Signals Analytics, the next generation advanced analytics platform that leverages external data to uncover trends and predictive insights, announces a new rollout of its award-winning platform, leveraging breakthroughs in NLP, machine learning and other capabilities that enhance the precision, detail and utilization of advanced analytics in the enterprise. New features that span the entire data journey such as e-commerce product clustering, author, affiliation, and brand refinement, data mart integration and a daily alert allow businesses to seamlessly integrate Signals Analytics into their existing business intelligence technology stacks, while surfacing highly-detailed and predictive actionable intelligence across a broader range of use cases.
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While it is well-established that data-driven organizations are significantly more successful than their peers, many organizations express difficulty with actually standing up analytics and incorporating data-driven decisioning processes in their enterprises. The challenge becomes even more acute as the interest in connecting external data grows to capture consumer sentiment and a better understanding of the market landscape. The issue that is hardest to overcome is the intrinsic nature of these external data sources – they are generally unstructured, unconnected and made up of many different data types that can change often. The latest advancements announced today will allow companies to seamlessly combat these common obstacles so they can better leverage data and analytics in their business processes.
“There is a clear distinction between market leaders and laggards with their use of analytics, and at times like these the value of data and the utilization of data will only help to further separate winners from losers,” says renowned author and analytics expert, Tom Davenport. “While the benefit of analytics is well understood in theory, in practice, implementing analytics is very difficult and dealing with external data makes it even more challenging. Platforms like Signals Analytics put the end goal within reach in a practical, accessible way, helping to address many critical business questions across the enterprise.”
Recipient of the Frost and Sullivan 2020 North America Enabling Technology Leadership Award, Signals Analytics encompasses main components of a data fabric: the data collection layer, the data classification layer, and the data access layer that are all configurable to meet the business needs. More than 13,000 data sources get connected and harmonized by the analytic engine, which extracts context and sentiment and generates insights that are presented in more than 100 business-ready analytic models and apps. Because the insights generated by the platform are extremely accurate and contextualized, they help to shorten product development lifecycles, reduce the timelines associated with market intelligence and competitive positioning, and maximize the effectiveness of business decisions and marketing strategies, all of which brings brands closer to their customers, giving them first mover advantage in predicting and reacting to trends. With an impressive roster of marquee FMCG and pharmaceutical brands, Signals Analytics has already proven to deliver maximum returns on investment. The new capabilities take the platform to a new level of accuracy, scalability and insight.
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“From day one, our commitment has been to use AI to power the future of market intelligence and increase the business impact of analytics for the enterprise,” said Signals Analytics CEO and Co-Founder, Gil Sadeh. “What this means, given the volume of external data and how fast it changes, is a continued focus on connecting more and more data sources, pushing the envelope with NLP to extract granular and actionable insights, and enabling integrations with other business intelligence platforms. The capabilities we are announcing today, such as auto ML and product clustering for example, showcase the depth of our AI-focus, while the daily alerts demonstrate how our platform delivers strategic and tactical value to multiple stakeholders across the enterprise.”
New Advanced Analytics Capabilities
- New NLP and automatic machine learning (auto ML) engines improve data collection and classification and help address unique external data analytic challenges, such as product clustering, author, affiliation, and brand refinement.
Product clustering is a capability that identifies products that are the same but have different names across different e-tailers, preventing skewed analytic results. With greater than 90% recognition accuracy, organizations can configure this capability and tailor predictive analytics outcomes to align with their business. This means more prescient decision-making that lowers risk and maximizes business outcomes.
Author, affiliation and brand refinement capabilities are part of a set of new named entity recognition (NER) features that allow accurate representation models for precisely identifying authors, research institutions and FMCG brands within a large depository. The model compares the authors and brands of each new research paper, patent, or clinical trial, and determines if there is a match or if the representation model should be augmented.
- Expanded openness of the platform through the new Data Mart integration offers a direct connection to Signals Analytics’ connected and classified data sets for organizations to conduct analytics in their own business intelligence environments.
“With today’s announcement, we are answering a call from the market for more open platforms that increase the impact of analytics across the enterprise, with greater flexibility to support multiple use cases while reducing the reliance on in-house analytic, data science and machine learning expertise. Extending the connectivity and openness of the platform helps our customers move away from cumbersome analytics projects, while still fulfilling their need to be data-driven and precise in their decision-making process,” Sadeh concluded.
More information is available on the “Competing with Analytics and External Data” on-demand webinar, co-hosted by Tom Davenport and Gil Sadeh.
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