The need for Big Data is a hot business topic, but the common reality is that companies are actually drowning in data. What they seek is knowledge. Applicable. Usable. Meaningful. Knowledge.
The reason for this is clear. While it is called data science, it is also art: It demands insight, experience, creativity and vision to produce winning solutions.
Ars Quanta provides data smarts for smart businesses.
We craft custom solutions that consistently deliver measurable results. Our seasoned, multi-disciplinary team delivers the big data, decision-science tools you need to power growth, improve efficiency or gain greater visibility
into your business.
Solving your most challenging/complex problems:
Ars Quanta applies advanced statistics, econometrics , AI and machine learning methodologies to create and optimize models of specific business activities or events, or “responses.” These models provide insights into the drivers of those responses and can be used to create predictive models of the same response . Some common problems include:
FORECASTING - SEGMENTATION - AFFINITY - RISK
Forecasting - companies forecast product demand, inventory levels, consumer activity, and many other things. With the right data and domain expertise, we can fit predictive models to these variables in order to generate a forecast to inform business decisions.
Segmentation - understanding the markets and potential consumers of a product is critical to effective marketing. Using data science and machine learning, we can identify the natural groupings of activity and consumer types to better understand your customers, product consumption, and inform marketing and product development strategy.
Affinity - as in measuring the activity of existing consumers of a product, affinity modeling is used to predict other market segments that are likely to have similar interests in a product or service.
Risk - often companies need to model less desirable responses so they can assess the risk and create mitigation strategies. Credit risk and probability of default modeling is a common example used to assess the risk of lending. Any recurring event can be modeled, given enough relevant data to inform a model, and used to assess the risk of that event occurring.
An Approach Built Just for You.
Ars Quanta applies advanced statistics, econometrics, AI and machine learning methodologies to internal and external data sets to find data driven solutions to specific business problems. This follows a four step process:
First, we define the formal business problem to be analyzed or predicted and align project sponsors and team with the project objectives.
Second, we conduct a thorough evaluation of the available data sources - both internal and external - for applicability to the model.
Third, we apply cutting edge techniques to optimize and evaluate different predictive models to get to the best solution.
Fourth, we employ an agile approach to projects, using iterative development approaches, and A/B test-and-learn methods.
A Compelling Mission. A Passionate Team.
We actively pursue projects that leave marketplaces, communities, customers and our clients in a better state. Our mission is to enable more optimal decisions based upon data and create social good by applying advanced Data Science, Analytics, and Quantitative Modeling techniques to a broader range of organizations that don’t otherwise have access to them.
Chief Data Scientist
John combines the technical skills of a Ph.D. statistician with the seasoned maturity from 20 years in industry. He began working in data science and marketing in 1999. He has delivered data-driven insights and value to scores of Fortune 500 companies. Prior to joining Ars Quanta as Chief Data Scientist, he was Principal Analyst at Atlas (a division of aQuantive), Research Director at Microsoft TV, and ran his own consulting business. He is a Clinical Professor of Marketing at the University of Montana's School of Business and teaches core classes in their new MS in Business Analytics.
Norman Sedgley, PhD is a full professor and chair of the economics department at Loyola University Maryland and a data scientist at Ars Quanta. Norman has 20 years of experience working in the areas of quantitative economics and econometrics both within academics and in a consulting capacity. As an active and engaged researcher Norman has completed many projects with AQ, received a number of awards in recognition of outstanding research and scholarship, contributed to several books and published over 20 peer reviewed articles in top academic journals.
Ben is our principal engineer at ArsQuanta. Prior to joining Ars Quanta, Ben has enjoyed 15 years of data excellence at AdRelevance, Nielsen, AQuantive, Microsoft and multiple start-ups and small companies. Small and large datasets alike, his work in business analytics, BI solutions, data warehousing, database/server administration and software development make him a significant value add to our engineering efforts.