Cognitive Search & Analytics Capabilities Out of the Box for Box Customers

In today’s digital age, leading organizations are looking for better ways to get more out of their data. They are choosing platforms that make every employee more connected, productive, and mobile-without compromising security. As companies adopt Box, providing intuitive information access and advanced search capabilities become increasingly important to end users. Using advanced Natural Language Processing (NLP) and Machine Learning algorithms, Sinequa’s Cognitive Search & Analytics platform enables users to search, analyze and gain valuable insights extracted from Box content repositoriesalong with on-premises enterprise applications, big data and cloud environments.

To build a sophisticated search and analytics engine is one thing, but to build such an engine that can preserve all the native security and permissions settings of connected repositories is another matter altogether. With Sinequa and Box connected, workers can search the Box environment (and all other data sources) while maintaining the native control settings of the respective platforms in which the data resides. This ensures that the granular security and permissions within Box are maintained in the Sinequa search interface, allowing individual users to seamlessly search and leverage only the content they are entitled to access.

The result is an environment unhindered by unnecessary, cumbersome processes for permission requests, or worse, unintentional viewing of unauthorized content. This allows users to quickly search and pinpoint the data, content, subject-matter experts, and topics they need in a fully secure and managed environment, where only the relevant data appears to each individual.
To learn more about the partnership between Box.com and Sinequa and the benefits of Cognitive Search & Analytics, you can download the complimentary research note “Sinequa partnership with Box amplifies cross-platform enterprise search and analytics” – April 2017 – from Paige Bartley, Senior Analyst at Ovum.

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Gartner Named Sinequa a Leader in Its Magic Quadrant for Insight Engines

As the CEO of Sinequa, I am proud that Sinequa was recognized as a leader in the recently released Magic Quadrant for Insight Engines 2017. Being a Gartner Leader, once again, underlines our continued progress that has led to this renewed leadership position in a Gartner Magic Quadrant. (We have previously been positioned as a leader in the Magic Quadrant for Enterprise Search.)  Gartner selects leaders for their “Completeness of Vision” and their “Ability to Execute” Good to see that others find our vision convincing and believe in our ability to realize it!

More reassuring still is the testimonial of our customers that led Gartner to state that “reference customers regarded Sinequa’s roadmap and future vision for its software to be particularly attractive. All indicated that those were significant reasons for choosing the software.”

As an established Cognitive Search platform, we’re continuing to evolve our vision and invest in enabling the largest organizations such as Airbus, AstraZeneca, Bristol Myers Squibb, Credit Agricole, and Siemens around the globe to get more value from their ever growing and diverse Enterprise data, as well as broadening the impact of search and analytics within the digital workplace of their employees.

According to Gartner:

“Insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments.”

Gartner-Magic-Quadrant-for-Insight-Engines-2017-Sinequa

Get your copy of the full report here and see why Sinequa is among the 3 leaders over the 13 vendors who participated in this Magic Quadrant.

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Machine Learning Becomes Legit, but Not Mainstream in 2017

ML-Sinequa-Predictions-2017

There has been a lot of hype around machine learning lately. Over the past decades, we’ve heard about various concepts around machine intelligence that in most cases didn’t get anywhere. But more and more frequently, organizations are learning how to bring together all the ingredients needed to leverage machine learning, and there is a simple reason for that: according to Moore’s law, the performance of microprocessors has increased since 1980 be a factor of more than 16 million! A program that ran on a 1980 computer for more than half a year today delivers its results in one second!

That is why I think Machine Learning will be the story for 2017. We’ll see it move from a mystical, over-hyped holy grail, to more real-world, successful applications. Those who dismiss it as hocus-pocus will finally understand it’s real; those who distrust it will come to see its potential; and companies that apply ML to appropriate use cases will achieve real business benefit without the high cost of entry that was common in years past. In 2017 it will be clear that it has a credible place in the business toolkit.

The four necessary enablers for machine learning – huge parallel processing resources, cheap storage, large and appropriate data sets, and accessible machine learning algorithms – are all now mainstream. Most large organizations have readily-available access to all these components (appropriate data sets are potentially the only open question, as they are always business- and use-case-specific), which makes machine learning a real possibility to reduce risk, increase customer satisfaction and loyalty, create new business models, identify patterns, and optimize complex systems.

One area where machine learning is growing rapidly and already showing success is for cognitive search and analytics applications. It won’t take over core algorithms anytime soon, but ML is already supplementing and enhancing search results based on user actions and smart analysis of content.

I don’t foresee machine learning achieving “mainstream” status in 2017, but it will within the next few years because the technology is advancing exponentially, quickly enabling its use in broader contexts.

For more on my complete prediction on machine learning, check out this article in Virtual Strategy Magazine.

 

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Artificial Intelligence in 2017: Expands Capabilities, but Impacts the Workforce

Artificial-Intelligence-SinequaThe beginning of the new year is a good time to reflect on the events of 2016 and on their forebodings for the coming year and beyond.There has no doubt been a great deal of buzz around artificial intelligence (AI) this year. However, it’s difficult to sort through what’s hype and what’s not to determine where these technologies will actually take us in 2017. While we know the trend will continue in some form, what will be new or different next year? Here are some of my predictions: 

Artificial Intelligence is taking the industry by storm, and not just in “Westworld.” We’re entering a new phase of AI thanks to advances in computing power and volume of data. This has opened the door to solve computational problems on a scale that no human mind could approach – even in a lifetime. The result is that computers are now able to provide responses that aren’t dictated by a collection of “if A, then B” rules, offering results that can only be explained by saying that the computer “understands.” The benefit is that complex and time-consuming cognitive processes can now be automated, and we can do things at scale that were previously impossible because unlike humans, computers are not overwhelmed by volume.

We’re definitely headed in the direction of workforce displacement and I believe it’s going to happen quickly, as there are huge economic incentives to increase efficiency and to automate manual tasks. This will happen faster than we expect because we think linearly, while technology is advancing exponentially. We struggle with that perspective because it quickly outpaces what we can readily grasp, whether that be in size or speed, or both. This will bring additional challenges because the disruption will occur across the occupational spectrum (unlike the industrial revolution, which primarily impacted “low-skill” jobs). I don’t see any particular sector being hit by this tidal wave in 2017, but AI is a disruptor like we’ve never seen before and it will be here soon whether we are ready for it or not.

However, with this transformation, tasks that have been impractical because of the time/labor involved now become feasible, which means we’ll be able to do things we haven’t been able to do before. It will also free us from many mundane and repetitive tasks, enabling people to focus on new or more valuable activities. This will increase efficiency in the workplace as well as consistency, which will improve quality and safety. So while the workforce will look very different from how it looks today – certainly in 10 years and probably in five, AI and ML are going to greatly extend and expand our capabilities in ways that, for now, we can only imagine.

What are your predictions for 2017 and beyond? For a full list of my predictions on AI other topics such as machine learning and big data, check out my post in VMblog.

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What is Cognitive Search? How a New Generation of Platform is Transforming Enterprise Insights?

Despite the effort from technology vendors to deliver relevant, contextual, and actionable insights with their applications, most organizations have been slow if not reluctant to embrace these advances in search-driven experiences. In fact, a lot of companies have been burned by their past enterprise search experiences.

The good news is that something is shaking the world of Enterprise Search – some would say ‘finally.’ New industry investments and R&D effort are changing the search experience to provide more relevant results and deeper insights to users in their work context.

As we enter the era of “cognitive computing,” new search solutions combine powerful indexing technology with advanced Natural Language Processing (NLP) capabilities and Machine Learning algorithms in order to build an increasingly deep corpus of knowledge from which to feed relevant information and 360° views to users in real-time. This is what leading analyst firms call “Cognitive Search” or “Insight Engines.”These cognitively-enabled platforms interact with users in a more natural fashion, learn/progress as they gain more experience with data and user behavior, and proactively establish links between related data from various sources, both internal and external.

In a recent brief, Forrester defines cognitive search as:

“Indexing, natural language processing, and machine-learning technologies combined 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.”

How does cognitive search work to deliver relevant knowledge?

  • It extracts valuable information from large volumes of complex and diverse data sources. It is crucial to tap into all available enterprise data whether internal or external, both structured and unstructured, to provide deeper insights to users in order for them to make better business decisions. Cognitive search provides this connection to provide comprehensive insights.
  • It provides contextually and relevant information. Finding relevant knowledge across all available enterprise data requires cognitive systems using Natural Language Processing (NLP) capable of “understanding” what unstructured data from texts (documents, emails, social media blogs, engineering reports, market research…), and rich-media content (videos, call center recordings..), is about. Machine Learning algorithms help refine the insight gained from data. Trade and company dictionaries and ontologies help with synonyms and with relationships between different terms and concepts. That means a lot of intelligence and horse power “under the hood” of a system providing “relevant knowledge” or insight.
  • It leverages Machine Learning Capabilities to continuously improve the results relevancy. Machine Learning algorithms (amongst the most popular ones: Collaborative Filtering and Recommendations, Classification by Example, Clusterization, Similarity calculations for unstructured contents, and Predictive Analysis) provide added value by continuously refining and enhancing the search results in an effort to provide the best relevancy to users.

Thanks to new technology advancements, cognitive search brings to data-driven organizations a new generation of search enabling them to go far beyond the traditional search box, empowering its users to get immediate and relevant knowledge at the right time on the right device.

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