Reference our Data & Analytics Crash Course guide below for information that may be helpful in identifying the skills necessary to solve your most pressing challenges.  Then, align those skills with our roster of analytics practitioners and begin your journey toward deeper insight, improved operations, and competitive advantage for your organization.


For many of today’s organizations, data is an invaluable resource, but unlocking the insight it can bring is often challenging due to the high volume and the many aspects of collecting, organizing, and activating it. Developing a sound data strategy can help your organization overcome these challenges while efficiently using your scarce resources.


Descriptive analytics describe

or summarize an organization’s existing data to gain under-standing of what is happening or what has already happened. The simplest form of analytics, they utilize data aggregation and mining techniques to provide insight. They are applied to existing data to make it more accessible to an organization's members, from investors and shareholders to marketing executives and operations managers.


Descriptive analytics can help identify an organization's strengths and weaknesses and provide insight into customers' behaviors. Strategies can then be devised and deployed in areas of targeted marketing and service improvement, albeit at a more basic level than with more complex diagnostic procedures. A common output of descriptive analysis is a report with various visual statistical aids.


Diagnostic analytics shift from the “what” of past and current events to the“how” and “why,” focusing on past performance to determine which factors may be influencing trends. They utilize techniques such as drill-down, data discovery, data mining, and correlation to identify potential root causes of events.


Diagnostic analytics use 

probabilities, likelihoods, and the distribution of outcomes to understand why events may occur and employ techniques including attribute importance, sensitivity analysis, and training algorithms for classification and regression. However, diagnostic analysis have 

limited ability to provide actionable insights, delivering correlation results as opposed to confirmed causation.

A common output of diagnostic analysis is an organization dashboard.


Predictive analytics forecast 

the possibility of future events by using statistical models and machine learning techniques. This type of analytics builds on the results of descriptive analytics to develop models that extrapolate likelihoods of select outcomes. Machine learning experts and data scientists perform predictive analysis using learning algo-rithms and statistical models, thereby enabling higher accuracy levels than with

business intelligence alone.


A common application of predictive analytics is sentiment analysis, in which text data is collected from sources (e.g., social media) to provide representation of customers' opinions. This data can be analyzed to predict sentiment towards a new subject (e.g., positive, neutral, negative). A common output of predictive analysis is a 

report to support complex

forecasts in sales & marketing.


Prescriptive analytics go beyond predictive analytics and are capable of not only suggesting favorable outcomes, but also recommending specific actions to deliver a desired result. Prescriptive analytics rely on a strong feedback system and iterative analysis to continually learn about the relationships between different actions and their outcomes.


A common use of prescriptive analytics is the creation of recommendation engines, which aim to match options to customers' needs. Effective prescriptive analysis utilize deep learning and complex neural networks, which simultaneously  micro-segment data across multiple parameters and timelines. A common output of prescriptive analysis is a focused recommendation for next-best actions relative to identified business goals.