3 Ways Manufacturers Can Leverage Insight Engines

This article was originally published on Manufacturing.net.

As distributed manufacturing gains adoption by some of the world’s largest companies, products are finding their way into the hands of customers faster than ever before. But in the process, companies are becoming increasingly disconnected and less efficient in new areas.  Unlike in traditional manufacturing, where materials are assembled in remote, centralized factories and shipped to customers, distributed manufacturing takes place at multiple, decentralized plants where products are assembled closer to the customer.

Global manufacturing

The process of leveraging a network of geographically dispersed manufacturing facilities connected over the Internet helps eliminate many of the inefficiencies of traditional manufacturing. However, distributed manufacturing creates new challenges in the form of separate entities, languages and processes.

Teams once gathered under a single roof are now spread across multiple sites, working on projects and programs unique to their local area. These distributed teams are inadvertently scattering knowledge and data across continents and systems, creating information silos that leave workers disconnected.

Content and data have been scattered across a myriad of applications and repositories without any means for end users to surface accurate information with any confidence. In some cases, access to critical institutional knowledge, insights and innovation is restricted to a small few. As a result, disparate teams are spending countless hours making and solving the same problem over and over again, lowering productivity and delaying projects.

A digital solutions manager at a global manufacturing firm once told me, “We had thousands and thousands and thousands of places where our documents were shared, managed by check-in, check-out. In fact, we once had thousands of applications with no search capabilities.”

But once information is accessible from a single place, there are no longer countless teams scavenging for data and insights. With insight engines, manufacturers are bringing the best people together for new projects. Critical parts are easier to locate instead of rebuilding or re-designing them. Even proposals and RFP responses are being developed faster with information found and shared from other RFPs.

These improvements are the result of insight engines that combine cognitive analytics, artificial intelligence and NLP (natural language processing) to helps computers understand, interpret and manipulate human languages, dialects and patterns.

This technology helps manufacturers quickly respond to changing conditions and identify products, parts and components across multiple data sources despite their distance from one another. Here are three ways manufacturers can leverage insight engines to rediscover their synergies and avoid mistakes.

Building Teams Through a Patchwork of Data

Many companies struggle to create the best teams for new projects because expertise is hidden in large organizations with multiple divisions and product groups. Sometimes this task is made more difficult by employees who fail to enter their background and experience in their HR profiles or update them with new skills or certifications.

AI-powered insight engines leverage NLP to surface information from large volumes of data stored in hundreds of millions of files, and thousands of repositories and databases to uncover work histories, experience on different projects, expertise, training and education in order to locate experts. Projects employees have worked on, languages they write in, and locations where they’ve traveled are all easily queried and analyzed to identify experts within the company.

Avoiding Duplication of New Parts

In a distributed environment, it’s difficult to find parts across systems and continents. Sometimes it’s easier to make new ones. That’s because parts are generally indexed by labels, which can be difficult to identify correctly. As a result, the exact same part is recreated, sometimes up to five times, each version with its own label.

This is a complete waste of time and money. And when a part is recreated, there’s a greater chance of a new flaw being introduced, which can be catastrophic in industries like transportation or medical devices.

With insight engines, parts data can be contextualized for engineers to identify duplicate parts and eliminate duplications. For any company with a broad base of engineers, the cost savings from this approach can be enormous.

Increasing the efficiency of proposal development

Companies with multiple departments and divisions often fail to share technical information for sales proposals and RFPs. Consequently, proposals for an opportunity in one part of the world with many of the same requirements as others in different markets or regions must be developed from scratch.

And, if questions arise during the proposal process, it is time-consuming and difficult to get answers from engineers. Acquiring new customers in a complex environment with a highly engineered, configurable project is time-consuming by its nature. The inaccessibility of data makes it worse.

Insight engines surface information in context across emails, databases, and records. With this information in hand, companies can respond faster to sales opportunities and present a common face for the company overall.

In all three of these scenarios, information and insights are securely surfaced from content and data in every application and every repository from every location across globally distributed companies, which enables teams to cultivate, improve quality and take advantage of new opportunities.

+1Share on LinkedInShare on Twitter

Sinequa Featured in IDC Technology Spotlight Dedicated to Financial Services Organizations

ScreenHunter_1549 Jan. 16 16.48With increased regulatory pressures, data silo proliferation and cognitive drain on analysts, AI-powered platforms become a key enabler to extract insights from data.

Today, we announced that Sinequa is featured in a new IDC Technology Spotlight report: Financial Services Organizations: Extracting Powerful Insights with AI-Powered Platforms. The report, written by Steven D’Alfonso, research director, IDC Financial Insights, and David Schubmehl, research director, Cognitive/AI Systems, highlights the importance of AI-powered platforms in their ability to extract insights from data as well as the need for financial services organizations (FSOs) to improve their capabilities to derive insights from the data they possess.

According to the report, collecting and maintaining increased amounts of data related to their clients and portfolios can provide major opportunities to improve the customer experience and increase revenue while reducing risk. But at the same time, too much data can be a cognitive drain on analysts and knowledge workers. This increasing need to collect data from multiple applications requires FSO stakeholders to organize and provision their data in ways that allow analysts to extract meaningful insights. AI can help FSOs mature from being data-driven to being information-driven.

“Over the years, Sinequa has continued to expand its footprint within leading financial institutions such as Credit Agricole, DZ Bank, LCL, Navy Federal Credit Union, and U.S. Bank as our platform enables them to tackle the challenges highlighted in this report,” said Scott Parker, director of product marketing at Sinequa. “By offering a broad-based AI-powered platform including search, content analytics, semantic understanding and auto categorization technologies, Sinequa provides relevant insights to users in their work environments, while supporting a range of machine learning algorithms and capabilities to improve findability and relevance, allowing FSOs to access the information they need when they need it.”

With the demand for AI technologies that enable intelligent analytics increasing every year, IDC estimates that “by 2022 spending on AI technologies will grow to over $8 billion, up from $2 billion in 2017.” Sinequa has in the past offered a flexible information collection, access and analysis architecture and now provides cognitive capabilities, such as machine learning, natural language processing, improved relevance and better decision support, while offering intuitive user and data interaction capabilities.

To learn more, click here or on the banner below to sign up for the webinar.

idc-finance-webinar

+1Share on LinkedInShare on Twitter

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.

+1Share on LinkedInShare on Twitter

Sinequa Wins the 2018 KM Promise Award

In addition to the awards Sinequa has collected this year – KMWorld Trend-Setting Products, Datanami Readers’ Choice Award, and Bio-IT 2018 Best in Show, just to name a few – we are excited to return from KMWorld / Enterprise Search & Discovery 2018 with yet another trophy.

On Wednesday, November 7th, at the KMWorld conference in Washington D.C., we participated in the Awards Ceremony for the finalists of the KM World Promise and the KM World Reality awards. The KM Promise Award is given to a company that implements and integrates knowledge management practices into business processes and works with clients to ensure they reach their goals. The award recipient provides innovative technology that breaks through the hype to help customers gain insights, collaborate and compete in a mobile and global business environment. The KM Reality Award recognizes an organization in which knowledge management is a positive reality, not just rhetoric. The award recipient has demonstrated leadership in the implementation of knowledge management practices and processes, realizing measurable business benefits.

We are very honored to have received the 2018 KM Promise Award. The other finalists in this category included ASG Technologies, BP Logix, DocStar, Nuxeo, Unifi Software and Workgrid Software. Laurent Fanichet, our VP of Marketing, accepted the award on behalf of Sinequa, followed by Jill Harris, Delta Airlines Sr. Communications Coordinator, who picked up the KM World Reality Award.

award

“2018 is a fantastic year for Sinequa. Leader for the third time in a row in the Gartner Magic Quadrant for Insight Engines, we are experiencing triple-digit growth as we continue to expand in North America with the opening of an office in San Francisco, among other major milestones. Winning the KM Promise Award is great validation of the value of our approach and the completeness of our solution. This reaffirms our commitment to offer our customers the most advanced Cognitive Search & Analytics platform to provide them with relevant and contextual insights to make better decisions, drive innovation and achieve greater operational efficiencies,“ said Fanichet.

booth

In addition to winning the award, we had a very successful show at KMWorld! It was a pleasure to meet with our customers and partners at our brand new lit-up booth and see new faces at the very well-attended keynote presentation from Scott Parker, Sinequa Director of Product Marketing, titled “Becoming an Information-Driven Organization.”

keynote

We look forward to returning to DC next year and adding more trophies to our “honors” shelf in the New York office! In the meantime, we are rounding up 2018 with a few activities – a KMWorld-hosted roundtable webinar on Cognitive Search and Analytics in Action (November 27) and exhibiting sponsorships at the AI World Conference in Boston (December 3-5) and the Forrester Data Strategy & Insights Forum in Orlando (December 4-5).

+1Share on LinkedInShare on Twitter

Sinequa Snags Three Key Industry Award Wins in September

Sinequa Industry Recognition - September 2018

We’re off to a busy September here at Sinequa! We’re excited and humbled to have received a few different awards for our Cognitive Search & Analytics Platform and company as a whole this month. Sinequa recognition has included the following awards from leading industry publications:

KMWorld Trend-Setting Products 2018

KMWorld’s 2018 list of Trend-Setting Products features not only emerging software directed toward human-like functionality but also more traditional offerings impressively refined. It encompasses AI, machine learning, cognitive computing and the Internet of Things, as well as enterprise content management, collaboration, text analytics, compliance and customer service. Read more.

DBTA’s Cool Companies in Cognitive Computing for 2018

DBTA and Big Data Quarterly presented the 2018 list of Cool Companies in Cognitive Computing to help increase understanding about the important area of information technology and how it is being leveraged in solutions and platforms to provide business advantages. Read more.

Datanami Readers’ Choice Award Winner

Sinequa won the Readers’ Choice – Best Big Data Product or Technology: Machine Learning category.

The Datanami Readers’ and Editors’ Choice Awards are determined through a nomination and voting process with input from the global big data community, as well as selections from the Datanami editors, to highlight key trends, shine a spotlight on technological breakthroughs and capture a critical cross-section of the state of the industry. Read more.

Looking forward to continuing the momentum for the rest of the year!

For more information on Sinequa’s cognitive search and analytics platform visit: https://www.sinequa.com/insight-platform-2/

+1Share on LinkedInShare on Twitter