In the last couple of years, the field of machine learning has become increasingly popular as data teams shift their focus toward predictive and prescriptive analytics. While the overall concept is still relatively nebulous, there’s a growing realization of the importance of using existing data to attempt to intelligently identify key indicators and predict future outcomes.
In order for machine learning to be successful, it’s more important than ever that the data used to make a prediction is clean. Bad or incomplete data can lead to bad predictions, which is detrimental to your organization and also detrimental to establishing a culture of data-based decisions. Luckily, the advanced languages that are now a part of the Sisense offering enable teams to clean data more efficiently and lay the groundwork for intelligent predictions.
Using R and Python for machine learning
SQL-only analysis platforms do not have the capability to build machine learning models. R and Python include many analysis capabilities that are needed to identify complex patterns in existing datasets. Data teams are building complex machine learning models that look at past data with a known outcome and apply algorithms that relate inputs to that known outcome. The same algorithm can then be used with new inputs to predict unknown futures as accurately as possible. In short, if data can be used to draw a line from the past to the present, the same line may be applicable when extended from the present into the future.
For example, consider a company’s lead scoring process. Data analysts can look backward to build a model that attempts to determine the value of specific lead characteristics and interactions between a company and its prospects. That model can then be tested to see if it holds true with new data. From there, they can adjust their strategy to focus on certain prospects and replace low-value interactions with high-value ones. The same model could even calculate how much new revenue those changes will generate. In this instance, machine learning can be used both as a predictive and a prescriptive technique.
The future of machine learning is wide open and limited only by imagination and the capabilities of your team’s data platform.
Machine learning in Sisense for Cloud Data Teams
If you look at machine learning as combining clean data with advanced regression analysis tools, R and Python enable Sisense customers to address both needs. Sisense for Cloud Data Teams allows users to easily create datasets from their database that they can use to train a machine learning model and then test against unknown data. Companies that have a data scientist who can build models using regression analysis can make machine learning models that are accessible to their entire team of analysts.
As the field of machine learning advances, the R and Python new and existing libraries will be updated to accommodate new capabilities. Teams that record reliable, clean data will be able to use that information in a variety of new ways to predict new things. These teams will increasingly add value to their organizations as machine learning becomes more concrete in the upcoming years.
To learn more about how your data team can use machine learning in Sisense for Cloud Data Teams, download our guide.
Chris Meier is a Manager of Analytics Engineering for Sisense and boasts 8 years in the data and analytics field, having worked at Ernst & Young and Soldsie. He’s passionate about building modern data stacks that unlock transformational insights for businesses.