January 31, 2024
AE Business Solutions is proud to support technologies and organizations that work to further the technology landscape and provide thought leadership, and that is why we have chosen to once again sponsor the UW Data Science Research Bazaar. As sponsors we were invited to share a bit about ourselves and our thoughts on the data science landscape in a guest blog. Read an excerpt from that blog below, and find the full version by clicking here.
As a consulting firm, we work with a wide range of industries and see many different applications of data science. The biggest impact we see is organizations adopting and integrating predictive models into their daily operations and decision making. The demand for predictive models is greater than ever, and our challenge is in helping organizations to develop and support them. This demand has led organizations to adopt common use cases, such as predicting sales or member attrition. Industry verticals ranging from public education to large-scale manufacturing can all benefit form predicting operational outcomes or understanding preferences about their constituents through data science.
An equally important aspect of adopting predictive models is an improved data environment that must be built to support them. In addition to more common use cases, organizations have also developed more bespoke solutions, where we see applications of algorithms which create unique business opportunities and new revenue streams. To support a diverse set of models, data teams are investing resources in communicating and operationalizing these machine learning systems. We see a large migration to cloud (ass opposed to on-premises) platforms. Cloud platforms can provide compute power, continuous integration, and storage capabilities which promote a robust data science environment.
Beyond the accompanying infrastructure, implementing predictive models requires thorough understanding between data teams and business users. Data science is a new and intricate discipline, and business users have an increased responsibility to communicate business needs to data team members. Likewise, data teams must be prepared to communicate their ideas and results to business users in an effective manner. In our experience, we take on a didactic role as data scientists, explaining different modelling techniques and their limitations. This has allowed us to bridge the gap between business knowledge and data science within may organizations.
Ultimately, we see data science shaping our partner organizations into more capable versions of themselves, armed with new methods for learning form data and robust data systems to take on new challenges.