Data management & analytics software outfit Lavastorm have announced extensive updates to their Analytics Engine platform. The enhanced engine will allow business analysts make significant insights using predictive analytics, even with a limited knowledge of complex data science.
“Demand for advanced analytic capabilities from companies across the globe is growing exponentially, but data scientists or those with specialized backgrounds around predictive analytics are in short supply,” notes Drew Rockwell, CEO of Lavastorm.
In an announcement made earlier last week, Lavastorm points out that lack skills for working in programming environments like R, along with the lack of ease in business intelligence tools to enable self-service data assembly for business analysts to marry rich data sets with their essential business knowledge and the wanting transparency between business analysts, IT and data scientists are the issues faced by business analysts.
“Business analysts have a wealth of valuable data and valuable business knowledge, and with the Lavastorm Analytic Engine, are perfectly positioned to move beyond their current expertise in descriptive analytics to focus on the future, predicting what will happen, helping their companies compete and win on analytics,” Mr. Rockwell further explained.
New Functionalities added to the engine for Business Analyst are:
- Linear Regression: Calculate a line of best fit to estimate the values of a variable of interest
- Logistic Regression: Calculate probabilities of binary outcomes
- K-Means Clustering: Form a user-specified number of clusters out of data sets based on user-defined criteria
- Hierarchical Clustering: Form a user-specified number of clusters out of data sets by using an iterative process of cluster merging
- Decision Tree: Predict outcomes by identifying patterns from an existing data set
- The latest offering is available since the announcement and Lavastorm will be providing webinars and other educational content to facilitate the adoption of this new functionality.
(Image credit: Greg Bishop, via Flickr)
