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 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.

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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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Accelerating GDPR Compliance with an Information-Driven Approach

With GDPR in full effect, Ovum has published a new white paper featuring Sinequa and outlining how cognitive search can be a compliance accelerator.  The full white paper can be found at http://go.sinequa.com/white-paper-gdpr-ovum-2018.html.

ovum-gdprThe white paper, written by Ovum senior analyst Paige Bartley, outlines the challenges of aligning existing business objectives with GDPR compliance and how companies can accelerate GDPR compliance with an information-driven approach.

There is a new precedent with GDPR for enterprise control of data of all kinds, both structured and unstructured. Gaining better control of data is a deceptively simple concept, but requires numerous capabilities, such as the ability to search for data across silos and granularly manage who has access to what. Sophisticated search and analytics capabilities, spanning organizational silos, are key to both facilitating compliance and driving informational value.

According to the white paper: “GDPR compliance requirements should not be thought of as an antithetical force against enterprise initiatives to leverage and analyze data. The more the enterprise can align its compliance obligations with existing business objectives – such as the demand to provide excellent customer service via a 360° understanding of customer desires – the more it can benefit from the perceived ‘burden’ of GDPR compliance. Enterprise-wide search is central to these capabilities and will help the enterprise gain a centralized view of subjects and topics in the increasingly distributed IT ecosystem.”

As an example of this concept, the white paper highlights Sinequa’s platform deployment at Stibbe, a global, full-service law firm with an internationally oriented commercial practice. Stibbe implemented Sinequa’s Cognitive Search & Analytics Platform to provide secure, unified access to millions of documents and legal matters. The result was a holistic view of the firm’s informational landscape, with the ability to quickly retrieve all material related to individual topics, cases or clients.

This Ovum white paper gets at the heart of Sinequa’s approach to GDPR and data analytics. By taking control of data and unleashing it as a strategic tool, organizations can become information-driven in a way that meets compliance requirements while accelerating innovation and creating a key competitive advantage.

The full white paper can be found at http://go.sinequa.com/white-paper-gdpr-ovum-2018.html

 

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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.

ROI: BUSINESS BENEFITS—NOT JUST COMPLIANCE
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.

NEW PARADIGM: CUSTOMER SELF-SERVICE FOR INFORMATION RETRIEVAL
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.

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