family life.

Watch this video to discover six reasons why you will love learning about data science online through University of Wisconsin.

If you’d like to find out more about online learning, visit the About Online Learning page of our website.

Are you looking to start or advance your career in data science? Find out why the online UW Master of Science in Data Science is a great program for aspiring and established data professionals. Call 608-800-6762 or email learn@uwex.wisconsin.edu to discuss your options with an enrollment adviser today.

[Webinar] View Professor Lyna Matesi’s Interview with Data Science Guru Zach Gemignani

April 6, 2016 By UW Data Science Team Leave a Comment

Did you miss our free webinar on March 30, 2016? Worry not. You can view it here right now, or anytime!

In this 60-minute webinar, University of Wisconsin Data Science Professor Lyna Matesi interviewed Zach Gemignani, co-author of the book, Data Fluency: Empowering Your Organization with Effective Data Communication.

Zach is the co-founder and CEO of Juice Analytics, a company that helps organizations display, communicate, and act on their data in exciting and easy-to-understand ways. Zach and his brother, Chris, co-authored Data Fluency, which Dr. Matesi’s students use in her course, DS 735: Communicating About Data.

Talking About Data Science

The conversation between Professor Matesi and Zach focused on what it takes to create a data-fluent culture in any organization. Specifically:

What is the Data Fluency Framework, why it is useful, and what inspired it.
Some important lessons Zack has learned from past mistakes in producing and sharing data products.
How long it takes to build a data-fluent organization.
Ways Zach has overcome cynicism about how data is presented.
Which is more lucrative: taking a job as a data scientist or starting your own firm.

By all accounts, the webinar was a big success. UW Data Science Program Manager David Summers said, “This event was great. About 40 participants joined the webinar. Lyna and Zach had a good conversation and answered questions from the audience. From everything I have heard, people found it interesting and valuable. So stay tuned. We plan to offer more of these events in the future!”

View the recorded webinar now.

About Juice Analytics

Juice Analytics was founded in 2005 because brothers Zach and Chris saw a problem in the marketplace. Organizations were working hard to analyze and disseminate data, but they were not delivering the “last mile” in data visualization—where data actually creates better decisions. Juice Analytics is tackling this problem head on.

To find out more, visit:

Data Management: An Interview with a Career Professional

November 10, 2015 By UW Data Science Team Leave a Comment

Missy Wittmann, enterprise data strategist at American Family Insurance, shares her thoughts on big data, the challenges facing data managers, and what makes the new University of Wisconsin Data Science program unique.

The field of data science is exploding as fast as the world is creating data. But what is behind this growth, and what does it mean for business? To find out, we spoke with Missy Wittmann, a data management leader and evangelist for more than 20 years. In this exclusive interview, Ms. Wittmann talks about her experiences, her influences, and her advice for those considering a career in data science.

Please tell us about yourself and your background.

Certainly! My name is Missy Wittmann and I serve as enterprise data strategist at American Family Insurance. I am also president of the Wisconsin Data Management Association (DAMA) Chapter and vice president of Chapter Services for DAMA International.

I have worked in data management for 20 years. You could say it’s a passion of mine!

What do you do as enterprise data strategist?

I analyze how we define, model, source, and quantify data across the enterprise. My team and I look at data in the context of the business’s needs and its customers’ needs. Our goals are to help the business win new customers and retain existing ones. At AmFam, we have a data lab we use to find patterns, make predictions, and improve customer service and satisfaction.

I talk to the business side a lot. When the company runs TV commercials, for example, we collect and analyze data from social media sites to learn how well our ads are received. Are we speaking in the same voice as our customers? Has an ad resonated so well that it’s gone viral on Facebook or Twitter? The insights we find influence our strategies.

How did you get your start in data management?

I am very fortunate to have been in the right place at the right time. As I said, I’ve been in data management for 20 years. I have worked at American Family for 30. At some point, I got involved on a data project and became passionate about it. Data drives so much in our daily lives. How can you not be interested in that, or excited by it?

My employer recognized my interest in data management—as well as the importance of data to the organization. The company supported me with professional development and other educational opportunities to foster my knowledge and help me grow my career in this field.

Tell us about DAMA.

The Data Management Association is a users’ group. We provide training on data management. It’s a way for data practitioners to meet, network, and share ideas. DAMA meets quarterly and hosts internationally known speakers. We are growing. We draw people from all over Wisconsin. People of all ages and walks of life are coming into data management these days, particularly students.

The opportunity to hear from guest speakers is one of the best parts of DAMA membership. We recently hosted Bill Inmon, the grandfather of data warehousing. He spoke about the future of the field. To hear him speak anywhere else would cost a lot of money.

Why is big data getting so much attention right now?

Because it’s huge! Today’s world is generating more data than ever. More and more people want to get hold of that data. They want to peel the onion and find ways they can use it to make better decisions—about business, about health care, about energy and finance and education . . . the list goes on and on.

Businesspeople in particular have discovered how data can help them uncover insights, opportunities, and solutions. These folks are clamoring for more data and more ways to use it.

Today’s reporting tools really help them get to the heart of things. For example, Google Analytics is making it easier for less-technical people to find patterns and actionable insights. With this kind of reporting, organizations can see just how they are doing, and how they can do better. It’s a fun place to be.

What are your major challenges as a data management professional?

The big challenge is in collecting the right data. It has to be useful. You can be in an environment like Hadoop—you can collect all the data you want, but is it useful and valuable? Does it contain the information we need? Oftentimes, it takes creative thinking to figure out how seemingly useless data can in fact be very valuable.

After that, we need to be able to present the data and our findings in a way that makes a difference. That means creating visualizations to help us communicate clearly and persuasively to leadership and nontechnical audiences in language they can easily understand.

What kinds of organizations need data scientists?

Everybody needs data scientists! I really don’t think there’s an area that doesn’t.

Take Wisconsin, for example. We do a lot of farming here. Data analytics allows growers to track crop production from year to year and make decisions on what to plant the next season based on weather and moisture.

Wisconsin breweries use analytics to identify and predict sales patterns, inventory levels, and customer preferences.

Insurance companies like AmFam need data scientists to help them assess risk, improve pricing, create custom plans and services, and built customer relationships.

Again, everyone needs data scientists. Manufacturing, retail, energy, finance, medicine—everyone.

Who or what has influenced you most in your career?

Karen Lopez is a great educator and influence. She is a renowned data management expert who speaks and blogs about data quality, data governance, data modeling, compliance, and more.

Steve Hoberman is another influence. He is a prominent data modeler, author, and trainer who was awarded DAMA’s International Professional Achievement Award in 2012.

Tell us about your role in developing the UW Master of Science in Data Science program.

I was approached by George Kroeninger, assistant dean at UW-Extension’s Division of Continuing Education, Outreach and E-Learning. He and the other founding program members were interested in talking with professionals in the industry, and as DAMA’s president, I was one of those.

So I joined the team as an adviser. One of the best things we did was run focus groups made up of business leaders. We asked them, “What are you not getting from your new data recruits? What skill gaps do you need to fill?” We were surprised by their answers.

One of the biggest things they told us was their employees lacked soft skills. These folks knew how to hunker down and analyze data, but they didn’t talk to one another. And communication in data science is key.

Data modeling is another area they mentioned. This isn’t really taught in-depth at universities—it might be one chapter in a book. But it really is the foundation for everything. It is the blueprint that determines how all of an organization’s systems integrate and talk with each other.

I enjoyed helping with public relations. I have often been the program’s spokesperson for interviews. I will soon be a co-chair on the advisory board. It’s exciting.

What do you think makes the UW Master of Science in Data Science program unique?

The program is unique in the way it was designed. We didn’t just sit down and make up a curriculum. We talked with employers and business leaders to find out what they want and need in data professionals. Then we built a program from the ground up to address those needs.

What words of wisdom do you have for students entering this program and the field?

This is a great time to get into the world of big data. Employers have a critical need for skilled data professionals, and that need will only grow as organizations generate and collect more and more data.

Companies are figuring out that they can find competitive advantages in data; ideas and insights they can use to get ahead their rivals. There aren’t enough data professionals right now to fill the available positions and accomplish the things employers want and need to. So the demand is huge.

Employers need skilled people so badly that some are even changing organizational policies in an attempt to attract top talent. For example, some places are offering telecommuting options for new hires who don’t want to move across the country.

I think a lot of this is due to the fact that the field of data science is still immature. If you are good, you can find a job in Silicon Valley making $250,000 a year.

What changes or growth do you see in the field over the next five years?

I see more businesspeople continuing to get involved. Data collection, analysis, and visualization tools will mature, allowing more “regular” people to spot trends and uncover the insights they need to make better decisions.

The needs of the business will drive IT, and data scientists will be at the heart of it all.

Is there anything else you would like to share?

Data science is an exciting place to be. It’s a strong field and only getting stronger. Now is a great time to jump in!

One last question. What do you do for fun?

My kids keep me busy. I have three children (two boys and a girl) with my daughter still at home. She is a three-sport athlete. I follow her around to watch her play.