Mind the Information Gap

The following was originally published on the Benelux Intelligence Community website.

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.

MIND_the_GAPMany organizations now understand the value of extracting relevant information from their enterprise data and using it for better decision-making, superior customer service and more efficient management. But to realize their highest potential in this space, organizations will have to evolve from being “data-driven” to being “information-driven.” While these two categories might sound similar, they’re actually quite different.

In order to make a data-driven decision, a user must somehow find the data relevant to a query and then interpret it to resolve that query. The problem with this approach is there is no way to know the completeness and accuracy of the data found in any reliable way.

Being information-driven means having all of the relevant content and data from across the enterprise intelligently and securely processed into information that is contextual to the task at hand and aligned with the user’s goals.

An information-driven approach is ideal for organizations in knowledge-intensive industries such as life sciences and finance where the number and volume of data sets are increasing and arriving from diverse sources. The approach has repeatedly proven to help research and development organizations within large pharmaceutical companies connect experts with others experts and knowledge across the organization to accelerate research, lab tests and clinical trials to be first to market with new drugs.

Or think of maintenance engineers working at an airline manufacturer trying to address questions over an unexpected test procedure result. For this, they need to know immediately the particular equipment configuration, the relevant maintenance procedures for that aircraft and whether other cases with the same anomaly are known and how they were treated. They don’t have time to “go hunting” for information. The information-driven approach draws data from multiple locations, formats and languages for a complete picture of the issue at hand.

In the recent report, “Insights-Driven Businesses Set the Pace for Global Growth,” Forrester Research notes organizations that use better data to gain business insights will create a competitive advantage for future success. They are expected to grow at an average of more than 30 percent each year, and by 2020 are predicted to take $1.8 trillion annually from their less-informed peers.

To achieve this level of insight, here are several ways to evolve into an information-driven organization.

Understand the meaning of multi-sourced data

To be information-driven, organizations must have a comprehensive view of information and understand its meaning. If it were only about fielding queries and matching on keywords, a simple indexing approach would suffice.

The best results are obtained when multiple indexes are combined, each contributing a different perspective or emphasis. Indexes are designed to work in concert to provide the best results such as a full-text index for key terms and descriptions, a structured index for metadata and a semantic index that focuses on the meaning of the information.

Maintain strong security controls and develop contextual abilities

Being information-driven also requires a tool that is enterprise-grade with strong security controls to support the complexities and multiple security layers, and contextual enrichment to learn an organization’s vernacular and language.

Capture and leverage relevant feedback from searches

As queries are performed, information is captured about the system that interacts with the end user and leveraged in all subsequent searches. This approach ensures the quality of information improves as the system learns what documents are most used and valued the most.

Connect information along topical lines

Connecting information along topical lines across all repositories allows information-driven organizations to expose and leverage their collective expertise. This is especially valuable in large organizations that are geographically distributed.

As more people are connected, the overall organization becomes more responsive in including research and development, service and support and marketing and sales as needed. Everyone has the potential to be proficient in less time as new and existing employees learn new skills and have access to the expertise to take their work to the next level.

By connecting related information across dispersed applications and repositories, employees can leverage 360-degree views and have more confidence they are getting holistic information about the topic they are interested in, whether it be a specific customer, a service that is provided, a sales opportunity or any other business entity critical to driving the business.

Leverage natural language processing

A key to connecting information is natural language processing (NLP), which performs essential functions, including automated language detection and lexical analysis for speech tagging and compound word detection.

NLP also provides the ability to automatically extract dozens of entity types, including concepts and named entities such as people, places and companies. It also enables text-mining agents integrated into the indexing engine that detects regular expressions and complex “shapes” that describe the likely meaning of specific terms and phrases and then normalizes them for use across the enterprise.

Put Machine Learning to work

Machine learning (ML) is becoming increasingly critical to enhancing and improving search results and relevancy. This is done during ingestion but also constantly in the background as humans interact with the system. The reason ML has become essential in recent years is that it can handle complexity beyond what’s possible with rules.

ML helps organizations become information-driven by analyzing and structuring content to both enrich and extract concepts such as entities and relationships. It can modify results through usage, incorporating human behavior into the calculation of relevance. And it can provide recommendations based what is in the content (content-based) and by examining users’ interactions (collaborative filtering).

Taking these steps will help organizations become information-driven by connecting people with the relevant information, knowledge, expertise and insights necessary to ensure positive business outcomes.

 

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Cracked Conversations: What to Do When Chatbots Aren’t Enough

Enterprise Search to Compliment Your Chatbot ExperienceBy: Robert Smith, Sales Engineer and John Finneran, Product Marketing

Conversational AI, or chatbot, vendors, are everywhere, deafening customers with the promise of AI-Powered solutions for their customer service needs.  According to Capterra, 158 companies currently offer chatbot software.  In Forrester’s evaluation of the emerging market for conversational AI for customer service for Q2 2019, the analyst firm identified the 14 most significant providers in the category – [24]7.ai, Avaamo, Cognigy, eGain, Indenta Technologies, Interactions, IPsoft, Kore.ai, LogMeIn, Nuance Communications, Omilia, Saleforce and Verint.

This makes understanding what works best to improve customer experience hard.

Chatbots work best guiding users along straightforward, well-defined conversational paths.  If a customer asks new, unpredicted questions the typical chatbot gets confused. More complex questions require complementary solutions.  

Sinequa offers one such complementary solution – Enterprise Search that can work with chatbots to help customers and employees find what they need.

We have spoken with a number of companies ranging from those considering the technology, to building prototypes, to deploying chatbots in customer-facing applications.

Several of the concerns about the value produced by chatbot deployments

  • Slow Conversation speeds
  • Conversation path-sets grow larger and longer
  • Low accuracy because the chatbot was unable to answer and was unable to maintain the chat
  • High development effort with too many expert hours spent conceiving, designing, deploying, and maintaining those conversational paths.

Some Reasons Why?

Chatbots work best when guiding a well-defined type of user through a set of preconceived conversational paths.

The typical chatbot’s tooling provides a graphical interface, and some testing capabilities; conceiving, designing, deploying, and maintaining those conversational paths will be up to you.

  • When you consider how many paths a user might take, multiplied by the number of user types, it can grow to an astonishing amount of work.
  • When chatbots have a lot of this work to do, they tend to slow down compromising, the chat experience
  • Most requests for information are ‘ad-hoc’ and therefore not well-suited for a pre-planned and pre-built conversation flow.

When Do Chatbots Make Sense?

An example is a chatbot at your local bank

  • They have a limited set of offerings for users to choose from
    • E.g. checking, savings, mortgages, lines of credit
  • Those offerings have a limited number of actions
    • Checking deposit, transfer, bill pay, balance inquiry
  • The site is often for reference, not as much for execution
    • To actually open up an account type, you typically have to apply in-person

If you can’t narrow the scope to specific user-types and paths like these, then the outcome of multi-step “chats” is by definition, less predictable, leading to a higher failure rate.

This also makes it difficult for some chatbots to get a PTO (Permit to Operate), because companies have not let applications go into production that couldn’t guarantee outcomes.  This is to avoid “Rogue AI” situations, among other things.

Addressing the Challenge

Enterprise Search, like Sinequa’s, leverages natural language processing (NLP) to get users the most relevant content, without the chatbot’s requirement that the conversational path be designed, built and maintained.

Where chatbot interactions are sometimes helpful, that chatbot can connect to enterprise search; when the chatbot gets a user’s request for information, the chatbot can refine and forward the request to the underlying Sinequa search, then channel the results back to the user’s conversation.

In Short

By using chatbots and a powerful enterprise search platform together for the jobs they were designed for, you can deliver profitable and productive solutions that enhance both customer and employee experiences.

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Enterprise Search – Then and Now

This following post was originally published on emerj.com and is based on a presentation by Daniel Faggella for Sinequa‘s INFORM 2019 client event.

Traditional Search – Then

Older search applications would usually search through structured documents, such as loan application forms. They emphasized predictable formats and matching keywords directly to their appearances in enterprise documents. Also, at the time, only natively digital text was searchable, as opposed to scanned print and handwriting. It would take some years before scanned documents and other unstructured data types became searchable.

Daniel Faggella speaking at Sinequa’s INFORM 2019 event in Paris.

Daniel Faggella speaking at Sinequa’s INFORM 2019 event in Paris.

Before machine learning, “intelligent” search applications could not handle as much metadata as current systems. This made searching for complex topics difficult. In addition, metadata was applied to documents manually. This was a time-consuming process that was required for documents that a company wished to be able to search in the future. In many cases, this continues to be the case.

Intelligent Search – Now
Current search applications can now handle all kinds of structured and unstructured content in various file types with an emphasis on classification for further accessibility. These applications could also enrich documents with metadata, allowing for concept searching and automatic document organization.

Past Difficulties Persist Today
Artificial intelligence and machine learning are not the solution to every search-related business problem. Despite how much search applications have developed over the years, companies still face some of the same difficulties as in the past. The difficulties with adopting an intelligent search application include integration, defining metadata, and determining what data is needed to search the documents a bank or financial institution wants to search.

AI startups and other vendors that are new to the intelligent search space often underestimate the difficulties their clients are likely to face with adoption. Overcoming these challenges can be hard work, and we find that many companies that are just starting out with intelligent search do not consider the commitment required to do so.

These companies often market their AI applications as easy to deploy within the enterprise. However, it is likely that they do this because they have not finished the thorough process of bringing an AI application into the enterprise. They may not have run into the common problems with data infrastructure (an ML problem that almost every enterprise data science leader struggles with) or defining their use cases (easier said than done, requires lots of business context from subject-matter experts).

What AI and ML Bring to Enterprise Search

The potential influence of artificial intelligence and machine learning on enterprise search can be understood as two important capabilities:

Making more information accessible – Making data digitally accessible using techniques such as optical character recognition, machine vision, scanning documents, and analyzing more data types. An AI application can also accomplish this by automatically adding metadata to backlogs of enterprise data.

Enabling companies to ask deeper questions – Enabling the capability of searching for broader concepts as opposed to strict keywords. This is helpful for finding insights on a general topic instead of simply every document including a few terms. Employees could search for documents and information beyond what directly pertains to a single keyword.
When observing the differences between search applications of the past and those of the present, one can see that artificial intelligence could help broaden a bank’s access to data. At the same time, the technology could transform the way in which employees search for that data, thus capitalizing on that access even more.

Use-Case Overview

Enrichment and Classification
One use case of intelligent search for banks and financial institutions is in data enrichment and classification. Documents need to be tagged with metadata, or data that describes the data within those documents. Metadata is what allows employees to search for documents using search queries with keywords and filters.

Traditionally, these documents need to be manually tagged with metadata, often upon uploading or creating them in the ideal situation. But that doesn’t always happen, and as a result, a bank’s digital ecosystem can end up very disorganized. Employees forget to tag documents or tag them incorrectly, making them difficult to find when needed.

Artificial intelligence could improve this process, but leaders at the bank will still need to decide what kind of metadata they want documents tagged with. For example, leaders at the customer service department may want to tag call center logs with metadata about the kind of problem the customer is facing and the emotional state of the caller.

Once they determine categories of metadata, subject matter experts at the department can start tagging documents with this metadata, and once this is complete, they can feed these tagged documents into the machine learning algorithm that will power the intelligent search engine. The bank will then be left with a search application that could automate and improve two parts of the search and discovery process:

Enrichment – When employees upload or create a document, the intelligent search application could automatically tag the documents with metadata, immediately preparing them for search. The application could also run through older documents and automatically add metadata to them as well.
Classification – The machine learning algorithm could also cluster the metadata into broader categories. As a result, documents that are uploaded and created could be automatically organized into folders and allow for easier search with keywords.

Example: Data Confidentiality
Banks and financial institutions could use an intelligent search application to restrict access to enterprise data based on different levels of confidentiality.

They could use these groups as thresholds for documents so that the higher one’s threshold, the more access they have. The top-level would be the most confidential, where nearly no one has access unless it is specifically defined.

The middle level might allow certain categories of people to access certain documents based on what they need to do their job. For example, an account executive for financial services may not have access to the bank’s profit and loss information. The bottom level would allow most or all employees to access openly accessible data, such as customer service agents.

Once thresholds are decided, the company’s subject matter experts and data scientists can begin to label various documents in the database according to their level of confidentiality. The company can then use that labeled data to train an algorithm to go through the rest of the database and find commonalities between all of the documents labeled under a certain threshold. The algorithm could then determine which other documents fit those patterns or involve similar topics.

Unified View of the Customer
Another use-case for intelligent search is gaining what vendors market as a unified view of customers. Customer data is often scattered across various data silos and in structured and unstructured formats, such as a history of transactions or a mortgage application respectively.

This makes it difficult for company employees, especially those that deal with customers every day, to know whether or not they have all of the information a company has on a customer when dealing with them. A wealth manager, for example, may have trouble finding all of the information about a client they need to make the best decision for their portfolio.

When we studied the vendor landscape of intelligent search applications in the banking industry, we found that 75% of the products in the space included capabilities for customer information retrieval. The unified view seems to be a point of resonance for banks and financial institutions in customer service and wealth management use-cases.

Example: Call Centers
A unified view of a customer may allow a call center agent to not only pull up a customer’s contact record in a CRM, but also their past emails with the company, call logs on their past phone calls with the company, and, in some cases, sentiment analysis information on these conversations.

As a result, the call center agent would have a better idea of how to deal with the customer; they may learn that an angry customer has been calling in frequently about overdraft fees and decide it’s better to refund the customer for those fees than to allow them to keep calling in to the support line and take up agent time.

In the future, this use-case may evolve into automated coaching for call center or live chat employees. Employees would get recommendations for how to best handle the customer and even what to sell them on. Instead of deciding for themselves whether or not to refund the irate customer, the AI software might recommend this to the employee.

Concept and Advanced Entity Search
A third use-case for intelligent search is the capability to search for broader concepts and phrases as opposed to individual words or entities. Employees could search for documents with more contextual natural language phrases, as opposed to just searching for specific keywords.

For example, an employee could search “angry customers with an account login issue between June and August” into the search application, and the software could present a list of call logs for customers fitting the criteria. Such a capability is useful for finding more information relating to concepts that could appear in various documents scattered throughout a database, especially when those concepts are discussed in tangential ways.

Example: Searching For Documents Related to LIBOR
In banking, the 2021 sunset of LIBOR may have compliance departments scrambling to search for contracts that reference it so that they might update or manage them for a post-LIBOR state of affairs. In many cases, it may still be very simple to find all LIBOR-related documents and update them via strict keyword searches.

However, there may be many documents within a database that contain LIBOR-related discussions that don’t specifically mention any keywords one might normally associate with LIBOR. Employees using traditional keyword-based search software might miss these documents,

Intelligent enterprise search software could help employees find these documents. Subject matter experts could first find documents that appear to only suggest LIBOR-related discussion and label these documents.

Data scientists could then run this labeled data through the machine learning algorithm behind the search software, and this would train the software to pick up on the patterns that tend to constitute LIBOR-related discussion within a document. As a result, employees could type “LIBOR” into the search application, and the software would return LIBOR-related documents that compliance officers would want to stay on top of.

This way, employees do not have to guess which of the results actually reference LIBOR without mentioning it directly, manually reading through documents to find LIBOR-related discussion. Instead, they would search for LIBOR as a concept, and the algorithm would search the enterprise database for entities/phrases related to that concept.

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Becoming Information-Driven Begins with Pragmatic AI

d_schubmehl_m
Written by guest blogger, David Schubmehl, IDC Research Director, Cognitive/Artificial

Intelligence Systems.  Sponsored by Sinequa.

Over the last several years, I’ve spoken to many organizations that have all asked the same question: How can we most effectively make use of all of the research, documents, email, customer records and other information that our employees have collected over the years, especially those that are now retiring? In the past, organizations had corporate libraries and corporate librarians whose job it was to help collect, organize, and disseminate information to employees and staff when and where they needed it. That department and positions are long gone from most organizations today. Why have they gone? The rate of data and documents (including research papers, contracts, and even emails) has exploded, making this task impossible. But let’s be honest: even before today’s information explosion, no classification system could ever keep up with the fast pace of change in the economy. No one could have foreseen today’s most important questions, in content categories that did not exist until today. And with the baby boomers retiring at an ever-increasing rate, an urgent question must be asked: How do organizations get the most value from the vast amounts of information and knowledge that they’ve accumulated over decades?

IDC has identified the characteristics of organizations that are able to extract more value out of the information and the data available to them. Leader organizations make use of information access and analysis technologies to facilitate information access, retrieval, location, discovery, and sharing among their employees and other stakeholders. These insight leaders are characterized by:

  • Strategic use of information extracted from both content and data assets
  • Efficient access to unified and efficient access to information
  • Effective query capabilities (including dashboards)
  • Effective sharing and reuse of information among employees and other stakeholders
  • Access to subject matter experts and to the accumulated expertise of the organization
  • Effective leverage of relationships between information from different content and data sources

So how can artificial intelligence (AI) and machine learning affect information access and retrieval? The types of questions that are best answered by AI-enabled information access and retrieval tools are those that require input from many different data sources and often aren’t simple yes/no answers. In many cases, these types of questions rely on semantic reasoning where AI makes connections across an aggregated corpus of data and uses reasoning strategies to surface insights about entities and relationships. This is often done by building a broad-based searchable information index covering structured, unstructured, and semi-structured data across a range of topics (commonly called a knowledge base) and then using a knowledge graph that supports the AI based reasoning.

AI-enabled search systems facilitate the discovery, use, and informed collaboration during analysis and decision making. These technologies use information curation, machine learning, information retrieval, knowledge graphs, relevancy training, anomaly detection, and numerous other components to help workers answer questions, predict future events, surface unseen relationships and trends, provide recommendations, and take actions to fix issues.

Content analytics, natural language processing, and entity and relationship extraction are key components in dealing with enterprise information. According to IDC’s Global DataSphere model developed in 2018, of the 29 ZB of data creation, 88% is unstructured content that needs the aforementioned technologies to understand and extract the value from it. In addition, most of this content is stored in dozens, if not hundreds of individual silos, so repository connectors and content aggregation capabilities are also highly desired.

AI and machine learning provide actionable insights and can enable intelligent automation and decision making. Key technology and process considerations include:

  • Gleaning insights from unstructured data and helping to “connect the dots” between previously unrelated data points
  • Presenting actionable information in context to surface insights, inform decisions, and elevate productivity with an easy-to-use application
  • Utilizing information handling technologies that can be used in large scale deployments in complex, heterogeneous, and data-sensitive environments
  • Enriching content automatically and at scale
  • Improving relevancy continuously over time, based on user actions driven by machine learning
  • Improving understanding by intelligently analyzing unstructured content

IDC believes that the future for AI-based information access and retrieval systems is very bright, because the use of AI and machine learning coupled with next-generation content analysis technologies enable search systems to empower knowledge workers with the right information at the right time.

The bottom line is this: enabled by machine learning–based automation, there will be a massive change in the way data and content is managed and analyzed to provide advisory services and support or automate decision making across the enterprise. Using information-driven technologies and processes, the scope of knowledge work, advisory services, and decisions that will benefit from automation will expand exponentially based on intelligent AI-driven systems like those that Sinequa is offering.

For more information on using AI to be an information leader, I invite you to read the IDC Infographic, Become Information Driven, sponsored by Sinequa at https://www.sinequa.com/become-information-driven-sinequa/

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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.

keeping secrets - stamp draft

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