Sinequa’s Insight Engine Helps Atos Differentiate by Providing Intelligent Digital Workplace Capabilities

A big congrats to our longtime strategic partner Atos who was named a leader in the Magic Quadrant for Managed Workplace Services (MWS). Gartner calls out Sinequa as a key supporting technology.


Gartner’s Magic Quadrant for MWS, North America, recognizes leaders in enabling sourcing and vendor management leaders to select the right partner in the rapidly changing market, which focuses on using MWS to increase staff engagement, drive productivity and enable digital benefits.

As a recognized global leader in digital transformation, Atos provides an end-to-end solution to transform the employee experience. By combining skills tightly, from advisory to consulting and design thinking through to business and vertical solutions, including applications to the digital workplace platform, Atos has the skills in place to offer a complete solution to our joint customers to deliver an end-to-end workspace transformation. In terms of Cognitive technologies, Atos differentiates itself by integrating Sinequa’s insights engine. The partnership brings together Sinequa’s cognitive search & analytics platform and Atos’s business consulting and IT services expertise  to change the way people access applications, data and help, improving end user productivity and user experiences, whilst reducing cost and ensuring security and compliance.

We are excited to be working with a leading system integrator recognized for setting the tone in the digital workplace space and can’t wait to see where our partnership takes us in the future.

People at these digital workplaces need information, not just data. While information must often be comprehensive to be valuable –  like in a 360° view of a customer – it must also be relevant. People have no time to sift through tons of information to get to the insights that guide their actions. To help organizations sift through the abundance of information, data coverage must be total, and the delivery of insight must be intelligent and selective. This delivery of information must also match the expectations of today’s digital worker, who wants answers in seconds rather than hours or even minutes.

In this new generation of the digital workforce, there are certain tips that address the challenges of catering to this always connected society, including being proactive in delivering information and tackling unstructured data.



+1Share on LinkedInShare on Twitter

GDPR Investments for Compliance AND for Competitiveness

This article was originally published in Database Trends & Applications.

The deadline looms on the horizon. On May 25, 2018, the European Union will enact some of the most stringent data privacy regulations the world has ever seen. These regulations will impact thousands of companies around the world, not only EU-based organizations but any company that collects or processes personal data on EU residents. The General Data Protection Regulation (GDPR) recognizes the “fundamental right” of people to control what data is stored about them and how it is used.

GDPR Investments for Compliance AND for Competitiveness

Organizations must be ready for this date since the fines for non-compliance could be as high as 4% of annual revenue or $21 million, whichever is higher. To put this in perspective, small companies could go out of business with a $21 million fine, and for a company with revenue of $10 billion, the fine could be a staggering $400 million.

No organization with large datasets can sift through them manually to find personal data and judge its GDPR compliance. Companies need sophisticated technology to deal with their data effectively, enabling them to search, discover, and review. Most organizations find it challenging to quickly and accurately identify and find personal data.

Under GDPR guidelines, people can request to be informed about the data that organizations store about them and can demand rectification, erasure, or the restriction of how their data is used. They can also ask to receive their personal data in a common format that allows them to transfer it to another organization.

The impending deadline and the fear of painful fines put organizations under a great deal of pressure, such that they may forget about pursuing the potential business benefits of conformity measures. For example, the prospect of thousands or even millions of people demanding to know what data is stored about them may seem daunting. Since an organization is obliged to answer within 30 days, this might result in thousands of cases per day being handled by customer service.

On the other hand, many large enterprises with millions of individual customers—banks, wireless providers, etc.—need to provide a 360-degree view of a customer to their sales and service personnel—in seconds, not in a month. This is a business requirement independent of GDPR compliance. When customers contact the company, they expect the sales or service reps to know them and give them knowledgeable recommendations and advice.

One way of providing such a 360-degree customer view is using cognitive technologies that can ingest structured data from enterprise applications such as CRM and billing and unstructured data such as emails and other correspondence. Companies often have hundreds of such data sources. Cognitive capabilities, such as natural language processing and machine learning, are necessary to extract relevant information from structured and unstructured data: what kinds of contracts the organization has with customers; service and payment history; whether the latest exchanges were friendly or aggressive; suggestions from past experience with other customers to help solve the current customer’s problems; etc.

In a call center, operators need to get a complete picture of the person on the line within less than 2 seconds, according to industry standards. If a company has 20 million customers, more than 200 enterprise applications with customer data, and 10,000 call center agents, that is a daunting challenge—but a challenge that has been successfully overcome by companies.

Gartner estimates that European companies will each spend an average of 1.3 million euros to comply with GDPR personal data protection requirements while U.S. businesses are setting aside at least $1 million for GDPR readiness, with some assigning up to $10 million. What do they get for it, apart from avoiding fines?

Let us look at a concrete example of a wireless telecom company that implemented a 360-degree view strategy using cognitive technologies. The first objective of the project was reduction of average call handling time, increased customer satisfaction and loyalty, and increased up- and cross-selling. All these goals have been achieved, but there is another aspect to the project that offered massive savings: Call center employees now have a unique and intuitive user interface to access customer data.

They no longer need to understand some 30 enterprise applications they had to navigate before to access this data. This reduces the need for training from 30 days to 1 day. With 10,000 employees and a turnover rate that often approaches 50%, that means 5,000 x 29 workdays saved per year, i.e., 145,000 workdays or 29,000 person-weeks. ?The company can certainly offer a lot of customer service during that time! The overall ROI of the project would be approximately 60 million euros over ?3 years.

One of the 10 biggest banks in the world has implemented a similar project to provide a 360-degree view of customers to its customer-facing employees. Its objective from the outset was also to provide their customers a 360-degree view of their own dealings with the bank: accounts, share deposits, insurance contracts, etc. It is easy to extend this interface to answer the question, “What data does the company have on me?” In this way, the company improves its service to customers and fulfills its GDPR obligations without a single employee being involved.

GDPR is coming, but instead of seeing it only as a costly burden, organizations should view the regulation as an opportunity. By implementing advanced cognitive technologies to derive deep customer insights, organizations can ensure compliance while reaping the business benefits of greatly improved customer service that can have a tremendous impact on the bottom line.

+1Share on LinkedInShare on Twitter

Streamline Global Manufacturing with the Information Driven Supply Chain

This article was originally published in Manufacturing Business Technology.

A new kind of manufacturing company is emerging that leverages big data and analytics for a unified view of the supply chain. This new approach provides supply chain insights that enable these organizations to respond quickly and decisively to changing conditions despite geographically dispersed suppliers and customers. And yet at the same time, they can also pursue long-term opportunities by identifying products, parts and components across all the data sources where supply and demand spans states, countries and continents.

No matter the supply chain model, customers expect quality service, on-time delivery and the right product every time, which can be challenging if an organization manages erratic supply and demand on a global basis.

For most organizations, products consist of numerous parts that move through the enterprise and its network of suppliers, creating a need for parts logistics. Every part number within the organization takes on a life of its own and every department must have access to all the information surrounding it.

As organizations build new products, and service existing ones, they need cohesive and comprehensive visibility for a unified view of the entire supply chain.  This approach helps organizations optimize their supply chain and increase responsiveness by focusing on achieving greater visibility into products and parts inventory. Organizations that focus on these objectives can tighten the gaps in their supply chain and enhance their overall operations.

Supply Chain Unification

A unified view of the supply chain connects the enterprise and suppliers seamlessly to various applications and databases—such as enterprise resource planning, a data warehouse and customer relationship management systems.

This connected environment helps organizations keep abreast of the manufacturing process and supply chain management, and share relevant information across design, engineering, procurement, quality control and more. From understanding customer needs to building requirements, product prototyping and selling products, everything is streamlined and simplified across disparate systems.

By adopting a unified view of the supply chain, organizations can see what parts are in stock, which suppliers they re-order from and if those suppliers have available inventory. This gives engineers visibility into the specifications of components, the mean times between failures
for components, discontinuation plans and recent negative reports. It also promotes accurate shipping expectations and on-time delivery, while connecting all departments and partners in the supply chain into one efficient manufacturing shop.

Finding the right part information when and where needed

An information-driven supply chain makes it easier for workers to search and locate specific parts for production. Workers can create alerts to be notified when relevant information surfaces. Empowered and informed workers can then concentrate on manufacturing products on schedule.

A unified view of the supply chain helps engineers know who has previously worked with each part and learn from their experiences. If a component is found faulty during production, engineers could spend days trying to find who completed the original design. A unified view of the supply chain helps pinpoint the most knowledgeable workers and provides immediate access to information about the component and its design specifications. By empowering engineers, organizations are better able to meet customer demands.

This approach also empowers sales with information about specific parts to understand when to sell a specific version, and to know who to talk to if they need more information. Customers then get a confident, knowledgeable sales associate to help them make the right decision.

Knowing how and where to get parts in a hurry

Organizations must be able to respond immediately to customers who need replacement parts and immediate service. If a part is not available, they must know expected shipment dates, transit times and who can supply it. This is increasingly challenging with globally distributed suppliers and a dispersed customer base.

A unified view of the supply chain can resolve this issue by giving customer service representatives visibility into all parts across the enterprise, regardless of location, repository or format in which the information is stored. It can also extend access to information from supplier sites and applications.

To assist customers with support requests, customer service representatives need to be aware of past problems and how to identify and resolve them. With a unified view of the supply chain, they immediately know the parts associated with a problem and how it can be fixed.

In the final analysis, managing the supply chain is about information access. Although many applications are necessary to manage information at different stages of the supply chain, a unified view provides cohesive visibility across all applications that manage information about products, suppliers and customers. It is a critical part of streamlining and optimizing the use of an organization’s supply chain.

+1Share on LinkedInShare on Twitter

Cognitive Search & Analytics Transforms The Enterprise From Data Driven to Information Driven

**This article was originally published on AI Business magazine**Sinequa-AI-for-Business

The quest for actionable insights and answers from within vast troves of data is neverending within the modern enterprise. There’s good reason for that – it is the end goal of all information work – but the process is anything but optimized. Global analytics firm Forrester revealed as much in a 2017 report, which found that more than 54% of global information workers are interrupted from their work a few times or more per month by time wasted trying to gain access to information, insights, and answers.

It’s a problem that goes far beyond the limitations of conventional enterprise search technology – it’s a Sisyphean challenge, thanks to the sheer volume of data being created every single second.

“As organizations in data-intensive industries strive to create value, enhance customer experiences, and differentiate themselves from their competition, they are placing demands on their knowledge workers in unprecedented ways,” explains Laurent Fanichet, VP of Marketing for Sinequa. “Frequently, the data and knowledge they are looking for is isolated, segmented, and fractured. It’s difficult to surface the right information at the right time to see the patterns in the data.”

Fanichet has a clear grasp on the key problem Sinequa, an independent software vendor specialising in cognitive search & analytics, is trying to address. In its recent report, Forrester Wave: Cognitive Search and Knowledge Discovery Solutions, (Q2 2017), Forrester defines cognitive search as ‘the new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content’ – and, in the same report, go on to highlight Sinequa for the applications of their NLP technology in enterprise search.

The kind of cognitive search and analytics platform Sinequa offers, Fanichet explains, refers to an information system that is capable of automatically extracting relevant insights from diverse enterprise datasets for users within a specific work context. “Cognitive search brings the power of AI to enterprise search,” he says. “It helps organizations in data-intensive industries to become information driven.”

A recent IBM Watson report highlights the applications of cognitive search in the aerospace sector. One company uses these enhanced search capabilities “to improve supply chain visibility and reduce cycle time, saving millions of dollars on critical parts deliveries.” Furthermore, the system enables aircraft technicians to search through “reams” of maintenance records and technical documentation. “Now, if a worker needs to know what’s causing high hydraulic oil temperatures, the [cognitive solution] identifies historical cases with similar circumstances, finding patterns that point to the root cause of the overheating.” The report goes on to note that the solution in question saves the airline manufacturer up to $36 million per year.

Cognitive search and analytics likewise has its applications in the health and pharma sector. AI Business recently spoke to Karenann Terrell, GlaxoSmithKline’s first ever Chief Data and Analytics Officer, and former CIO of Walmart. She explained that a big component of what it takes to develop medicine can benefit from next-generation computing and machine learning. “Approximately 1/3 of the total cost of developing a medicine (>$2.5bn) is spent during the time it takes from identifying your target (the process in the body that you want to affect) to testing your molecule in humans for the first time,” she explained. “This process can take around five years. [GSK’s] goal with artificial intelligence is to reduce this time to just one year in future.”

“These are just a few of the many business areas where surfacing the information from within their data can drive better decisions,” Fanichet argues. He explains that cognitive search and analytics also have a range of powerful potential applications within customer service, enabling organizations to:

  • Provide personalized and highly relevant communication to their customers
  • Nurture customer relationships and prevent customer churn
  • Improve productivity, reduce operating expenses, and gain operational efficiencies
  • Minimize customer service representative turnover and knowledge loss

The Challenges Ahead for Cognitive Search

The potential use cases speak for themselves, but that doesn’t mean there aren’t challenges ahead for enterprises looking to incorporate cognitive search technology into their work. While working with clients, Fanichet explains, Sinequa helps them to understand that there are a set of common machine learning challenges along the path ahead. Expertise is often the first hurdle, but he maintains that there are many different types of AI implementation challenge. “Assuming that enterprises are able to resolve a dearth of expertise, there are still other challenges – most of which are specific to the type of AI being pursued.”

Take supervised machine learning, where the system learns to recognize patterns by observing ‘correct’ patterns provided by humans. “The greatest challenge is around providing sufficiently labelled training datasets from which the system can learn,” Fanichet explains. This is something Matt Buskell highlights in his ‘10 keys to AI implementation‘, recommending that following the initial loading of data and knowledge base, the system needs to go through a phase of refinement once the software has launched. “During this phase, things like gain and variance for Machine Learning, or intent training for NLP and maybe model refinement to cognitive reasoning need to be improved. During this phase, it is essential to carefully release the software and measure how well it’s performing over a 6-12 week period, at the least.”

Fanichet likewise highlights the obstacles unique to unsupervised machine learning, in which the system identifies existing patterns and a human determines their usefulness. “The greatest challenge is balancing the system’s need for sufficient data with the proper human guidance and interpretation needed to train the system,” Fanichet argues. This is as much an issue of skills and process culture as it is technical expertise, and is reflected in a recent Genpact survey of over 300 senior executives, which argues “AI cannot be implemented piecemeal. It must be part of the organisation’s overall business plan, along with aligned resources, structures, and processes.” Collaboration is therefore key.

Finally, there’s a need to formulate clear goals and outcomes, Fanichet says. “When pursuing reinforcement learning, where the system makes many attempts and learns from the outcome to take better actions, the greatest challenge is providing the system with a defined goal and sufficient practice in a dynamic environment so that the system can effectively learn from trial and error.”

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

Sinequa Brings the Power of AI to Enterprise Search

Fanichet believes Sinequa offer a range of unique intelligent capabilities within the analytics space:

  • Robust Indexing Engine: “If cognitive search was all about matching a keyword, a single index would suffice. The best results are obtained when multiple indexes are combined, each providing a different perspective or emphasis, providing a comprehensive overview of the information available. This provides the best possible understanding of the meaning it carries.”
  • Enterprise Grade: “Sinequa was designed from the start to support the complexities and multiple security layers of today’s enterprises. It was also designed to be immersed in diverse enterprise environments and can operate within the context of a specific industry and the language of the specific organization.”
  • Topically Aware: “Connecting information along topical lines across all repositories surfaces the collective expertise of the organization and makes it transparent. This is especially valuable in large organizations that are geographically distributed. By connecting people with expertise, the overall responsiveness of the organization increases.”
  • Natural Language Processing: “Sinequa’s world-class NLP offers automated language detection; lexical and syntactical analysis; and automatic extraction of dozens of entity types, including concepts and named entities like people, places, companies, etc. It also supports text mining agenda that is integrated into the indexing engine. This enables the extraction of virtually any function, relationship, or complex concept from the content.”
  • Machine Learning: “Sinequa leverages ML to enhance and improve search results and relevancy. This is done during ingestion but also constantly in the background as humans interact with Sinequa. It has become an essential part of the platform since it can handle complexity beyond what’s possible with rules.”
  • Well Designed User Experience: “Sinequa’s front-end serves as an intelligent agent that employees can consult for institutional knowledge that can be readily applied to the task or situation at hand. The experience is well designed in the sense that it is aesthetically pleasing, it is understandable in that it makes use of the user’s intuition, it is unobtrusive, and perhaps most importantly, it is contextual to the user’s goals.”
  • Ubiquitous Connectivity: “Sinequa’s product comes with over 160 ready-to-use connectors, all of which were developed in-house, thus ensuring consistency, quality control, and high performance.”


+1Share on LinkedInShare on Twitter

IDC Highlights Sinequa Strengths and Leadership in Cognitive Applications Vendor Spotlight

IDC-Vendor-Spotlight-Cognitive-Applications-Sinequa-2017 (2)A new IDC report is recognizing Sinequa for our Cognitive Search & Analytics platform around critical technologies, including machine learning and advanced natural language processing. This Vendor Spotlight looks at how Sinequa leverages artificial intelligence and cognitive computing-based analytics to meet the needs of companies that are looking to address complex problems with easy-to-use, powerful solutions featuring simplified interfaces.

According to the report’s author David Schubmehl, Research Director for IDC’s Cognitive/Artificial Intelligent Systems and Content Analytics research, “The capabilities being offered by cognitive knowledge discovery systems, such as Sinequa, provide many opportunities for enterprises to innovate and advance their organization using approaches that were either not possible or not easily implemented several years ago. Within many enterprises, these opportunities are limited only by the imagination and creativity of those seeking to improve their business and information handling processes.”

The report states that Sinequa’s software provides organizations with real-time, relevant results from unstructured and structured internal data, and that the we are developing our Cognitive Search & Analytics platform on an extensive foundation of unstructured information access technologies that include advanced natural language processing capabilities in 21 different languages.

Schubmehl adds: “While Sinequa has offered a flexible information collection, access, and analysis architecture for many years, it has now built capabilities around cognitive technologies, such as machine learning, advanced natural language processing, improved relevance, and better decision support while offering strong user and data interaction capabilities.”

The advancement of natural language processing and increased maturity of machine learning are creating substantial demand for cognitive search and analytics solutions. At the same time, the growth of unstructured data and pressure to improve worker productivity makes it even more critical to find the right information at the right time. This report highlights the fact that Sinequa’s platform meets this demand and by combining our solution with human ingenuity, we can produce the best possible search and analytics results.

The full report is available here:

+1Share on LinkedInShare on Twitter