Pecan Included in 451 Research Coverage on Predictive Analytics: Highlights

Lucas Stewart

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We’re excited to be included in 451 Research’s latest Market Insight coverage on predictive analytics, within their automated machine learning category. 451 Research is an NYC-based technology industry research firm, and we spoke to analyst Krishna Roy about all things predictive analytics and what, exactly, we’re on a mission to do here at Pecan. 

Here are some of our favorite highlights from the report, with excerpts:

1. Templates ground the predictive process in actual business objectives, and are the foundation for making predictions actionable

"Pecan's templates essentially enable the user to build models using drag-and-drop actions as well as SQL queries. They are for common business-oriented predictions including customer churn, customer life-time value and customer next-best offer... The templates are designed to foster an approach where users can take a business question and translate it into an insight that the business can use, rather than simply taking data and transforming it into a model, which might never get used and therefore won't be actionable."

2. Having business intelligence is a good indicator for whether companies are ready to get value from predictive analytics 

“As long as the data is BI-ready and can be used for analytics, Pecan can translate it into a machine learning model – providing the data or business analyst has created the associations between data sources first. The startup employs its own proprietary deep-learning algorithms to make the data AI-ready. Furthermore, it is worth noting that a core part of Pecan is data structuring by taking multiple tables and restructuring them into a single two-dimensional table automatically.”

3. Order of operations is critical for a company providing any type of automated machine learning

“We like the fact that has chosen to start marketing its offering in earnest once it has paying customers because it means the startup isn't operating on mere buzz alone but has solid real-world use cases to demonstrate its value.'s deep-learning knowledge also provides differentiation, which is important because AutoML is a vibrant but increasingly competitive sector.”

4. Predictive analytics not only grants data science superpowers to any analyst, it can free up precious data science resources for highly customized or unique projects

“Finally, it is worth pointing out that AutoML functionality has become an integral part of data science platforms in order to either free up data scientists' time so they can focus on the higher and more complex aspects of their job and/or enable non-experts to get ML-driven insights.”

You can read the full report here with a 451 Research membership.

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