Our Approaches

Critical decisions should be Data-driven.

The decisions that drive your organization are too important to be left to chance - or to the subjective opinions of “experts”.  Instead, we enable you to make decisions informed by objective data about past results, current conditions, and future expectations.  

We use real-world data and complex mathematical models to build analytic tools capable of objectively informing the critical decisions you confront each day.  When decisions are suported by unbiased evidence, the decision-making process is optimized, even in the face of uncertainty.

Small adjustments to the inputs might affect the outputs dramatically.
Understanding this prospectively is a game-changer.

We help you predict the relationships between the changes you make to your processes and the outcomes that result from these changes.  We do this by building advanced  mathematical models that use the past performance of your systems and the current structure of your data to facilitate prospective prediction of key outcomes.

We use techniques from failure analysis, risk modeling, survival analysis, and recommender systems to calculate the likelihood of specific outcomes.and then help you turn these data models into quantifiable results.  Predictive analytics is the core of what we do, and its what we do best.

Complex questions call for complex tools.

Just as important as asking the right questions are choosing the most appropriate modeling techniques.  

Not every situation is linear, nor do they always fall into a perfect bell curve. Your high-stakes challenges take place in the real world, where basic mathematical models might not apply.

We don’t look for easy answers. And we don’t force your data into simple solutions. We employ the most cutting-edge analytics models to answer your toughest questions.  From computational intelligence models, to forecasting, to decision support, we capture your system and apply the models that provide the best answers.

Nothing in life is certain, but understanding
uncertainty leads to better decisions

Anyone can make a prediction, but how confident can you be that the prediction is a solid basis for making critical operational decisions?

If you are calling in the data scientists, then the decision is too important to be left to chance . . . at least when the role of chance is unknown. The degree of certainty with which you proceed and the level of comfort you feel in implementing the results of your analysis should not be based on reputation, experience, or blind reassurance.  It should be based on calculated estimates of uncertainty. That's the only way we do it.

Expected values and probabilistic estimates are only half of the story. That's why we spend as much time thinking about distributions, deviation, variance, and stochastics as we do about mean values. You need all of this information if you are going to make a data-driven management decision, and you probably need some guidance on how to integrate these parameters into an informed decision-making process. We can help.

 Data science is about identifying questions,
not just about answering them.

Even experienced domain experts cannot possibly recognize all of the paths to value in their data. A major limitation of traditional approaches to data analysis is that you can only answer the questions that you already thought to ask. We can do better.

When we examine a data set, we look both at its information content and at its inherent structure. This approach often helps identify unexpected relationships between data elements that you never knew existed. The result can be new and unexpected strategies for extracting additional value from your data.

Whether is requires modern approaches to enhanced data visualization or complex methods for discovering relationships using graph theoretic or topologic approaches, we have the tools and the experience to help

The answers you seek start with the questions that we help formulate.

Locked within your databases are methods for making your operations more efficient…your processes faster…your service more consistent…your innovation cycle more precise.

Somewhere in those rows of numbers and columns of figures carefully collected over the years and securely stored on vast networks and servers are the answers you seek. You know the data hold value, but you don’t know how to access it.  

We do. We speak the language of data analysis. We also speak the language of business. Being fluent in both, we dig deep and probe your operations, business strategy, and markets to identify nuanced opportunities for improvement and competitive differentiation.  Then we formulate precise questions that are mathematically solvable, fiscally beneficial, and fully actionable.  

Going beyond static analysis means designing systems that learn.

For decisions that are made repeatedly and where data is constantly being generated, machine learning models will help you adapt to new information and evolving circumstances.

Computational intelligence models learn as data is analyzed, and they are specifically designed to help you improve the quality of your decision making process and the accuracy of your results.  These dynamic approaches to data analysis are the state of the art in modern analytics, and we have the experience to implement them within your data structure.

Real-world situations are almost never linear,
and actual data rarely conform to normal distributions.

Fortunately, nonlinear and nonparametric approaches are our specialty.

Early in the engagement we focus on the underlying structure of your data and the nature of the questions that you need to answer.  Complex questions require careful attention to detail and a non-constrained approach to data analysis.

We aren’t afraid to tackle tough questions using the most advanced mathematical models available.  Designing the right solutions means thinking outside the box - and that's the only way we think.