Pecan AI, which is developing a predictive analytics platform for enterprises, raises $66 million


“Big data analytics” may be the currency of the pandemic. The term describes the process of uncovering trends, patterns and correlations in large amounts of data to help make decisions. Big data analytics is being used by a growing number of organizations to anticipate trends across industries — according to according to a 2020 NewVantage Partners survey, 64.2% of companies claim they use data to drive innovation.

The promised benefits of big data analytics include faster innovation cycles, improved business efficiency, increased productivity and more effective R&D. Netflix reportedly uses big data saves $1 billion a year in customer retention. But implementing big data infrastructure within an organization often poses challenges, including poor data quality and a lack of expertise.

With the big data analytics market set to grow to a staggering $100 billion by 2027, vendors — looking to get in on the ground floor — are offering new services designed to abstract the complexities of working with big data. Noooga, Imply and Unsupervised are among the companies offering predictive modeling, anomaly detection and other tools that ingest and process large amounts of data with AI to identify insights. pecan nut AI also falls into this category – the company is working with clients to automate data cleansing and engineering to create machine learning-based predictive algorithms. Marking its second round in the past 12 months, Pecan announced today that it has raised $66 million in a Series C round led by Insight Partners that includes S-Capital, GGV, Dell Technologies Capital, Mindset Ventures and Vintage Investment partners. total capital raised to $117.5 million.

Predictive Analytics

Tel Aviv, Israel-based Pecan was founded in 2016 by Noam Brezis and Zohar Bronfman. Bronfman holds a PhD in history and philosophy of science and technology and computational cognitive neuroscience from Tel Aviv University. Prior to joining Pecan, Brezis was a senior data consultant at Madiera Data Solutions, a company that has worked with clients including Intel on data-centric products and services.

Pecan allows users to connect to various databases and use a drag-and-drop, no-code interface to create machine learning data sets. The platform can enrich the datasets with external data and automatically perform feature engineering, the process of selecting the most relevant variables from data to represent a business problem in a statistical model.

“The scarcity of highly skilled analytical talent is a challenge that every industry faces. Each year we generate significantly more data than the year before — but data isn’t useful on its own without the right tools to analyze and visualize it,” Bronfman told VentureBeat via email. “Data science can have a major business impact, but must be implemented by skilled data scientists and data engineers. [M]Most companies do not have enough data science talent and resources to analyze and optimize their business performance. Pecan helps companies leverage data science without data scientists knowing.”

Pecan.ai’s platform creates a model that predicts important trends.

Pecan trains and optimizes models over time, prioritizes features as they change in importance, and displays the changes in a live monitoring dashboard. As the company explains on its website, “Pecan builds a myriad of deep neural networks to suit the nature of your data, the size, and the predictions you need. After a…series of recursive competitions between multiple networks, there is only one fully trained neural network across – refined for optimal performance and accuracy.

The idea is to enable analysts and business stakeholders to gain actionable insights and show results within days of adding their raw data. Pecan supports use cases such as demand forecasting, conversion, lifetime value, next best offer, VIP customers, upsell and cross-sell, churn and retention, and sales analytics.

“Because Pecan focuses on data analysts, our AI automation platform is use-case focused, significantly reducing the complexity and statistical knowledge required of its users,” added Bronfman. “Unlike some of our competitors, Pecan handles everything from data preparation to modeling and production monitoring with a drag-and-drop and low-code SQL-based user interface. The data preparation and job selection/engineering components are critical. Getting data into shape for AI models can take weeks or months when working with data scientists and data engineers, but with some help from the Pecan team and from our platform, this can be automated with minimal effort.”

Implementation challenges

While big data analytics has its advantages, enterprises often encounter hurdles in adopting it. As Harvard Business Review (HBR) points out, predictive analytics is particularly easy to go wrong, especially if companies don’t prepare their workforces to manage the machine learning integrations and jump into modeling before determining a path to operational implementation.

“Predictive analytics isn’t a technology you just buy and plug in. It’s an organizational paradigm that needs to bridge the gap between quantitative and business culture through a collaborative process led jointly by strategic, operational and analytical stakeholders,” writes Eric Siegel of HBR. .

Pecan, for its part, who claims annual recurring revenue has tripled in the past year, points to success stories such as Johnson & Johnson, which used its platform during the pandemic to help predict consumer behavior and buying patterns and supply chain resistance.

“Pecan has dozens of customers in the mid-market and enterprise segments, including fintech, insurance, retail, consumer packaged goods, mobile apps and consumer services. The finance [from this latest round] will be used to expand our operations in Israel and the US,” Bronfman said. “We expect to double our workforce in the next 12 months.”

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