Rob Ulmschneider, the new director of business intelligence solutions at Trilix, heads up the company’s newly launched Business Intelligence Practice.
His work involves helping clients see what data can offer in terms of growth, productivity and more, as well as build ongoing data strategies.
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He will also lead trainings and workshops throughout the region for companies looking to better plan and implement the use of data.
Ulmschneider previously worked in business intelligence roles at The Conference Exchange, an event-management software company in Cumberland, and at First Citizens Federal Credit Union in Fairhaven. He is a graduate of Anna Maria College in Paxton, Mass.
PBN: You assist clients with data strategy. Do you see many companies that don’t have solid strategy in place first but have data more for the sake of data?
ULMSCHNEIDER: It’s not so much that companies are compiling data aimlessly, but they are often just tackling one or two burning questions at a time. They embark on ad-hoc data projects that have a sole objective of answering a single question instead of building a data ecosystem that can quickly respond as new questions arise. When you start with a data strategy that aligns with your overall strategy, you’re looking at data holistically. It’s a proactive approach, rather than a reactive one.
PBN: Are there fundamental categories of data that should be collected and analyzed for every business, or does it vary depending on industry and company size?
ULMSCHNEIDER: There are some high-level measurables that nearly all organizations can track; everyone would love to reduce expenses, for example, and that’s easy to measure. Of course, other metrics will vary by industry. If you run a nonprofit, you might be more focused on community engagement, whereas a bank or credit union is going to pay more attention to profitability metrics [such as] return on assets. The biggest variable is overall strategy. Two companies of the same size competing in the same industry might have very different key performance indicators if one is focused on providing a lower-cost product and the other aims to deliver higher quality and value.
PBN: What is data visualization and storytelling?
ULMSCHNEIDER: Visualization is the graphical presentation of data. When we think of visualization, we think of charts, graphs and infographics. It involves synthesizing raw numbers from a sea of data into visual depictions that can be quickly understood by a wide audience.
Data storytelling takes this a step further – taking both visualizations and underlying raw data and using them to build narratives. Visualization and storytelling work together to turn data into information and information into insight, adding context and meaning to make data paint a meaningful picture.
PBN: In a recent workshop you facilitated, a featured topic was management of nontechnical infrastructure: people, process, policies and culture. Can you elaborate?
ULMSCHNEIDER: People tend to see data projects as technical initiatives – something that is delegated to IT [information technology] to implement. In fact, the technology is just one part of it, and you can’t add software solutions without looking at how people are going to use them.
“Nontechnical infrastructure” components are the elements of an organization that data impacts, aside from systems and applications. How are people using data in their day-to-day routines? How is it incorporated into processes and workflows, both formally and informally? What policies include considerations around data? And most importantly, is data a significant part of the organizational culture? When building a data strategy, we evaluate these elements, adding formality to existing components and evolving any that are lacking.
PBN: What is the most common misconception you come across regarding data analytics in the business world?
ULMSCHNEIDER: The classic iceberg visual applies to analytics. Most discussion centers around what people like and dislike in dashboarding and visualization tools, since that’s how most people interact with data. Below the surface, there are elements that are often neglected: Source-data quality, cleanup and transformation processes, documentation and culture, just to name a few examples. Companies need to give some consideration to these issues if they are going to be successful in implementing a data ecosystem.
Susan Shalhoub is a PBN contributing writer.
