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Predictive Analytics & Data Mining

Predictive Analytics & Data Mining

Today’s business managers face greater complexities than ever when it comes to making business decisions. For every business decision, there are a number of factors that impact the associated risks. Through the use of statistics, predictive analytics, and data mining, Kestrel is increasingly helping companies take the “gut feel” out of making important and often complex business decisions.

Statistical Techniques

Most people are familiar with common descriptive statistical techniques, like measures of central tendency (e.g., mean, median, mode) or variability (e.g., interquartile range, standard deviation). Through inferential statistics, Kestrel’s experts are able to take things a step further, using probability-based analytical methods (e.g., t-tests, chi square goodness-of-fit test, one- or two-way ANOVAs, Pearson correlation) to draw conclusions about a population based on observations of a sample.

Beyond inferential statistics, more advanced data mining and predictive analytical techniques are increasingly being used to explore and investigate past performance to gain insight for future business decision making—a practice that Kestrel has applied to many projects to alleviate future business risks.

Data-Driven Decisions

Data mining draws on large amounts of data to identify patterns, which are often classified as opportunities or risks. Predictive analytics encompasses a variety of statistical techniques (e.g., discriminant analysis, linear regression, logistic regression, decision trees, and neural networks) that are used to analyze historical data to predict the most probable future events.

The versatility of predictive analytics, combined with the variety of statistical techniques available, can be applied to help companies analyze a wide variety of problems and gain insight for future business decision making.

This approach allows Kestrel to synthesize information to help decision makers to predict the outcome(s) of a decision before it is made—and make smarter decisions based on data instead of gut feelings.

Analytical Expertise

Kestrel has experience helping companies map out and improve the interconnected set of processes, activities, and tasks that allow businesses to perform effectively, reduce their risks, and manage their organization responsibly.

To support this, Kestrel’s team includes experts in statistics and predictive analytics, who are:

  • Highly skilled in conducting research and assessments, analyzing data, and compiling reports that interpret assessment results
  • Well-versed in using statistical analysis to evaluate the significance of research results
  • Adept at using t-tests, ANOVA, chi square, correlation coefficients, and measures of effect size to determine whether a particular treatment leads to significantly different outcomes
  • Experienced at mining and analyzing data to identify patterns and trends that impact business performance