Data Doesn’t Drive Finance

Your people need information, not data. On average, they waste a day a week searching across silos, systems, and clouds for information. It’s pre-digital-age work. Learn how AI-Powered Search gives your employees the information and intelligence they need.

View the white paper

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Keeping Secrets Secret: How to Industrialize Information Privacy

Banks run on trust. At the core of trust is protecting the privacy of client information. Clients expect it. Regulators require it. Though a challenge for any financial institution, this challenge amplifies at complex global banks. Traditional approaches rely on human skill and craft, rather than on software. This means the average information privacy process isn’t industrialized or providing systematic assurance that it’s working.

Click here to download the solution white paper to learn how one of the world’s top 20 banks addressed this challenge.

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Sinequa 2018 Roundup… 2019 Here We Come!

2018Sometimes it helps to look at an entire year to gauge just how far you’ve come in a relatively short period of time. Sinequa experienced some very positive developments in 2018 that are worth highlighting. Our software platform evolved on several fronts to help us accelerate our mission to power the information-driven economy. In parallel, our customers demonstrated what the platform can do, even when stretched in creative and unexpected ways.

On the Technology Front

The Sinequa platform evolved with some very useful and powerful new capabilities in 2018.

Content-related Capabilities

Many of the new capabilities improved on the platform’s ability to integrate with even more enterprise applications and content formats, including:

  • New connectors to support the goal of ubiquitous connectivity across the enterprise. Among these were connectors to Atlassian products to incorporate information from software development projects, including source code files. Also addressed were new versions of popular repositories like SiteCore (a leading web content management platform according to Gartner), along with the likes of Azure storage, AODocs, Beezy, and Teamcenter.
  • New converters to index more formats like OCR on PDF and Images, AutoCAD and Windchill files, Visio, Improvements on PowerPoint, and a dedicated converter for source code files
  • Tighter integration with SalesForce.com
  • In a year full of major data privacy breaches being reported, the Sinequa platform continued to strengthen support of additional levels of encryption like in-flight encryption between all components in a distributed deployment and encryption at indexing time to secure the document cache, which contains elements like HTML preview and thumbnails to better serve customers operating in highly secure environments

Further Automation for the Interpretation of Meaning

The platform’s ability to interpret the meaning of content also evolved in 2018.

  • Query Intent: It is now possible to configure rules to be applied on queries to change the behavior of the underlying search process. This new query intent capability analyzes the query to detect certain words and entities and triggers actions based on the specified rules and classifications. New default entities were also introduced in the platform in 2018 that can be leveraged by the query intent capability and for enrichment during indexing.
  • Enhanced Linguistics: There were some language-specific improvements added to the platform to help automate the interpretation of meaning. These included things like enhanced linguistic processing for compound words in French, improved lexical disambiguation in English, enhanced detection of ordinal numbers for Danish & Swedish.

Improvements in Machine Learning

The year 2018 brought several significant improvements in the Sinequa platform’s ability to leverage machine learning, including:

  • The platform evolved to embed Online Machine Learning, applying machine learning models based on Spark or TensorFlow directly in the indexing pipeline. This represents the first of many new components that can serve machine learning models in real time. Deep learning is also used during indexing to detect new entities or concepts. These are immediately fed into machine learning algorithms, for example in the classification of incoming documents.
  • Packaged with the platform is a new unsupervised Deep Learning application for text analysis that detects the key words, key phrases, and key sentences of a document.
  • The platform now supports the Spark 2.3 implementation.
  • Packaged integration with 3rd party spark distribution providers – e.g. AWS EMR, HortonWorks.
  • Battle testing of supervised classification algorithms – i.e. Sinequa reached a threshold training set size over 10M documents
  • First machine learning customers are now in production
  • Packaging of hierarchical classification
  • Ongoing transition to Software 2.0 paradigm where software is effectively “trained” rather than manually programmed with the packaging of the lifecycle of the model and the model feedback from the search based applications into the Sinequa platform.

Presentation Enhancements for End Users and Admins

Sinequa invested significantly during 2018 to evolve the way the platform presents insights to end users as well as status information and optional settings to administrators. Here are a couple of the most significant developments:

  • A very exciting development from 2018 involved a complete overhaul of the user interface framework to a responsive design based on Angular 7. This will not only ensure optimal flexibility and performance for end users on all kinds of devices, but will open up Sinequa application development to a much wider audience.
  • On the Admin front, components have been reshaped to offer administrators of the platform more functionality and a better user experience for their work behind the scenes.

On the Customer Front

There were a few compelling themes driven by our customer base in 2018, each of which was rewarding in its own way.

Customer satisfaction and retention is a predominant theme for Sinequa. We are extremely pleased by the sheer number of existing satisfied customers that came back to us in 2018 with additional use cases to accelerate their information-driven journey. For instance, business drivers related to governance, risk and compliance with the advent of GDPR and related regulatory demands spurred a lot of activity this past year.

We also had a significant number of customers who experienced that “light bulb moment”, which often occurs when they realize their existing return on Sinequa investment could be significantly amplified by extending the use of the platform with information-driven applications in other parts of the business – e.g. areas like customer service, R&D, supply chain, and other knowledge-intensive arenas.

We even had a few long-time customers take a pause to re-evaluate their vendor choice and, without exception, decided to double-down on their commitment to Sinequa for years to come.

Of course, the disappearance of the Google Search Appliance brought some new customers into the fold, most of them fiercely determined to go beyond their previous use of a dying application and truly become information-driven.

Possibly the single most exciting development for Sinequa in 2018 was the surge in machine learning projects, which contributed significant business value back to the respective organizations, especially in the Financial Services industry. As the underlying technology matures, we see a steady trend for machine learning projects going from research to production stages. Some of the projects from 2018 focused on applying machine learning models to automate the curation of enterprise content and improve relevance. For example, one customer demonstrated how trained machine learning models could be used to make the enrichment of their enterprise corpus more efficient. It turns out that by proactively identifying what content qualifies as “scientific”, both time and money can be saved by preventing non-scientific content from even being considered for scientific enrichment during ingestion. Another customer took a completely different tack, using machine learning to automatically reproduce confidentiality policies to classify large volumes of banking documents with measurably higher quality at a fraction of the cost they would have spent to do it manually or even with a more traditional rules-based approach.

Now it’s on to 2019!

As we turn the corner into 2019, we are grateful for both the accomplishments of our R&D team and for all of our partners and customers, especially those who provide the challenges, creativity and critical feedback necessary for Sinequa to continue providing the leading platform for information-driven applications and solutions.

We wish you all the best and look forward to serving all of you in 2019 and beyond.

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How organizations can evolve from data-driven to information-driven

This article was originally published on Information Management.

Over the last several years, data analytics has become a driving force for organizations wanting to make informed decisions about their businesses and their customers.
With further advancements in open source analytic tools, faster storage and database performance and the advent of sensors and IoT, IDC predicts the big data analytics market is on track to become a $200 billion industry by the end of this decade.
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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.

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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.

 

 

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