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:

DEFINITION
First, we define the formal business problem to be analyzed or predicted and align project sponsors and team with the project objectives.

EVALUATION
Second, we conduct a thorough evaluation of the available data sources - both internal and external - for applicability to the model.

MODELING
Third, we apply cutting edge techniques to optimize and evaluate different predictive models to get to the best solution.

ITERATION
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.

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