Les Misérables and the Digital Workplace

When Optimizing Data Access Shows Soft and Hard ROI

When Optimizing Data Access Shows Soft and Hard ROIs

Les Misérables? Ring a bell?

Of course! This is a famous book by Victor Hugo, and the story is amazing! But what does it have to do with the digital workplace? Let me focus on a specific quotation and comment on similarities with the digital workplace.  It occurs in the chapter where Jean Valjean and Cosette are residing in a house with a garden.  In that part, Victor Hugo explores the multiple dimensions of nature.  What caught my attention is the following question: “Where the telescope ends, the microscope begins.  Which of the two has a grander view?”  The quotation resonated in my mind as it evokes similarities with the digital workplace, particularly in reference to data access.  For large and diverse content, having relevant and timely information is critical to companies.  There are different methods to query the data and the kind of ROI that can be expected varies by orders of magnitude.

The telescope – see far into the universe

What does it mean for the digital workplace? This means breaking internal data silos and opening up global information to your entire organization (any information shared by all, such as policies, procedures, HR information, compliance, etc.).  Having a digital workplace that includes an enterprise search layer that connects people to corporate content is therefore critical.  Every employee can see beyond its reach and access data spread over a wide range of different repositories.  This data is made available to everyone and everyone stays informed.

Such use of enterprise search does not bring a high degree of business specificity.  This is typically a Google-like experience with a simplified interface that is used indifferently by marketing, sales, engineering, or accounting people – any employee.  Working across business units to address multiple audiences (a horizontal approach) – its value can be uncovered by helping a large number of employees to find information; the ROI (Return on Investment) is based on an overall improvement of the company’s productivity.  According to McKinsey, employees spend close to two hours per day search for information.  In addition to increased productivity, such employee empowerment also has positive impacts on a company’s culture and employees’ wellbeing.  This is what we call a soft ROI.  A soft ROI is not easy to measure and rely on in a business case.  Benefits are referred to as indirect.  Having said that, some dollars savings can be estimated through productivity gains.  The main assumptions include the number of employees,  the average salary, and the percentage of working time saved thanks to a simple information finder.  A summary of an ROI that was calculated for a company comprising of 30,000 employees can be seen below.

ROI of Search for Digital Workplace

Assumptions were made regarding user adoption ramp-up schedules, with a greater number of users and a higher efficiency over time.  The ROI in this example is close to 13 million dollars over 3 years.

The microscope – explore what is next to you

How would this translate for the digital workplace? This ability would indeed be very helpful to assist intensive-knowledge workers in their daily tasks.  The term “knowledge worker” was first coined by Peter Drucker who defined knowledge workers as high-level workers who use advanced data collection techniques, statistics, complex correlations, case studies, and a lot more.  Data is key in helping them to perform their jobs.  And guess what? Enterprise search technology can also help in such a context.

As opposed to the simple Google-like experience, the objective here is to design a “Search-based application” customized with business-specific knowledge.  The value resides in the ability to follow a targeted business function along the key phases of its work.  Only enterprise search can index and aggregate very diverse data coming from both structured and unstructured content in order to extract the nuggets of information and provide a unified view on a specific topic (product, customer, company…)  For example, for a bank advisor, it is critical to aggregate internal data such as payments, information from the CRM, transaction history as well as external data, such as market analysis and news, to recommend the most relevant products to a customer.  The ROI is no longer related to a high number of people but to clear business-process improvements.  To do so, we target a precise group of knowledge workers on a designated use case in a specific vertical, a tryptic of “industry, use case, persona.”

Let’s take the example of clinical trials with a large pharmaceutical company.  Clinical trials are research studies that are aimed at evaluating a new drug.  They are the vehicles for evaluating a new drug.  They are the primary way that researchers find out if a new treatment is safe and effective.  In that case, the tryptic mentioned previously would then be “pharmaceutical, clinical trials, researchers.”  A specific “Search-based application” has been designed to dive into clinical data dispersed across millions of files and multiple systems and applications, surfacing insights to support the evaluation of new drugs.  The enterprise search technology had increased speed to market for new drugs.  Knowing that in the pharma industry, the average cost of new drug development is $1.0 billion, any slight improvement in the global process immediately gives better margins leading to bottom-line improvement.  This is what we call a hard ROI.  This type of ROI refers to clear measures that can be quantified in hard dollars.  To give you a flavor of the way the above pharmaceutical company calculated the ROI, you’ll find below some of the assumptions that were made (for your information, clinical trials include 3 main phases):

  • 10% to 14% of all drugs that make it to phase 1 succeed
  • 31% of all drugs that make it to phase 2 succeed
  • 50% of all drugs that make it to phase 3 succeed
  • 32% of drugs make it to phase 3
  • Average trial costs- phase 1: $170m; phase 2: $400m; phase 3: $530m
  • The cost of a trial is between $800m and $1.8b
  • The cost of patient/site recruitment averages $40k per patient/site

Locating key data and deriving insights is a key success factor for researchers.  The “Search-based application” has increased efficiency, shaving months off drug development timeline.  According to this large pharmaceutical corporation, the ROI realized is 25 million dollars per drug.

So, which has the grander view- the telescope or the microscope?

Both reveal worlds that are normally hidden from view.  For the digital workplace and data access, you require them both.  Accessing the right information at the right time is becoming ever more complex, and there are many factors with the potential to make it even more complicated.  Either for corporate content or business-specific data, enterprise search can help with both dimensions.  The ability to retrieve a company’s data assets and provide actionable insights in order to make informed decisions is indeed vital for business efficiency.  By applying methods and technologies, you can be sure that “Even the darkest of night will end and the sun will rise.” Another quote from Les Misérables.

Digital Workplace telescope vs microscope

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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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Big Projects on Track: Achieving the Goals of Long, Complex Projects

big-track-manufacturing-06-2019-1024Big projects, well executed, are the lifeblood of large, distributed manufacturing organizations.

Such projects solve existing and future problems that enable the organization and its stakeholders (and sometimes all of society) to move forward economically. These projects are naturally chaotic and require significant organization and planning to manage the chaos. Successfully executing these projects also means bringing together the right people and making it easy for them to collaborate, share ideas and provide inspiration.Today’s large, distributed manufacturing organizations cannot successfully plan and execute big projects without intelligent automation to help connect project stakeholders to relevant information and to each other.

Download the Big Projects On Track solution white paper to learn how one of the largest rolling stock manufacturers in the world addressed this challenge.

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How Biopharmaceutical Companies Can Fish Relevant Information From A Sea Of Data

This article originally appeared in Bio-IT World

Content and data in the biopharmaceutical industry are complex and growing at an exponential rate. Terabytes from research and development, testing, lab reports, and patients reside in sources such as databases, emails, scientific publications, and medical records. Information that could be crucial to research can be found in emails, videos, recorded patient interviews, and social media.

school-of-fish

Extracting usable information from what’s available represents a tremendous opportunity, but the sheer volume presents a challenge as well. Add to that challenge the size of biopharmaceutical companies, with tens of thousands of R&D experts often distributed around the world, and the plethora of regulations that the industry must adhere to—and it’s difficult to see how anyone could bring all of that content and data together to make sense of it.

Information instrumental to developing the next blockbuster drug might be hidden anywhere, buried in a multitude of silos throughout the organization.

Companies that leverage automation to sift through all their content and data, in all its complexity and volume, to find relevant information have an edge in researching and developing new drugs and conducting clinical trials.

This is simply not a task that can be tackled by humans alone—there is just too much to go through. And common keyword searches are not enough, as they won’t tell you that a paper is relevant if the search terms don’t appear in it, or if a video has the answer unless the keywords are in the metadata of the video.

Today, companies can get help from insight engines, which leverage a combination of sophisticated indexing, artificial intelligence, and natural language processing for linguistic and semantic analyses to identify what a text is about, look for synonyms and extract related concepts. Gartner notes that insight engines, “enable richer indexes, more complex queries, elaborated relevancy methods, and multiple touchpoints for the delivery of data (for machines) and information (for people).” A proper insight engine does this at speed, across languages, and in all kinds of media.

For biopharmaceuticals, this is particularly powerful, allowing them to correlate and share research in all forms over widely distributed research teams. Here are several ways biopharma companies can use insight engines to accelerate their research.

Find A Network Of Experts

Many companies struggle to create the best teams for new projects because expertise is hidden in large, geographically-distributed organizations with multiple divisions. A drug repositioning project might require a range of experts on related drugs, molecules, and their mechanisms of action, medical experts, geneticists, and biochemists. Identifying those experts within a vast organization can be challenging. But insight engines can analyze thousands of documents and other digital artifacts to see who has experience with relevant projects.

The technology can go further, identifying which experts’ work is connected. If they appear together in a document, interact within a forum, or even communicate significantly via email, an insight engine can see that connection and deduce that the work is related. Companies can then create an “expert graph” of people whose work intersects to build future teams.

This technique can extend beyond the borders of the company, helping to identify the most promising collaboration partners outside the company in a given field, based on publicly available data, such as trial reports, patent filings and reports from previous collaboration projects.

Generate R&D News Alerts

Biopharma companies can also use insight engines to watch for new developments in drug research and stay on top of the latest trends. These news alerts can go beyond typical media sources to include scientific publications, clinical trial reports, and patent filings.

This capability can be used on SharePoint, Documentum, or other sources within a large company to surface relevant information. An insight engine ensures the right information gets to the right people in the right context, and in a timely way.

Optimize Clinical Trials

Clinical trials that stretch over many years generate millions of datasets for every drug and study provide a treasure trove of data. Biostatisticians can ensure they get a comprehensive list of patients having certain diseases within trials on a drug, something nearly impossible with traditional methods.

They can also search and analyze across many drugs and studies, across content and data silos. Over time, this allows biopharmaceutical companies’ growing number of clinical trials to become a valuable asset that can be easily leveraged across a growing number of use cases.

All of these uses can lead to biopharma companies developing new drugs more quickly and getting them to market faster—necessary as these companies face tremendous pressure to innovate quickly and develop new promising drugs as patents for older drugs expire. With insight engines, they can make every part of the journey more efficient, from research, to clinical trials, to regulatory processes, presenting incredible opportunities for everyone in this field.

 

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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.

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