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.

 

+1Share on LinkedInShare on Twitter

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.

+1Share on LinkedInShare on Twitter

The Top 5 Business Outcomes for Financial Institutions with AI-powered Platforms

An IDC Infographic Sponsored By: Sinequa

The Top 5 Business Outcomes for Financial Institutions with AI-Powered Platforms

Cognitive search capabilities help financial institutions make the best use of their data, while creating insights that drive business opportunities.

Data Management Challenges

Top drivers of the data explosion in financial institutions:


Data regulations require financial institutions to make data more secure, accessible, and transparent.
Challenging to access and analyze data e­fficiently
88% OF WHICH IS UNSTRUCTURED

Information-Driven Financial Institutions Connect and Contextualize Data Using AI-Powered Platforms

AI-powered search and analytic platforms give knowledge workers the ability to see data in context.

First-generation content analytic tools loaded knowledge workers with the full cognitive burden of manually processing and understanding the meaning of data.
Information-driven organizations can deliver relevant business information across lines of business.

 

 

5 Business Outcomes for Financial Institutions Using an AI-Powered Platform

Keeping Secret Secret
Keep Secrets Secret

Reinforce protection of confidential documents and improve productivity at the same time using natural language processing and machine learning.

 

Generate Service Alpha

As assets shift out of active investments into passive investments, beat the market in investor care with service alpha.

Generate Service Alpha

 

Understand The Customer Ladder
Understand The Customer Ladder

Develop a complete view of the customer at every level of your bank, from the branch to the board.

 

Find Financial Crime

See and connect patterns across many sources, including people, transactions, phone calls, email, and travel activity to find financial crime faster.

Find Financial Crimea

 

Digital Without Disruption
Digital Without Disruption

Minimize the risk of digital transformation and big data projects. Instead, understand data across legacy systems and silos. And turn unmanageable amounts of data into information that can be used to make valuable decisions.

 

Essential Guidance

Support Fact-Based Decisions with AI-Powered Platforms
Digital Without Disruption
Spending on AI technologies will grow to over
$8 BILLION BY 2022

Financial institutions can deliver personalized customer experiences, make quicker decisions, and adapt quicker to regulatory changes.

 




+1Share on LinkedInShare on Twitter