Working Effectively while Working Remotely with Enterprise Search

COVID-19 Work From Home

The working world is experiencing an unprecedented spike in remote work. “We’re being forced into the world’s largest work-from-home experiment and, so far, it hasn’t been easy for a lot of organizations to implement,” says Saikat Chatterjee, Senior Director, Advisory at Gartner. “In a recent webinar snap poll, 91% of attending HR leaders indicated that they have implemented ‘work from home’ arrangements since the outbreak, but the biggest challenge stems from the lack of technology infrastructure and lack of comfort with new ways of working.”

At the center of these challenges are employees not having a consistent and reliable way to reach the information they need to be well-informed. In some organizations, this is happening quickly and even starting to threaten business continuity, especially as more employees begin to rely on the digital workplace to be productive.

Enterprise Search in the Digital Workplace

Any knowledge-intensive organization of significant size probably has a digital workplace that includes what could be referred to as enterprise search (even if they don’t call it that). Maybe they downloaded an open-source kit that provides employees with a rudimentary way to query across sources using keywords. Or maybe they’ve chosen the ecosystem of a large technology company like Microsoft, Google, or IBM, which tend to exclude content and data stored outside of the ecosystem.  Now, faced with a sudden surge in the importance of quickly accessing essential information, regardless of its source or format, companies are realizing that these solutions fall short.

Regardless of the initial path chosen, there are some fundamental requirements that must be seriously considered to maximize the value of an enterprise search investment. These requirements include the following:

  • All enterprise content and data across time, locations, and languages must be securely available for employees to access without the need for risky data migration projects
  • Data security and access control must be rigorously enforced by default
  • Relevance and information accuracy are a must for users to do their work properly and swiftly. This requires different types of linguistic analysis, preferably provided out-of-the-box to save time in implementing enterprise search.
  • Classification-by-example powered by machine learning algorithms must also be available out-of-the-box for scenarios where a rules-based approach does not suffice
  • The user interface must be flexible and agile to support solutions for multiple use cases across the organization

These capabilities provide significant benefits for employees in the digital workplace in several different ways. Let’s take a look at some of the key benefits.

Employee Productivity

Having a robust enterprise search solution in place allows employees to quickly find the document, content, and information they are looking for, rather than spending time trying to contact other employees and disturb everyone’s workflow. This enables people to save crucial time, which can be channeled into more productive work.

Knowledge Sharing

According to data collected prior to the current spike in remote work, Fortune 500 companies were already losing roughly $31.5 billion a year by failing to share knowledge. Much of this “hidden” knowledge could be extremely useful in providing new hires with information that is not widely known by other employees within the organization.  Making sure this knowledge is explicit and findable lays a foundation for a much more efficient workforce.

Enterprise search enables organizations to surface the know-how and experience of senior managers so that the knowledge of the organization does not remain hidden when the employee leaves.  With an enterprise search solution in place, your current or future employees can easily access this information and continue doing their work with ease.

Information Access

It’s difficult to know with any certainty how much productive time employees are leaving on the table just because they cannot find the desired information or content they are looking for.  According to a benchmarking survey done by the folks over at IntraTeam, users within only 25% of organizations surveyed are satisfied with the internal search functionality.  And that was before everyone was suddenly displaced from their offices and forced to use online tools for the majority of their work.

Having a robust digital workplace structure in place means easy access to information. Enterprise search in the digital workplace provides a central place to look for all files, documents, presentations, spreadsheets, weblinks, and other rich media. This makes it extremely easy for team members, irrespective of their location to access information from any device quickly.

Competitive Advantage

Consistently well-informed employees can also provide better service to customers and offer better turnaround times. Since they are saving a lot of time, they can focus on the things that really matter and contribute to the business’s success more effectively.

Summary

The old phrase “Make hay while the sun shines” reminds us to make the most of our opportunities while we have the chance. In the current world health climate, with travel restrictions becoming more prevalent and events being canceled or postponed, now might be the ideal time for organizations to invest in tools and technology that directly drive operational efficiency. The positive impacts in terms of business continuity, cost savings, and employee empowerment can be enormous.

 

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Enterprise Search Development: Start With the User Interface

This article was originally published on CMSWire.

Enterprise Search Development: Start With the User Interface 

By Martin White | Mar 10, 2020

sinequa-screenshot-enterprise-search

Start with the user interface (UI) and work backwards. That was the advice I shared with search managers developing their existing application or planning a new application during an enterprise search workshop at the recent IntraTeam event in Copenhagen, Denmark. Sinequa recently sent me some examples of user interfaces from its customers (thank you Laurent Fanichet), which showed the variety and inevitable complexity of enterprise search UIs. Too often businesses make a deliberate choice on the technology and give little thought to the UI (so much for the ethos of user-centric delivery!).

The topics outlined below cannot be left to the implementation stage. Most search applications (with the obvious exception of Office 365) are UI neutral and can support almost any UI development language. Early work around these topics is essential, even at the specification stage, to ensure the investment is fit for purpose, not just to specification.

Metadata

Enterprise information collections are much larger than might be imagined and inevitably contain many near-identical documents. HR and related corporate policies are just one example of this. So delivering the “most relevant” document in response to a query is limited at best.

The rhetoric of personalization through AI usually fails to deliver for two reasons: First, it assumes the user is seeking the information for themselves. Second, AI works on the basis of prior searches, but many of the searches will be by people who are new to an organization or role.

sinequa-screenshot-patent-miningProvide users with filters and facets so they can refine a set of results. But keep in mind, providing filtering just by file format and last revised date is a waste of screen space. Ask people how they might want to filter (e.g. country, date of publication, department, language). With that valuable information in hand, work out how the metadata to drive these filters is going to be derived — either from the text of the document, through tags or a combination.

Snippet Options

Quite a lot of work has been undertaken into the format of snippets. One size does not fit all. This is especially the case in enterprise search where the primary assessment of results is through information foraging. The format of the result and what ancillary information can be switched on or off by the user is important to consider. For some searches an expanded snippet with highlighted query terms might be invaluable, but this will limit the number of results displayed per page.

Usability

Designing search pages that scroll is a seriously bad idea. Even if the results are scrolled, the ancillary filters and facets will remain stationery, and in any case people will want to see the results in the context of a page of results. When usability testing happens later in the project, it will start with a discussion about which elements of the UI have been the subject of continuing discussion without a clear resolution and need real-life testing.

Accessibility

In the digital workplace, accessibility is very important as there will be few workarounds. At the outset you should be working with accessibility consultants to consider how voice browsers will work with the proposed UI and what the implications are for staff on the dyslexia spectrum.

Federated Search/Multilingual Search

The current interest in presenting the results from multiple repositories seems to ignore the challenges in how to present the final results. When there are only two applications (or languages) then two windows might be the best option, but as the number increases so does the complexity of the user interface. This becomes even more acute when results from text searches need to be interleaved with results from enterprise databases.

Training and Support

No matter how well you design a user interface, enterprise search is never going to be intuitive. This is due to the variable quality of the content and the metadata and the wide range of queries. Any discussion about a search UI has to take into account the extent to which training might be required for one or more aspects which will be a challenge to use.

To read the full article please visit https://www.cmswire.com/information-management/enterprise-search-development-start-with-the-user-interface/

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Sinequa is Proud to Announce the Opening of its New Office in the Big Apple

Sinequa US Expansion

Earlier this year, Sinequa expanded to a bigger space for its North American headquarters in New York City. The move, which happened in late January, extends Sinequa’s office space from 3,665 square feet to over 8000 square feet, steps away from the legendary Madison Square Garden and major transportation hub, Penn Station.  “Additional space was necessary to meet the needs of a rapidly growing team and extended pipeline of clients,” commented Xavier Pornain, Sinequa’s VP of Sales, NA who is charged with leading the office and Sinequa’s North America growth strategy.  The new location will be the company’s third move since expanding its reach to the North American market in late 2014.

To celebrate the grand opening Sinequa’s, CEO Alexandre Bilger, and COO Fabrice de Salaberry, flew in from Paris to christen the office with champagne, confetti and a few rounds of bonzini foosball.

Sinequa is dedicated to strengthening its competencies and expertise across North America to address the diverse needs of Enterprise Search among its existing fortune 500 clients and beyond.  For more than 18 years, Sinequa has been a leader in developing a next-generation Enterprise Search platform that turns data (both structured and unstructured) into information and insights necessary for organizations to become “Information-Driven.”

“I’m very excited to see our office flourish and grow. The new office comes with lots of conference rooms to meet with customers and partners with plenty of natural light that makes it a great working environment. In addition, it shows our commitment to the U.S. market while accelerating our growth and expansion,” stated Laurent Fanichet, VP of Marketing.

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Insight Engines in Wealth: How to Build Tomorrow’s Opportunities Today

Insight Engines in Wealth

McKinsey feels pessimistic. In their recent report, On the cusp of change: North American wealth management in 2030McKinsey forecast the future of wealth management. It’s a useful, thoughtful report. But you don’t have to wait until 2030. Most of the opportunities they sketch can be built today, with an insight engine.

Unsurprisingly, McKinsey provides a useful framework to think about the future of wealth management. They ask three big questions:

  • What will happen to advice?
  • What will happen to advisors?
  • What will wealth management firms do?

Insight engines — available today — can help provide answers to several of these questions. For context, I will explain insight engines briefly, covering their origins and what they do. Then, we can move on to explore how insight engines apply to wealth management today.

Insight engines: enterprise search evolved

Insight engines are enterprise search evolved. Gartner retired the category of enterprise search in 2016. In 2017, they unwrapped Insight Engines to reflect the profound changes in customer needs and technology capabilities.

Insight engines differ from enterprise search both in what they offer and the technologies used. In their inaugural 2018 report, Gartner highlights how Insight Engines are different:

Insight engines are distinguished by their capability to deliver insights in context to the right person, in the right place, at the right time.”

And they explain how the underlying technologies differ as well:

“These capabilities stem from the use of artificial intelligence (AI) technologies, specifically natural-language processing, graph-based data structures, and machine learning.”

Sinequa, a provider of insight engines to financial institutions, has been a leader in Gartner’s Magic Quadrant for Insight Engines since the category began.

Sinequa evolves enterprise search and insight engines even further. Coupling two decades of research in natural language processing with the latest deep learning approaches means users get immediate, relevant, auto-improving answers to their questions. Users have a complete view of customers or products or risks or contracts or deals all within a single view, created instantly from the most up-to-date content.

Advice

On advice, McKinsey makes three predictions:

  1. Hyper-personalized advice model built on data and continuous access.
  2. Bite-sized “fit-nance.” This means developing a granular ability to track customer investments, education, retirement, and broader financial wellness.
  3. Big tech will capture a large share of industry economics by providing core technology infrastructure.

The best investment advice comes from distilling mounds of data down into recommendations tailored to the client’s risk appetite and return objective. Sinequa’s Insight Engine delivers the investment insights required. The platform can search across all data sources including internal and external, cloud and on-premise, along with structured and unstructured data. Sinequa simplifies assessing financial wellness by providing a unified view of client assets and liabilities, irrespective of where the data is stored.

Advisors

For advisors, McKinsey thinks their working lives will change in three ways:

  1. Advisors remit expands to provide coaching on broader wealth and life issues. And McKinsey expects the industry to shed a fifth of its total advisors.
  2. The face of the advisor will become much more diverse, spanning increased numbers of women, minorities, and mid-career changers.
  3. User ratings will become ubiquitous, making advisor performance transparent.

Increasing advisor productivity remains a perennial challenge. Things will get worse as the current generation of wealth advisors retire. Routine work needs automating, so advisors can focus on adding value through relationship management and advice. Sinequa’s Insight Engine augments wealth advisors by saving their time foraging for data. And it applies decades of R&D in natural language processing, so advisors don’t have to read reams of documents.

Wealth management firms

McKinsey expects wealth management firms to have to make the most changes:

  1. Industry talent becomes more digital as wealth firms function as technology platforms.
  2. Several-at-scale firms will serve everyone while the rest will focus on providing differentiated service to ultra- and high-net-worth clients.
  3. Operational excellence will be required to protect margins from increasing transparency and falling fees.
  4. Integrated banking-wealth management ecosystems will emerge.

Insight engines can help wealth management survive and succeed in several ways:

  • Accelerate wealth firms build-out of their technology platforms with reduced risk using Sinequa’s multi-use-case Insights Engine.
  • Provide a unified view of clients to provide differentiated service to the extreme expectations of ultra- and high-net-worth clients.
  • Achieve operational excellence by applying All the AlphasHistorically, the wealth management industry has over-focused on the most transient of the alphas – the quest for above-market returns or investment alpha. However, this has resulted in overlooking the value hidden inside other internal functions, such as distribution and service. Delivering exceptional performance (alpha) in these functions can create competitive advantages more durable than investment alpha.
  • Find information and insight across any ecosystem, irrespective of the type, number, or location of ecosystem partners.

If you work at a wealth management firm and would like to learn more about how you can build tomorrow’s opportunities today, please attend one of our briefings.

Here’s how it works. You choose how much time you want to spend and where you want to spend it. We have an Executive Briefing Center on West 30th in New York City or in Paris or we can come to your office. We customize each briefing to your objectives and business challenges. We’ll start the briefing sharing our perspectives on insight engines in financial engines, learn more about your business, and discuss topics tailored to you. To arrange a briefing, please contact us at info@sinequa.com and add the subject line “Wealth Briefing.”

 

 

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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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