Analytics and Big Data – Copyright Clearance Center Rights Licensing Expert Wed, 21 Mar 2018 14:25:06 +0000 en-US hourly 1 Analytics and Big Data – Copyright Clearance Center 32 32 Machine Learning: Understanding the Difference Between Unstructured/Structured Data Tue, 20 Feb 2018 08:42:48 +0000 For us non-scientists, machine learning can be a confusing concept. The first step is to identify the difference between unstructured and structured data.

The post Machine Learning: Understanding the Difference Between Unstructured/Structured Data appeared first on Copyright Clearance Center.

We know machine learning has the potential to transform the workflows of pharma and biotech organizations looking to turn content into smart data, improve patient safety and increase drug development. But for those of us who aren’t scientists, and don’t work with machine learning on a regular basis, the concept can be confusing. What is machine learning, and how does it fit into our everyday processes?

In CCC’s Beyond the Book podcast, we spoke with Lee Harland, SciBite’s founder, about the role of humans in big data.

Listen to the podcast below, or check out our summary:

Humans In The Age of Big Data

As a first step in the machine learning process, we need to assess our two data types: structured and unstructured. In the world of machine learning, unstructured data is not only critical, but also the more challenging piece of the puzzle.

Structured Data – Think of a Spreadsheet

When thinking about structured data, envision a spreadsheet.  When a person looks at a spreadsheet that’s full of numbers or other data, he or she is typically able to understand the significance of the measurements by reading the data in the chart.

Computers, generally, can understand this data, too.

Unstructured Data – Think of a Text Document

For computers, understanding a text document is far more difficult than understanding a spreadsheet. Instead of being able to conceptualize what a word means, computers see strings of letters. This is an example of data that is unstructured.

“If a computer sees the letters M-O-U-S-E, it doesn’t know it means mouse, and it doesn’t know if that’s referring to an animal, to a rodent, and or if it relates to any other document that mentions other types of rodent,” Lee explained.

At SciBite, scientists take this unstructured data, and turn it into more structured information.  When unstructured text data is presented in a structured way, the goal is for computers to be able to understand:

Aha! This document is about a mouse, a rodent!

Once the computer can understand this, it opens up the possibilities for “exciting stuff” that couldn’t be done with raw documents. (Here are a few examples of the “exciting stuff” machine learning is helping the industry accomplish.)

Don’t Forget Data Quality

When analyzing structured text, data quality is critical to the performance of machine learning algorithms.  At SciBite, the mission is to solve what Lee describes as the “garbage in/garbage out problem.”

Today, there are several different approaches to taking raw documents and throwing them into machine learning algorithms. While this isn’t an invalid way forward, data quality will be better if you’re working with structured data.

“If you’re putting lots and lots of random data into machine learning [algorithms], it’s good, but it may not be that good,” Lee said. “Whereas, if you can go a little bit further and pretreat your data so that it’s a bit more structured, a bit more organized, and then feed that to these algorithms, we’ve seen time and time again with our customers that these algorithms start performing much better.”

Simply put: The quality of what you get out is directly related to the quality of what you put in.

Ready to Learn More? Check out:

The post Machine Learning: Understanding the Difference Between Unstructured/Structured Data appeared first on Copyright Clearance Center.

]]> 0
How Enterprise Data Science Has the Potential to Impact Life Sciences Tue, 12 Dec 2017 07:18:16 +0000 How can life sciences organizations transform large volumes of complex data into hypothesis-generating knowledge representation? Enterprise data science.

The post How Enterprise Data Science Has the Potential to Impact Life Sciences appeared first on Copyright Clearance Center.

Enterprise data science is a multidisciplinary approach to data analysis that transforms large volumes and varieties of complex data into hypothesis-generating knowledge representations such as graphs. Of particular interest are strategies to extract and integrate relevant features and relationships from unstructured, unstandardized bulk data, such as draft and published texts.

In life sciences, this requires the integration of expertise from several disciplines such as molecular biology and organic chemistry, statistics and machine learning, computer science and systems engineering.

Life science companies accumulate vast amounts of textual data: laboratory protocols and observations, experiment outcomes, grant and patent applications, conference abstracts, pre- and post-publication research articles. Additionally, companies collect and curate structured textual data in the form of dictionaries and ontologies. The quality of these data varies a lot and is often stored in various formats across multiple systems. Taking advantage of this information requires a robust, scalable, and adaptable pipeline of automatic and semi-automatic approaches that:

  • collect and pool unstructured as well as structured data;
  • ensure data coherency through validation and filtering, standardization, abstraction, and integration of the inputs;
  • transform and merge unstructured inputs into structured features and define relationships between them;
  • iteratively improve the process and its output through cross-validation and the addition of new data sources.

Enterprise data science pipelines require application of advanced analytical methods to large datasets in real time, and thus rely on scalable computational platforms. Although recent advances in machine learning, cloud computing, and big data made these approaches a reality, realization of these data systems requires deep knowledge of the data sources and practical knowledge of machine learning algorithms. A lot of work is currently directed at developing ways of making enterprise data science less opaque and more practically available to users of all levels.

Ready to learn more? Check out:

The post How Enterprise Data Science Has the Potential to Impact Life Sciences appeared first on Copyright Clearance Center.

]]> 0
What is Value Data and Why Do Information Managers Need It? Mon, 20 Nov 2017 07:31:26 +0000 Being armed with value data makes justifying R&D content spend significantly easier. Here are some types of value indicators to consider.

The post What is Value Data and Why Do Information Managers Need It? appeared first on Copyright Clearance Center.

Information managers have been using usage statistics for years to determine what content researchers consider important. While quantitative analytics are extremely important, standalone usage statistics are only the starting point. Data needs to tell a story that goes beyond numbers, and provide a more precise picture of what users are most interested in – and which content supports overarching business initiatives.

If you’re just beginning to gather and analyze data to inform your content strategy, it’s important to first assess your information center’s goals and set key performance indicators to measure against. Once you’ve determined your goals and KPIs, you’ll need to analyze and share with stakeholders.

This excerpt below from our new white paper, Tell Your Information Center’s ROI Story Through Data Visualizations, highlights the importance of value data when justifying your R&D content investments.

What is Value Data?

Information managers know that sometimes usage and spend data is not enough. There are many cases where cost is high and use is low, but the content is vital to the organization. Unfortunately, when budgets are flat or on the decline, this narrative won’t be enough. You need to be able to prove the value of content beyond usage metrics.

Here are some types of value indicators to consider:

  • What is the impact of usage on the research pipeline? For example, was a publication used heavily during early research? Does the usage of a specific business unit have a significant impact on one pipeline stage? This information will allow you to understand more than just sum usage numbers, but also the value of the content in helping the company bring products to market.
  • What are users searching for and are those searches aligned with organizational goals? If you know what users are looking for or what they will be focused on, you can make the case for content in those focus areas. It also helps you identify new content needs and gaps in coverage.
  • Could this content impact different areas within the organization? When different groups use the same content for various purposes, it’s easier to prove its value. For example, content that is used by marketing, research and regulatory groups may have higher value because its benefit can be seen across the organization.

Think about it like this: If a small group of researchers is interested in an obscure journal, you may wonder if it’s worth the investment. If you dig deeper and learn that in part through that journal’s research, a breakthrough discovery was made, the value of that subscription skyrockets. Being armed with these types of insights make justifying content spend significantly easier.

Ready to learn more? Check out:

The post What is Value Data and Why Do Information Managers Need It? appeared first on Copyright Clearance Center.

]]> 0
Enterprise Data Science: What It Is and Why It Matters Tue, 07 Nov 2017 08:52:56 +0000 The ocean of digital assets available to the modern enterprise contains an enormous amount of untapped business value. The ability to exploit these digital assets could prove to be the single most important differentiator between leaders and laggards over the next decade.

The post Enterprise Data Science: What It Is and Why It Matters appeared first on Copyright Clearance Center.

The typical enterprise is inundated with data. From scientific literature to technical reports, from laboratory notebooks to news reports, from structured databases to domain-specific lexicons and ontologies; the volume of relevant data is increasing year-over-year. Much of these data are siloed and uncategorized, making them inaccessible.  When accessible, often these data are not indexed, making them hard to discover even with authorization to access. While the curation of these data requires a sophisticated approach, which can be costly, the untapped business value which proper programs will expose is far greater.  Regardless of industry, the ability to systematically exploit digital assets will prove to be the single most important differentiator between leaders and laggards over the next decade.

The Digital Transformation Journey Called “Enterprise Data Science”

For that reason, many organizations are exploring a digital transformation journey that I call enterprise data science. Enterprise data science goes beyond big data initiatives and takes advantage of recent advances in machine learning algorithms and cloud computing infrastructure, to extract all possible knowledge (i.e. actionable information) from the digital assets of an enterprise and use it as a driver for change and value creation throughout the organization. The term “enterprise” differentiates and distinguishes this strategic approach from the field known as “data science”, which is currently limited to the application of machine learning and statistics on a case-by-case basis, rather than a holistic approach that aims to maximize the value of digital assets across the enterprise.

There are many benefits to this approach, ranging from operational efficiencies to identifying new opportunities. Enterprise data science can accelerate knowledge discovery and facilitate its diffusion across the enterprise. That, in turn, can lead to substantial value creation.

Almost ten years ago, Kirit Pandit and I wrote the book “Spend Analysis: The Window into Strategic Sourcing“. Therein, we described how a systematic approach for analyzing expenses can “cut millions of dollars in costs, not on a one-time basis, but annually—and to keep raising the bar for the procurement and operations teams to squeeze out additional savings year-over-year.” The concept of enterprise data science is a generalization of that approach, accelerated and enhanced by the tremendous advances that took place in cloud computing and machine learning over the past ten years.

Here is how enterprise data science and the approach referenced above relate. The main challenge in spend analysis is the consolidation of expense records into a single system in a way that one can discover “the truth, the whole truth, and nothing but the truth.” The truth, of course, being the precise amount of expenditure, for a given commodity, for a given supplier, for a given time, and so on. The solution to that problem is essentially the combination of the following four modules:

  1. A data definition and loading (DDL) module
  2. A data enrichment (DE) module
  3. A knowledge base (KB) module
  4. An analytics module

Many enterprises that pursue enterprise data science today face a supply chain problem. Instead of a supply chain of things, we now face a supply chain of information. Instead of a multitude of ERP systems and other sources, enterprises have a multitude of information sources. Instead of expense records that can be incomplete, inaccurate, or imprecise, we have data of many types that can suffer from the same undesirable characteristics. Instead of General Ledger or Commodity hierarchies that can differ between departments or territories, we may have other reference structures that differ depending on the nature of their source (e.g. domain specific ontologies). From an information perspective, there is a level of abstraction where the problems are equivalent and therefore similar solution strategies can be pursued.

  • For an enterprise data science program, your DDL module would be a cloud-based information acquisition module responsible for connecting to your data sources and loading your data.
  • Your DE module would be a general data processing framework that would allow you to analyze, synthesize, enrich, and even summarize your data. The term “enrichment” as used here goes beyond data augmentation, and includes elements of data quality such as structure, consistency, and derivative data reference schemes.
  • The KB module would be the distilled knowledge from all your data that has been integrated, evaluated, and can be used for analysis and fuel the creation of value in many areas across the enterprise.
  • The analytics module would now be responsible for providing various interfaces between the work accomplished by the program and every other activity that the enterprise performs. The spectrum of interfaces that can be provided to consumers of knowledge range from raw data access to question/answering systems and data science notebooks.

Companies who embrace enterprise data science as a strategic approach to sourcing of information will be handsomely rewarded and be leaders in their sectors.

Keep Learning: 

The post Enterprise Data Science: What It Is and Why It Matters appeared first on Copyright Clearance Center.

]]> 0
2 Big Takeaways for Information Managers from the 2017 State of BI and Predictive Analytics Report Fri, 29 Sep 2017 07:41:25 +0000 How, when, and why are organizations using business intelligence tools? A look inside Dresner Advisory Services' 2017 Advanced and Predictive Analytics Market Study.

The post 2 Big Takeaways for Information Managers from the 2017 State of BI and Predictive Analytics Report appeared first on Copyright Clearance Center.

Before planning the future of your information center, you need to understand the past. While not a crystal ball, predictive analytics help organizations forecast future events by finding patterns and trends in historical data. With this type of information more readily available than ever before, understanding the past has become much easier.

Deciphering how predictive analytics and business intelligence (BI) impact organizations is the subject of a recent report published by Dresner Advisory Services. 2017 Advanced and Predictive Analytics Market Study comprises 90 pages of in-depth market analysis, examining evolving user perceptions and future capabilities.

How, when, and why business intelligence is being used

An article in Forbes identifies key points from this global report, which is based on insights from more than 3,000 organizations. Here’s a look at the takeaways information managers should know, as they look to utilize data to tell their content ROI story.

End-user self-service is a top priority 

When it comes to advanced analytics, perhaps unsurprisingly, BI experts, business analysts, statisticians and data scientists are the most frequent users. Just over 60% of data scientists and statisticians reported using these tools either constantly or often. At the other end of the table, executives and third-party consultants were found to use the technology the least.

Remember: Just because executives aren’t working with BI technology, doesn’t mean they’re not utilizing the data found within it. Many times, information managers must compile and analyze data, and bring forth the most important trends or insights to upper management. In these situations, having a clear understanding of what your stakeholders will want and need to see is critical.

Learn more: Defending Content Spend – Make Sure You Involve the Right People

Reporting capabilities and dashboards are “must-haves”

It’s still early days for many of the technologies driving BI and predictive analytics, but the study revealed that companies are acknowledging their importance for future growth and are investing accordingly.

The top two priorities for enterprises planning to utilize BI data are reporting capabilities and dashboards; 80% of respondents consider these to be “critical” or “very important.” Within dashboards, end users can organize the data they find most valuable, making it easier to showcase to stakeholders and other departments within the organization.

Data mining, data discovery and data storytelling also featured highly, with more than 40% of respondents deeming them as critical or very important.

Additional takeaways:

Scalability: Regarding predictive analytics and business intelligence platforms, in-memory and in-database analytics come out on top, with more than 80% of respondents considering them to be important.

Features of analytics technology: The study also revealed that organizations are basing their BI initiatives on features within the technology. Around 80% of respondents rank regression models, textbook statistical functions, and hierarchical clustering as the most important features. Given less importance are text analytics functions and sentiment analysis (less than 70%), and ensemble learning (less than 60%).


As the volume of data continues to grow, so too does investment in advanced analytics tools, data scientists, and their ongoing skills training.

Not sure how analytics could help your information center? See if you can answer the following 5 Questions Every Information Manager Should Be Able To Answer

The post 2 Big Takeaways for Information Managers from the 2017 State of BI and Predictive Analytics Report appeared first on Copyright Clearance Center.

]]> 0
3 Steps to Turn Analytics into Actionable Insights Tue, 18 Jul 2017 08:06:42 +0000 In order to get the insights you’re looking for, you have to have a clear idea of your goals beforehand.

The post 3 Steps to Turn Analytics into Actionable Insights appeared first on Copyright Clearance Center.

Companies might understand the value of data, but they often lack the ability to turn it into actionable insights. In the corporate library setting, information managers are tasked with making content decisions for their organizations and they need analytics to support their historical knowledge.

That said, it’s not always easy. According to a report by Forrester, 74% of firms say they want to be data-driven, however just 29% are successfully transforming analytics data into business actions.

Other research by Interana reveals that 70% of organizations feel they neither get critical insights from their data nor do they get data into the hands of the right individuals.

So, when did actionable insights become the missing link between data and successful business outcomes, and how can information managers glean these insights to justify content spend?

Actionable insights explained

Before we can answer this question, we need to be able to answer the question: What is an “actionable insight?”

The starting point is data – the raw, unprocessed facts that take the form of numbers and text and generally live in spreadsheets and databases. Once that data has been processed and organized into a more user-friendly format it becomes information. By analyzing that information and drawing conclusions, insights can be generated.

As Techopedia explains: “once there is enough insight that a proper course of action can be made from it, then this result is considered as an actionable insight.” Insights that make business leaders rethink an idea or push them into a new direction are particularly valuable.

For example, for a corporate librarian a list of top content users may be helpful from a practical standpoint. It will help him or her understand who uses content and at what cost. But for stakeholders, who are less interested in day-to-day content use, this may not be considered actionable.

Taking it a step further, what if there was list of top content users and their associated division over the past three years? The data quickly becomes more than just a list of names, but trend information about what divisions use the most content, and how that has changed year-over-year. This data is actionable to stakeholders who may want to invest in certain divisions where heavy research is being done.

And if this supports organizational goals? Even better.

How to spot an actionable insight

To deem an insight “actionable” look for at least one of these five qualifications:

  • Alignment: Insights should be closely tied to key business goals.
  • Context: You need to understand why an insight is important or unique.
  • Relevance: If insights aren’t directed to the right decision makers, they might not get the consideration they deserve.
  • Specificity: An insight is incomplete unless it explains why something has happened.
  • Novelty: Successful insights will challenge or evolve our current beliefs and reveal new patterns.

How to turn raw content usage data into actionable insights

As information managers, the content usage data that you need to showcase can vary depending on who you’re speaking to, or what you’re trying to convey.

Professionals across departments all share the goal of working to overcome today’s data deluge. Luckily, there are several things organizations can do to turn raw data into the kind of insights needed to make more strategic business decisions.

1. Recognize different types of data analytics

Integrating analytics into business systems can be challenge, so it’s important to understand what you are dealing with.

As a rule of thumb, make sure you identify what types of data analytics you’re working with. These could be:

  • Descriptive: what the problem is
  • Diagnostic: why the problem has happened
  • Predictive: what will happen
  • Prescriptive: identifying the best course of action

2. Measure what matters

Unless you identify the business problem you are trying to solve, you won’t measure what’s relevant. With measurement comes optimization. Don’t get lost in the data that could be considered “vanity metrics” – look instead for real-time, high fidelity data. Not sure where to begin? Ask your stakeholders what they need to see.

RelatedWhat Types of Usage Data do Information Managers Actually Want? [Research]

3. Put your data into context

The more you understand the context of your data, the better you interpret it, and the more strategic the subsequent decisions will be. You can establish context by asking what data means, establishing its level of importance, and understanding its impact on the business.

Benefits to business

The benefits of content analytics data are clear. Not only does it allow you to identify the trends that capture best practices and raise potential issues, it also increases productivity and optimizes content investment.

In order to get the insights you’re looking for, you have to have a clear idea of your goals beforehand.

While an information manager may be able to collate a top-line report, someone else in the organization might have a different way of interpreting it. Not only does this unlock the potential for gaining actionable insights, it also promotes greater collaboration across the company.

Ready to learn more? Check out:

The post 3 Steps to Turn Analytics into Actionable Insights appeared first on Copyright Clearance Center.

]]> 0
What Types of Usage Data do Information Managers Actually Want? [Research] Wed, 28 Jun 2017 15:53:39 +0000 New research from Outsell suggests standalone content usage statistics are not enough. Here's what information managers actually want from usage data.

The post What Types of Usage Data do Information Managers Actually Want? [Research] appeared first on Copyright Clearance Center.

A new survey from research and advisory firm, Outsell, Inc., highlights the importance of frequent and detailed usage data for information managers in order to show the value of content spend.

The research – a combination of responses from current and former members of Outsell’s information management community, feedback from information managers, and industry analysis – confirms the value of usage data in supporting R&D, product innovation, and competitive advantage.

Content that’s worth the spend

Most information managers are tasked with proving that high cost content is worth the expense. According to the survey, collecting comprehensive usage data on a regular basis is crucial for many information managers because it offers real insight into what content is worth the investment. Respondents noted that not only does usage data demonstrate the specific value of subscriptions, it also allows them to accurately allocate charges within the organization.

Respondents were clear about what they want to do with usage information. For example, they need to be able to:

  • Convert data into visuals
  • Compare different pricing models
  • Access statistics that are in a useable format (i.e. electronic)

What information managers are looking for in usage data

It’s vital for information managers to understand the extent to which content is being used and by whom. To this end, respondents want to see COUNTER-compliant statistics demonstrating:

  • Downloads, searches, and views broken down by specific users, location, and by journal
  • Number of hits, downloads, and prints
  • Usage by date, broken down by month, quarter, and year
  • Patterns and trends

All of these metrics fall into what we at CCC like to call ‘measures that matter’ – measures that can be used to provide clarity around the content your employees are using.

Related Reading: Defending Content Spend 101: Make Sure You Involve the Right People 

Current challenges

According to some survey respondents, standalone usage statistics are not enough. The data needs to tell a story that goes beyond numbers, and provide a more precise picture of what content users are most interested in.

Information managers continue to look for even more data that can be used to help them better understand their clients, how they interact with the products they license, and how the content fits into their workflows.

The strategic solution

In an environment of declining or flat budgets and rising content costs, information managers need to monitor content use and demonstrate the ROI of their subscriptions. Usage and value statistics help information managers meet the information needs of their end users and show the strategic business value of content to their organization.

RightFind® Business Intelligence helps information managers gain insights on content usage and justify their content spend. Data is presented in a way that is quickly understood by senior management, resulting in increased visibility of information services and the strategic value of content. Learn more about it here.

The post What Types of Usage Data do Information Managers Actually Want? [Research] appeared first on Copyright Clearance Center.

]]> 0
How Information Managers Can Explain Usage Data Through Visualizations Wed, 31 May 2017 17:32:53 +0000 Defending your content spend can be difficult. Data visualization helps information managers transform usage data into a clear, engaging story. 

The post How Information Managers Can Explain Usage Data Through Visualizations appeared first on Copyright Clearance Center.

On its own, data is a series of facts and statistical information. Although data surrounds us, without context, it can be hard to draw any insights about what story the data is telling. Add visualization to that data, however, and it can be transformed into a clear, precise story with actionable insights.

Transforming data into more engaging, intuitive, and valuable information is growing in importance for information managers, as our need to convey progressively complex numbers and ideas increases.

Why visuals?

The power of data visualization lies in how we process information. Marcel Just, director of the Center for Cognitive Brain Imaging at Carnegie Mellon University, summarized the importance of data visualization in a Nieman Reports article. In it, he explains that humans take on information in a visual way, meaning the more visual the information, the more useable, interactive, and accessible it becomes.

“The printed word is a human artifact,” Just said. “It’s very convenient and it’s worked very well for us for 5,000 years, but it’s an invention of human beings. By contrast Mother Nature has built into our brain our ability to see the visual world and interpret it. Even the spoken language is much more a given biologically than reading written language.”

Tell stakeholders an engaging story through visuals

In the current budget climate, information managers are balancing static or declining content budgets with rising content costs. This is a challenge as they must continue provide the high-quality content their organization relies on to remain competitive.

Creating a robust information center that’s backed by strategic data is not easy, but it can be a way to ensure the organization understands the value of the information center and the resources it provides to researchers.

The challenge begins with access. Data for subscribed content and document delivery is often located in disparate sources (think publisher platforms, document delivery platforms and internal systems.) Then, the task of normalizing that data and presenting it in a unified view takes time and effort.

Once that data is compiled, an Excel spreadsheet crammed with lines of numbers may be useful to information managers, but most stakeholders want their information quickly and to-the-point.

The more interesting and compelling usage data is, the easier it will be to share with stakeholders who, while keen to understand why decisions are made, are largely unfamiliar with the complexity and nuances of the data itself. Visualizations can allow for a clear, succinct, and targeted message.

Britt Mueller shares the importance of this, here:

“Having influential stakeholders who are willing to talk about the value and importance of content is an extremely valuable way to justify spending. These people are vouching for the need as outsiders of the actual information function, and that can lend credibility and insight into the actual use of the content.” 

The data-driven information manager

As more organizations recognize the benefits of data visualization – less time spent creating reports, greater collaboration, better customer interaction, and a greater competitive edge – the more insight and ROI they gain. Because when it comes to visualization, just like the data, the opportunities are endless.

With RightFind Business Intelligence, the content usage, spend, and value analytics module for RightFind, you can quickly and easily create data visualizations and presentations to let the data tell your content ROI story.

The post How Information Managers Can Explain Usage Data Through Visualizations appeared first on Copyright Clearance Center.

]]> 0
Discuss your Research Challenges with CCC at Bio-IT World Conference & Expo ’17 May 23-25 in Boston Wed, 17 May 2017 08:30:40 +0000 Join CCC at Bio-IT World in Boston this May to learn about accelerating research through full text semantic enrichment and data integration.

The post Discuss your Research Challenges with CCC at Bio-IT World Conference & Expo ’17 May 23-25 in Boston appeared first on Copyright Clearance Center.

CCC will be among 3,300 life science, pharmaceutical, clinical, healthcare and IT professionals from more than 40 countries at the Bio-IT World Conference & Expo ’17 on May 23-25 at the Seaport World Trade Center in Boston, MA. We invite you to visit CCC (booth #548) to talk about your R&D team’s information challenges and how CCC solutions can help.

This year’s conference features more than 200+ technology and scientific presentations. It covers big data, smart data, cloud computing, trends in IT infrastructure, omics technologies, high-performance computing, data analytics, open source and precision medicine, from the research realm to the clinical arena.

Join CCC’s Anna Lyubetskaya (Data Scientist) and Mike Iarrobino (Product Manager) on Thursday, May 25 at 11:40 a.m. for their presentation, Accelerating Research through Full Text Semantic Enrichment and Data Integration.

In this talk, you’ll learn:

  • How to use a network environment where text and data are easily connected to make informed decisions
  • How to integrate expert knowledge computationally to address challenges resulting from an increased volume of information and novel facts present in unstructured text and experimental output.

Bio-IT World ’17 conference hours:

  • Tuesday, May 23 (4 – 7 p.m.)
  • Wednesday, May 24 (8 – 6:30 p.m.)
  • Thursday, May 25 (8 – 1:55 p.m.)

Not attending the conference? Follow all the action using hashtag #BioIT17. Connect with Bio-IT World (@bioitworld) and CCC (@copyrightclear) on Twitter for up-to-the-minute dispatches from the conference.

The post Discuss your Research Challenges with CCC at Bio-IT World Conference & Expo ’17 May 23-25 in Boston appeared first on Copyright Clearance Center.

]]> 0
What is Machine Learning? (And Why Use It?) Wed, 10 May 2017 08:04:40 +0000 Machine learning is making the most of one of pharma’s most valuable assets – data. And it’s transforming the industry in the process.

The post What is Machine Learning? (And Why Use It?) appeared first on Copyright Clearance Center.

The science of machine learning isn’t new, but interest in the field is gaining fresh momentum. The concept of technology allowing machines to learn from data dates to the 1950s, but new applications of machine learning have the potential to change the face of pharma forever.

But what is it exactly, and how is it being used currently? SAS defines machine learning as:

“A method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.”

All machine learning relies on data – and the healthcare industry is a data goldmine. Big data and machine learning in pharma could generate an annual value of $100 billion across the US, according to new research from McKinsey.

New drugs cost $2.6 billion to develop and take an average of 12-14 years to get to market

New drugs cost $2.6 billion to develop and take an average of 12-14 years to get to market, meaning the industry has a major challenge on its hands. Machine learning has the potential to reduce these costs, enhance competition, accelerate drug discovery, boost quality compliance, and cut time to market.

Let’s look at just three examples of how machine learning is helping the industry to evolve.

Improving patient safety and drug development

IBM Research has an impressive track-record of applying machine learning to medicine. In April, the company received a patent for a machine learning tool that can identify the side effects of certain therapies and diseases. This data can be used to improve patient safety and identify new applications for drugs, as well as improve the efficiency and effectiveness of drug discovery and development.

The tool has already established a link between weight loss and mood disorders such as bipolar and depression. This could lead to researchers examining ways of repurposing mood-disorder drugs to be used as a form of weight control.

Driving value-based, precision medicine

Celgene is a company committed to accelerating value-based, precision medicine development through machine learning. The biopharma giant has investments in a machine-learning platform to support decisions across multiple therapeutic areas.

As a result, Celgene can apply this technology to drug discovery, clinical development and commercialization. This means greater insight and answers to more complex questions for treatments when matching drugs and other health interventions to the individual.

Unlocking the secrets of stem cells

The team at the Allen Institute for Cell Science launched a vast online catalogue of 3D stem cell visuals. Each image in the Allen Cell Explorer has been created through deep learning analysis. Computer scientists used the technology to establish relationships between the locations of structures within cells and then create predictive models of cell organization. The program ‘learns’ by comparing its predictions to actual cells.

In the future, the portal will include distinct cell types and could help researchers identify stem cell lines with mutations linked to cancer and other diseases.

Turning content into smart data

Life sciences organizations are also increasingly turning to machine learning to extract insights from published scientific research. And with good reason – this content is the lifeblood of the organization, and its volume and velocity are growing. More scientific research published between 2010 and 2014 was indexed in MEDLINE than all research published before 1970.

However, these initiatives must surmount challenges to content licensing and data preparation – including lack of access to full-text scientific literature vs. abstracts, inconsistent terms of use, and formatting discrepancies. Informatics teams, data scientists, and knowledge managers must also make schema and template decisions that will affect the types of features and attributes that can be extracted from the content. How content is processed into smart data upstream affects results from machine learning processes downstream.

These challenges aren’t trivial, but in the competitive life sciences market, breakthroughs in these areas could produce big outcomes in cost and time to market.

Related Reading: Accelerating Drug R&D While Minimizing Costs

The post What is Machine Learning? (And Why Use It?) appeared first on Copyright Clearance Center.

]]> 0