The demand for data science is growing far beyond supply. The gap between what companies need and the data science skills that are available in the market is a wide one, and it’s only continuing to grow. This makes developing data science work activities expensive and difficult because of the limited availability of resources.
All this puts a serious strain on organizations, particularly because data science is so important to business: it produces predictive models that help drive new revenue, improve customer service, and reduce operating costs. But the inability to gain access to data science resources and produce analytics outcomes in a timely fashion can cause significant frustration.
Putting Data Science in a Box
To address this issue, the Prolifics Innovation Center has been working on a solution we call Data Science in a Box. It’s a turn-key machine learning solution that is the right answer for all kinds of data science needs.
Data Science in a Box doesn’t replace data scientists, it works alongside them to improve what they can do. For organizations lacking data science workers, it brings those capabilities to the table and makes them easy for organizational services to consume. For organizations with data scientists already on the payroll, it helps them automatically evaluate thousands of algorithms in the time it would take to manually evaluate just a few.
Think of it as a force multiplier. There’s no need to sacrifice quality or accuracy for the sake of cost (or the other way around).
And it doesn’t stop there. The solution also incorporates model development, enhancement, and management over the long term. The data part of data science is ever-changing. Data Science in a Box helps keep up with these changes so that data models remain accurate and relevant.
Data Science: An Analytics Workhorse
So where can data science help? The answer is just about anywhere. The solution can benefit companies of any size, especially when those companies are dealing with large consumer populations that produce large volumes of data.
To start, we’re focusing on certain use cases where data science can excel.
Retail enterprises can leverage data science to better understand their customers. Predictive analytics models can help identify and attract potential customers, cross-sell products more effectively, and increase the amount of money that customers spend.
The solution can also help utility companies predict when equipment is in need of maintenance. Fixing equipment before it fails prevents outages and improves customer relations.
That’s the magic of data science. Wherever there’s a question (How can we make more customers buy a product? Which equipment is about to fail?), predictive analytics can help answer it.
About the Prolifics Innovation Center
The Prolifics Innovation Center leverages our decades of experience to build next-generation solutions like Data Science in a Box.
We strive to provide the leadership and vision that keep organizations competitive. Our experts work closely with our customers to incorporate feedback and tailor our solutions to real-world business needs.
If you would like to learn more about our Data Science in a Box solution or become part of our Innovation Center, please email us at firstname.lastname@example.org.
About the Authors
Michael L. Gonzales, Ph.D., is an active practitioner in the IT space with over 30 years of industry experience in roles of chief architect and senior solutions strategist. He specializes in the formulation of business analytics for competitive advantage in global organizations. Recent engagements include companies in the top global 100.
Dr. Gonzales holds a Ph.D. from the University of Texas with a concentration in Information and Decision Science. His research includes analytics against extremely large data sets and success factors for IT-enabled competitive advantage.
Wayne is Prolifics’ Senior Practice Manager for all things Analytics including: Data Visualization, Advanced Analytics, Natural Language Processing, Business Intelligence, Big Data Engineering and Data Science and Data Modeling.
He has been a dedicated professional with over 16 years of experience leading large IT projects through all phases of the systems development lifecycle from the preliminary business case through development to final implementation and steady state support.