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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Sinequa Helps Box Customers To Be Information-Driven

noiseMany customers that use Box for cloud content management are typically large, geographically distributed organizations. The four scenarios below describe common ways that Sinequa helps these customers leverage their enterprise information to become information-driven.

Increase the Signal, Decrease the Noise
Customers who have migrated even a portion of their enterprise content to Box have made a significant step.  Workers in their organization can no doubt share and collaborate more easily than ever before; they no doubt have reduced email overhead; and they are probably working the way they want to given all of the friendly integrations with Box, including Outlook, Office365, Google Docs and the like.   However, being in the cloud does not automatically mean the valuable “signals” in your data rise above the “noise”.  Messy data migrated to the cloud is still messy data.  Sinequa helps workers quickly narrow in on the information and insights necessary to do their job effectively and with confidence.  By analyzing the content and enriching it using natural language processing and machine learning algorithms, Box users can quickly find the information and insights they need to be effective and responsive.

Connect Data

connect-data

Many Box customers run their business with other enterprise applications and information repositories, all of which contain data and content related to the information
stored in Box.  Sinequa brings advanced analytics and cognitive techniques to “connect” the data and bring context across all of the various enterprise sources, whether they be in the cloud or on premise.  By connecting the data, knowledge workers can better navigate and see how the data and connect fit together along topical lines, regardless of how many repositories make up the enterprise information landscape.

Identify Knowledge & Expertise

Screen Shot 2017-10-13 at 2.40.37 PMAs previously mentioned, many Box customers are large (or even very large) geographically distributed organizations with expertise in a wide variety of subject matter areas.  In these organizations, specific experts are difficult to identify given the size and distributed nature of the organization.  This is a modern problem that requires a modern solution.  As users store content and collaborate within Box, Sinequa’s advanced cognitive capabilities analyze that content to determine not only the areas of expertise across the organization but who the specific experts are and surfaces that information to end users.  This connects people across geographic and departmental boundaries, accelerating innovation and elevating the performance of the overall organization.

Leverage 360º Views

Screen Shot 2017-10-13 at 2.42.23 PM

Think of all the “entities” that are critical to Box customers running their business.  These business entities include customers, either specific individuals (B2C) or accounts (B2B), products, parts, drugs, diseases, financial securities, regulations, etc.  Having all of the enterprise data virtually connected by Sinequa makes it possibly to provide a unified “360º View” of these various entities to bring all of the right information to the right person at the right time.
As you can see, leveraging Sinequa to contextualize the information within Box and other enterprise repositories not only boosts productivity and keeps knowledge workers in the flow but has repeatedly proven to enhance customer service, improve regulatory compliance and increase revenue within different areas of the business.  Achieving these benefits positively impacts the bottom line and serves as validation that an organization has become truly information-driven.
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Join Sinequa at Bio-IT World Conference & Expo 2016 (Booth #421)

Sinequa will present and exhibit at Bio IT World Conference & Expo that will take place on April 5-7 at the Seaport World Trade Center in Boston, USA.

Sinequa For Life Sciences

We invite you to stop by the Sinequa booth #421 to discuss innovative use cases of our solution for the Pharma industry – Sinequa For Life Sciences - and see how our customers raised their competitiveness by implementing our Big Data Search and Analytics solution across the most diverse data silos.

  

Also, make sure to book your agenda and attend our presentation in the Bioinformatics Track #5:

Wednesday, April 6, at 2:55-3:10 PM

“Increasing the Competitiveness of Pharma Companies:
Real Time Search and Analytics Across Structured & Unstructured Data”

Speaker: Xavier Pornain, Vice President of WW Sales & Alliances

Book your agenda

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The Heat in The Trend Point: June 24 to June 28

We come across so many articles in the media that point to next generation search solutions and innovative business intelligence systems, but there are many companies still using the technology of bygone days. This idea occupied  The Trend Point over the past week.

Despite recent innovations with semantic search capabilities and interface design that tends towards an intuitive user experience, legacy systems still remain in the enterprise. “Enterprise User Experience Matters” summarizes the state of the matter:

The computational legacy of the 1960s is still with us today, despite a surplus of aluminum and gorilla glass. And despite the aspirations being fulfilled on the consumer level, a comprehensive simplicity is lacking at the core of most enterprise software. Obscurity and inconsistency reign (think of BlackBerry’s descent of late) where transparency and interoperability ought to go hand in hand. The egregious result is that the everyday tools, the interfaces that we must interact with daily in our jobs—from banker to lawyer, from journalist to physician—are almost incapable of leveraging the considerable network of information that many of us need to wade through at work.

When the problem has been recognized as an information management issue stemming from the software “solution,” many companies know they must take action. However, there is no one correct path to take. We saw the following summary in “Data Management Tips” offer advice:

Keep in mind that overhauling an existing system or syncing all of the databases in an organization can be an enormous, costly, and difficult project that can take months or years to implement – this may make it impractical, particularly if other projects will deliver a bigger business benefit. However, you can take other steps to improve data management for your team, and for your organization.

What should be done with existing data when replacing a legacy storage system? “Combining Big Data with Existing Data” calls for the integration of data previously collected and stored with the huge chunks of unstructured data represented by varying file types. The following information was relayed in this post:

Big data opens an entirely new data universe to consider and use to improve decision making. But how does a business/systems analyst turn it into actual usable data so that it can be used for operational improvements that result in real business value? Success depends on how fast and seamlessly you can combine your big data with your enterprise data and present that collective information to your decision makers.

While we definitely recommend storing and parsing old data in addition to new data, merging legacy enterprise data warehousing systems with new solutions is not always a cut and dry answer. When there are many search solutions that provide efficient information access in real-time, who needs to hold on to any remaining parts of a legacy search system? Companies like Siemens, for example, are choosing to replace their out-dated search technology with Unified Information Access.

Jane Smith, July 03, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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The Heat in The Trend Point: June 10 to June 14

Big data is usually mentioned at least a bit in The Trend Point, and last week was no exception. We noticed that many of the articles seemed to be pointing towards going beyond information retrieval.

Value must be added through the technologies of an information management, search and analytics system. An article quoted in “Big Data without Value is Just a Lot of Data” states the following:

Relying solely on the information gathered by Big Data is like watching a group of people from a relatively far distance. It’s possible to see what they’re doing while they interact with each other and engage in conversations, but it’s virtually impossible to understand why they’re holding those conversations, what are they feeling that drives their actions, what is the emotion underpinning those conversations, and most importantly, how they’ll determine the future behaviour of each individual and the group at large.

We heard a similar sentiment repeated in “Data Visualization Key to Data Understanding” with an emphasis on the end goal being easy access to actionable information. This post relayed the following:

It’s typical for an analyst who has been working on a project for more than two months to show all the frequency or statistical results with a presentation deck consisting of hundreds of slides. Stop! A few charts with great data visualization are worth 1,000 slides. Actionable visualizations such as Price or Attrition Alerts can help sales teams better engage with customers instead of analyzing a plethora of reports. The key: reports should be easy to understand as well as recommend the next actionable step for business leaders.

In another post, we saw another mumbling that big data is a misnomer better represented as big content. We noted some of the thoughts that followed — the necessity of extracting value from unstructured content — in the article “Big Data or Big Content“:

Unstructured content is often included almost as an afterthought, with extraction and enrichment applied on-the-fly, from scratch on a case-by-case basis. This undermines the potential of Big Data in several ways. It raises the cost of incorporating unstructured content while also increasing the opportunities for the introduction of inconsistencies and errors reducing the quality of the final product. Most importantly, the ad hoc approach also reduces the potential of Big Data by obscuring the extent of available raw materials.

It is refreshing to see that these several media sources are no longer discussing simply mashing up raw data from different sources. The important pieces are fusion of data (both structured and unstructured) and that comes through strong analytics that can detect what belongs to the same semantic category. Then a system like Unified Information Access from Sinequa can “fuse” results with other data, like geographic position or customer history, and others.

Jane Smith, June 19, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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