Our Sharp Eye on the Big Picture
Our solutions find actionable patterns in large datasets that help clients make informed decisions. Bigger datasets pose opportunities and risks, requiring an analytic engine that is powerful as well as agile. Our success in data mining engagements is founded on a number of key characteristics:
- Hypothesis-Driven and Systematic: Our deep knowledge of client industries means we know where to look for key insights in large masses of data, enabling us to avoid lengthy and unprofitable “fishing expeditions.”
- Integrative and Adept: Our expertise in database structure and management enables us to expand the scope of data mining efforts through transformations and combinations of diverse information, bringing to light important patterns that otherwise could not be clearly discerned in the raw data.
- Comprehensive and Analytically Flexible: We can draw on, and customize, a full portfolio of data mining methods to best meet client objectives. These methods include classification and regression trees, discriminant analysis, logistic regression, random forests, support vector machines, and neural networks for supervised learning problems (e.g., flag customers at risk for defection) and a diverse range of clustering techniques to support unsupervised learning (e.g., identify attractive market segments).
Ever larger databases are notorious for containing spurious correlations that can overshadow meaningful patterns. The rise of Big Data means that businesses will make more decisions faster than they ever have before. With decades of experience in rigorous model evaluation, our data scientists ensure that clients realize the promise of larger datasets while avoiding the costly pitfalls.