Sinequa’s Cognitive Search & Analytics Platform Receives an Award from BigData Insider

Last night, Sinequa participated in the Readers’ Choice BigData Insider Award Gala in Augsburg, Germany. From April 19 to August 31, 2016, the readers nominated their IT Vendor of the Year across six portals: BigData insider, cloud computing Insider, Datacenter Insider, IP Insider, Security insiders and Storage insiders. In total, more than 34,000 readers voted for their favorite solutions.

As a result of the vote, Sinequa’s Cognitive Search & Analytics platform won the Silver Award in the “Big Data Management & System Tools” category. In the same category, Talend and SAS received respectively the Platinum Award and the Gold Award.

Big Data Insider Award 2016

“We are honored to receive this distinction resulting from the vote of the readers of BigData Insider comprised of customers and partners. This is a great recognition for Sinequa’s growing momentum in the DACH region,” said Laurent Fanichet, Vice President, Marketing at Sinequa.

Sinequa @ BigData-Insider-Awards-2016

Bild: Dominik Sauer / VIT
From left to right: Matthias Hintenaus, Sinequa, Andreas Gödde, SAS and Harald Weimer Talend.

 

 

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Uncovering Business Insights Through Cognitive Search

Uncovering-Business-Insights-Through-Cognitive-Search-Sinequa

Big Data. It’s among the most pressing challenges — and opportunities — for today’s solution providers. Enterprise data, be it structured in databases and enterprise applications or unstructured textual data from documents (including contracts, letters, emails, news-feeds, websites, and more) or videos and images, contains a wealth of content that, if searched and analyzed with cognitive intelligence, can deliver valuable insights for the customers you serve.

It’s common today to have numerous silos, both on premise and in the cloud, of content in which critical data resides. From customer records and contracts to financial data and emails, data silos often take many different shapes and forms without the ability to “talk” to one another. If only a 360 degree view of this data were available at the employees’ finger tips. This could provide deeper customer insight, increased sales opportunities, and greater customer loyalty with the ability to meet rapidly evolving customer expectations.

With cognitive search and analytics, this goal can be achieved. Leveraging Machine Learning algorithms and advanced natural language processing (NLP), cognitive search and analytics solutions enable customers to embark on ambitious Big Data projects with the opportunity to extract relevant information from the volumes of content they retain.

In fact, some search and analytics solutions even offer as many as 150 smart connectors, out of the box that can seamlessly connect to multiple sources of data. This works to integrate your customers’ industry specific dictionaries allowing the information to be indexed, putting their specific knowledge under the hood of one platform — making it an intelligent partner for anyone searching for relevant information for his/her subject.

To efficiently leverage Big Data for your customers, consider an advanced search and analytics platform that delivers these five critical elements.

  1. Cognitive search with a combination of indexing, natural language processing and machine learning. For a search and analytics solution to be effective, it needs to understand the natural language as it’s spoken across ever major language. This will help to deal with unstructured content such as email and document files. It should also leverage machine learning algorithms so that it can learn as it progresses, delivering more value and insight with each new volume it analyzes. This is what one analyst firm defines as Cognitive Search which allows organizations to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.
  2. Extensive connections for comprehensive indexing. To make the most of the multiple silos of data throughout the organization, a search and analytics solution needs to have a wide range of connectors so that it can support every type of data, making it easily ingested into the platform so that it’s included in a comprehensive analysis. Building connectors before starting projects will delay value extraction and make projects more expensive. From databases and enterprise applications including CRM and ERP systems such as SAP, to big data Hadoop environments, cloud applications like Office 365, GoogleApps and Salesforce, and cloud storage such as Box and Microsoft OneDrive, having a connector for every vital application in the business will ensure that the resulting insights deliver a complete view into the business.
  3. Support for the structured and unstructured. When analyzing business data, it’s critical to include unstructured data, such as email and document files, as well as structured content, like the data included in databases. Only when both are included in an analysis can true insights be revealed. Since so much valuable data is embedded in unstructured files, evaluating these contents can produce truly insightful information into the business that can’t otherwise be recognized by evaluating structured forms of content.
  4. Extensive security and access control. Today’s data is not only a critical asset, it’s also private and stringently regulated. Any solution that touches regulated data must follow strict security and compliance guidelines, ensuring that policy controls are in place. Be sure to select a search and analytics platform that supports stringent access controls, including user authentication, cross-domain security and secure communications, to assure that compliance practices are followed.
  5. Agility to support hybrid infrastructure. The cloud is quickly changing everything. When large data sets are in play, it’s very likely that much of that data is being retained in cloud-based environments. Whether retained in public cloud solutions, such as Amazon Web Services (AWS), or private cloud architectures, data still must be accessed and integrated into a comprehensive enterprise search and analytics solution to be part of a successful solution for true business insights. Here it’s critical to select a solution that will not only support any combination of a private and public cloud infrastructure as well as on-premises architectures with a hybrid approach to data analysis that will also support hundreds of millions of documents and billions of database records. This will ensure that regardless of how large the environment becomes, and wherever data may reside, it can become part of a comprehensive analysis for true and accurate results.

Big data presents a wealth of opportunity for you and your customers. By taking a holistic approach to cognitive search and analytics so that every silo of data is included in an enterprise search activity, the insights can be exceptionally revealing. These results can not only increase customer opportunities, grow sales and improve overall organizational productivity, they’ll also help you build the customer loyalty that will pay off for years to come.

 

 

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5 Ways Finance & Insurance Organizations Take Advantage of Cognitive Search and Analytics

Sinequa-Cognitive-Search-Analytics-Finance-Insurance

Leading banks, financial institutions, and insurance companies are known to be data-intensive organizations and depend more than ever on data to make well founded decisions. They also rely on data to extract insights on customers that can result in increasing revenue streams. In order to address these challenges they need to be agile, innovative and responsive to evolving customer needs. Find here five ways Financial organizations leverage their big data using cognitive search and powerful analytics:

  1. Get Actionable insight from the most diverse data sources: the objective is to analyze, structure and categorize all available data to get intuitive and unified information access across all internal and external data sources, including customer contracts, insurance claims, payment history, email communications, CRM data, company policies and processes and more. Employees must be able to access relevant information without having to know where data is stored, in which format or how to access it.
  2. Obtain instant 360° views of customers, portfolios, investment targets, contracts, financial performance, and any other subject linked to the business of an organization. People can do so across all business units – from banking to insurance, leasing, property management, asset management, and beyond. Only an efficient “insight engine” – as some leading analysts call cognitive search and analytics platforms – can provide rapid 360° views to users without the need to change existing applications.
  3. Detect fraudulent activities & prevent money laundering: banks and insurance companies face the daunting task to accurately and rapidly identify fraud by analyzing Big Data volumes. To face this challenge, a cognitive insight platform enables the detection of “unusual” data patterns by predictive machine learning algorithms and the mapping of relationships between people, bank accounts, credit card numbers, financial transactions, and many other data types. To uncover patterns in behavior, analysts use a combination of interactive charts, timeline analyses, tables and relationship maps.
  4. Reduce customer churn: the combination of cognitive search and powerful analytics help organizations improve customer retention. Here, Natural Language Processing with text mining agents plays a major role in detecting relevant information in customers’ data and behavior, for example by analyzing information requests and navigation patterns on the company’s website. Predictive Analysis also plays a role in reducing churn rates. For example, machine learning algorithms help detecting patterns and trends in customers’ transactions which can identify them as “high-risk” potential defectors.  Companies can propose tempting offers to potential churners that prove usually quite effective in retaining them. This also reflects in staggering yearly ROI figures, up to tens of millions of dollars.
  5. Recommend up-sell and cross-sell offers: Once customer data is collected and analyzed across all available channels, additional functionalities can be added with marginal effort. Machine learning algorithms, such as “collective filtering and recommendation”, can then be used to optimize marketing campaigns, improve up-selling and cross-selling. Indeed, on top of the 360° view of customers, we can use machine learning algorithms to recommend products and/or services that are relevant to customers, based on deep analytics of contents and customers’ behavior data.

In the fast-evolving world of Finance & Insurance, it becomes increasingly important for these organizations to capture, process and analyze massive amounts of structured and unstructured to make better business decisions while better serving their customers. A Cognitive Search & Analytics platform that delivers superior agility, flexibility and scalability and turns data into business insight can bring significant value.

Interested to learn more about this platform for your organization? We’d love to help.

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