Alteryx, Inc., a provider of data blending and advanced analytics, and DataSift, which offers an enterprise social data platform, announced on March 19 a global partnership to provide an intuitive way for the millions of analysts in departments — such as marketing, merchandising and supply chain — to blend social data with other data sources for advanced analytics that deliver deeper insights.
The companies announced both product integration and a go-to-market partnership to deliver filtered and blended social data directly to line-of-business analysts.
“Social media data is critical to decision making — it isn’t just about sentiment analysis any more, it is about blending social insight with other relevant data to be able to drive more revenue,” said Paul Ross, vice president of product marketing at Alteryx. “With the integration of Alteryx and DataSift, we’re giving every analyst the ability to blend social data with all the other relevant data about their customers leading to real impact across organizations.”
“Social data is transforming decision-making across enterprises. Combining social data in aggregated form with other types of data unlocks a wealth of key insights about markets, segments and customers,” said Tim Barker, Chief Product Officer at DataSift. “The DataSift platform transforms massive unstructured social data into useable, structured data, enabling Alteryx to easily blend it with other enterprise data, perform advanced analytics, and turn data into action and a higher ROI.”
Alteryx analytics enables analysts to blend all of their relevant data, perform advanced analytics, and share the output with business users. Leveraging the expertise of DataSift to provide complete access to leading social networks and sources, such as Twitter, Tumblr, Facebook, WordPress, and LexisNexis, the analytics of blending social and enterprise data will enable deeper business insight across many organizations. This includes retail organizations that need to blend social data with loyalty program and Point of Sale (POS) data to determine the return on their social marketing activity.
Another key example includes telecommunication companies that are adopting ways to analyze social media data with location-specific signal strength data to better track and take action on weaknesses in their network.