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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Best Practices for Intelligent Search

This article originally appeared as part of a KMWorld Best Practices White Paper on Intelligent Search

Best Practices for Intelligent Search

Sinequa provides an intelligent search platform that enables organizations to become information-driven, which means having actionable information presented in context to surface insights, inform decisions, and elevate productivity, consistently and reliably. Our platform consists of packaged technology that allows this to happen quickly and without sacrificing context or quality as typically happens with “lossy” approaches involving data migration.  (more…)

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Sinequa Wins Best NLP Platform in AI Breakthrough Awards

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It’s been a busy few months at Sinequa, and we are ending June on a high note having been selected as the Best Natural Language Processing (NLP) Platform in the AI Breakthrough Awards!

The AI Breakthrough Awards recognize today’s top companies, technologies and products in the artificial intelligence industry. Over 2,500 nominees have participated from all over the world. (more…)

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How organizations can evolve from data-driven to information-driven

This article was originally published on Information Management.

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.
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GDPR Investments for Compliance AND for Competitiveness

This article was originally published in Database Trends & Applications.

The deadline looms on the horizon. On May 25, 2018, the European Union will enact some of the most stringent data privacy regulations the world has ever seen. These regulations will impact thousands of companies around the world, not only EU-based organizations but any company that collects or processes personal data on EU residents. The General Data Protection Regulation (GDPR) recognizes the “fundamental right” of people to control what data is stored about them and how it is used.

GDPR Investments for Compliance AND for Competitiveness

Organizations must be ready for this date since the fines for non-compliance could be as high as 4% of annual revenue or $21 million, whichever is higher. To put this in perspective, small companies could go out of business with a $21 million fine, and for a company with revenue of $10 billion, the fine could be a staggering $400 million.

No organization with large datasets can sift through them manually to find personal data and judge its GDPR compliance. Companies need sophisticated technology to deal with their data effectively, enabling them to search, discover, and review. Most organizations find it challenging to quickly and accurately identify and find personal data.

Under GDPR guidelines, people can request to be informed about the data that organizations store about them and can demand rectification, erasure, or the restriction of how their data is used. They can also ask to receive their personal data in a common format that allows them to transfer it to another organization.

The impending deadline and the fear of painful fines put organizations under a great deal of pressure, such that they may forget about pursuing the potential business benefits of conformity measures. For example, the prospect of thousands or even millions of people demanding to know what data is stored about them may seem daunting. Since an organization is obliged to answer within 30 days, this might result in thousands of cases per day being handled by customer service.

On the other hand, many large enterprises with millions of individual customers—banks, wireless providers, etc.—need to provide a 360-degree view of a customer to their sales and service personnel—in seconds, not in a month. This is a business requirement independent of GDPR compliance. When customers contact the company, they expect the sales or service reps to know them and give them knowledgeable recommendations and advice.

One way of providing such a 360-degree customer view is using cognitive technologies that can ingest structured data from enterprise applications such as CRM and billing and unstructured data such as emails and other correspondence. Companies often have hundreds of such data sources. Cognitive capabilities, such as natural language processing and machine learning, are necessary to extract relevant information from structured and unstructured data: what kinds of contracts the organization has with customers; service and payment history; whether the latest exchanges were friendly or aggressive; suggestions from past experience with other customers to help solve the current customer’s problems; etc.

In a call center, operators need to get a complete picture of the person on the line within less than 2 seconds, according to industry standards. If a company has 20 million customers, more than 200 enterprise applications with customer data, and 10,000 call center agents, that is a daunting challenge—but a challenge that has been successfully overcome by companies.

ROI: BUSINESS BENEFITS—NOT JUST COMPLIANCE
Gartner estimates that European companies will each spend an average of 1.3 million euros to comply with GDPR personal data protection requirements while U.S. businesses are setting aside at least $1 million for GDPR readiness, with some assigning up to $10 million. What do they get for it, apart from avoiding fines?

Let us look at a concrete example of a wireless telecom company that implemented a 360-degree view strategy using cognitive technologies. The first objective of the project was reduction of average call handling time, increased customer satisfaction and loyalty, and increased up- and cross-selling. All these goals have been achieved, but there is another aspect to the project that offered massive savings: Call center employees now have a unique and intuitive user interface to access customer data.

They no longer need to understand some 30 enterprise applications they had to navigate before to access this data. This reduces the need for training from 30 days to 1 day. With 10,000 employees and a turnover rate that often approaches 50%, that means 5,000 x 29 workdays saved per year, i.e., 145,000 workdays or 29,000 person-weeks. ?The company can certainly offer a lot of customer service during that time! The overall ROI of the project would be approximately 60 million euros over ?3 years.

NEW PARADIGM: CUSTOMER SELF-SERVICE FOR INFORMATION RETRIEVAL
One of the 10 biggest banks in the world has implemented a similar project to provide a 360-degree view of customers to its customer-facing employees. Its objective from the outset was also to provide their customers a 360-degree view of their own dealings with the bank: accounts, share deposits, insurance contracts, etc. It is easy to extend this interface to answer the question, “What data does the company have on me?” In this way, the company improves its service to customers and fulfills its GDPR obligations without a single employee being involved.

GDPR is coming, but instead of seeing it only as a costly burden, organizations should view the regulation as an opportunity. By implementing advanced cognitive technologies to derive deep customer insights, organizations can ensure compliance while reaping the business benefits of greatly improved customer service that can have a tremendous impact on the bottom line.

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