How big data is changing the face of business

Large and small companies alike must take advantage of the insights data brings. G-STOCKSTUDIO

Large and small companies alike must take advantage of the insights data brings.

Businesses increasingly rely on data to make strategic decisions. As businesses track and capture more and more types of data, the need for talent who know how to manipulate and interpret this data is increasing.

Many successful companies such as Amazon, eBay, and Humana have built their entire business model around analytics. They struggle to find new, well-qualified employees. The workforce demand has already outnumbered qualified job seekers with the gap predicted only to increase 

According to Constellation Research, recent developments in using business-data analytics to direct strategies are a just a precursor to what is to come. In 2018 and beyond, the study predicts, we will see analytic methods becoming more intelligent through the use of advanced analytics, machine learning (ML) and artificial intelligence (AI). These smart capabilities are already evident in areas such as data prep, discovery, analysis, prediction, and AI-powered prescriptive applications. As the rate of technology adoption advances, the need for professionals who are experts in these fields will increase. 

Many businesses have data, but talent is the differentiator. To address the growing need for analytics talent, the University of Louisville is working closely with local businesses to create a new graduate degree in business analytics. This new Master of Science in Business Analytics (MSBA) program at the university leverages the strengths of the faculty in areas such as statistics, machine learning, database, computer science, operations research, marketing analytics, business communications and strategy.

By blending the faculty’s diverse skill sets into an integrated curriculum, this new program offers deep insights into business analytics problems. The 13-month evening program will launch in August. As the only program in the region, the MSBA program partners with businesses such as Accent(x), Brown-Forman, Edj, GE, KFC, LG&E KU, Papa John's, Texas Roadhouse, Trilogy, and UPS to fine-tune the curricula to match industry needs and to offer opportunities for competitive paid internships.

"We wanted to make sure we got this right,” said Vernon Foster, executive director of graduate programs at the University of Louisville College of Business. “The meticulous process involved in building a program like this ensures a high-quality program with real value. Coupling this opportunity with a competitive internship will only enhance the skills the students acquire."

A paid internship is rare among graduate programs, especially one that lasts for the length of the program. Although it is getting more popular to give students work experience, most universities offer internships for only a few months and more commonly unpaid. When entering the job market, University of Louisville students will benefit from having professional experience in addition to educational credentials. Students graduate ready for positions well above entry level.

Data analytics is no longer a luxury. Large and small companies alike must take advantage of the insights data brings to compete in today’s data-driven economy.

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‘Deep learning’ — the hot topic in AI

Experts in the field are in demand and future managers would do well to grasp the concept

Brain boost: algorithms need regular updating as information changes constantly © Getty

Brain boost: algorithms need regular updating as information changes constantly © Getty

   Deep learning may be one of the most overhyped of modern technologies, but there is a good chance that it will one day become the secret sauce in many different business processes. For anyone entering the workforce now — or thinking about how to position their career for the long term — this would be a very good time to understand its implications better.

The term “deep learning” refers to the use of artificial neural networks to carry out a form of advanced pattern recognition. Algorithms are trained on large amounts of data, then applied to fresh data that is to be analysed. It has become the hottest subject in the field of artificial intelligence, thanks in particular to breakthroughs in image and language recognition in recent years that have approached or surpassed human levels of comprehension.

The potential scale of deep learning’s impact on business was laid out last month in a report from McKinsey Global Institute, Notes from the AI Frontier: Insight from Hundreds of Use Cases. Depending on the industry it is in, the value a company could hope to gain from applying this technology ranges from 1 to 9 per cent of its revenues, according to the consultants.

This points to trillions of dollars of potential impact on business — and the workers who are the first to learn how to apply it will be the big winners, according to Michael Chui, a McKinsey partner.

“If you learn sooner and faster, you have the chance to do much better relative to others,” he says. That applies not only to people with technical skills, but to any manager who works out how to use the technology to tackle business problems.

The reason that managers who learn how to make use of the technique have the chance to leap ahead of others, Chui explains, is that technologies such as deep learning can have an outsized impact throughout a business. “The technologies are levers of value creation . . . digital means you can do more, faster. If you can successfully scale something across an organisation or a customer base, you have that much more impact.”

The best way to think of deep learning is as a form of advanced analytics. Given enough data to train the algorithm, it can be used in many different tasks. The challenges include identifying the types of problem that are most susceptible to being solved with this technique, picking the particular approach that is best in any given situation and making sure the algorithms are fed with a good supply of high-quality and timely data.

Most of the business potential in deep learning, according to McKinsey, will come in two broad areas: marketing and sales, and supply chains and manufacturing. Examples of the former include customer service management, creating individualised offers, acquiring customers and honing prices and promotions.

This means that companies in consumer industries stand to benefit more than most from deep learning. Frequent interactions with customers generate the kind of data needed to feed the systems. Using real-time data to predict demand trends on a hyper-regional basis can add 0.25-0.75 per cent to sales, the consultants estimate, with further benefits from lower waste and spoilage.

Neural path: early adopters of deep learning stand to gain the most © Getty

Neural path: early adopters of deep learning stand to gain the most © Getty

Uses of the technology in supply chains and manufacturing, meanwhile, include predictive maintenance of equipment, yield optimisation, procurement analytics and inventory optimisation.

These potential benefits are purely theoretical, based on the capabilities of the technology, and it will take some time for most companies to capture them. There is, for instance, a huge skills shortage in the business world. Data scientists and machine-learning specialists are now the most in-demand IT experts, based on the high salaries they are attracting.

The tools that non-expert developers need to make use of the technology are also still in their infancy. Many of the recent advances in the field still count as cutting-edge research. Services such as Google’s Cloud AutoML represent the first real attempt to make deep learning more widely available as a practical tool, by automating parts of the laborious task of training the algorithms.

Deep learning will not just be for the technical specialists, however. General business managers of the future will need to understand when problems are likely to be susceptible to a deep-learning approach, and how to manage the diverse teams with more technical skills that will need to be pulled together to solve them.

There are plenty of stumbling blocks. The biggest involve data, starting with how to collect, “clean” and label it in a way that makes it useful for training machine-learning systems.

The good news is that many companies already have plenty of the raw material available. “Often, there is a lot of data already in existence and little of it gets used,” says Chui. But constant changes in the underlying information being collected means that models often need to be updated. In a third of the use cases for deep learning that McKinsey looked at, the algorithms needed to be retrained at least every month to keep them relevant.

A further challenge stems from ensuring that the data used to train a system is representative and leads to reliable answers. There is now widespread acknowledgment of the risks that come from biased data. Often, the problem stems from applying information collected for one purpose to a different problem, without making allowances for gaps in the dataset.

Most companies are at the very early stages of thinking about how to apply this form of AI in their own businesses — if they have thought about it at all. But for a future generation of managers, it could one day become a core skill.

The AI techniques that businesses will benefit the most from

The artificial neural networks used in deep learning systems are particularly suited to solving certain types of problems. Three analytical techniques have the potential to unlock the most value for businesses, according to McKinsey:
- Classification. The system learns to distinguish between different items, then when confronted with a new input places it in the right category. This is the approach applied in image recognition: it could be used to identify whether products coming off a manufacturing line meet a certain visual quality standard.
- Continuous Estimation. Also known as prediction, this involves estimating the next numeric value in a sequence. It could be used to forecast demand for a new product, based on information such as sales of a previous product, consumer sentiment and weather.
- Clustering. An algorithm learns to create categories based on common characteristics. Presented with data about individual consumers such as their location, age and buying behaviour, for instance, an algorithm could create a set of new consumer segments.

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Here’s Who Dies In “Game Of Thrones” S8, According To Data Science

Data scientist Taylor Larkin crunched the numbers, and here are his probability picks for which heads will roll in season eight.

[Photo: courtesy of Helen Sloan/HBO]

[Photo: courtesy of Helen Sloan/HBO]

Why we care: Nobody was prepared for it when Ned Stark (Sean Bean) literally lost his head in the first season of Game of Thrones. (Well, nobody except for the thousands who’d actually read the books and were actively anticipating what face you’d make when you watched that shocking execution.) Ned had been set up as the moral center of the series. His death was a shot across the audience’s bow, signaling that no character was safe. Valar morghulis, etc., ad infinitum.

By now, audiences are keenly aware of the eminent fallibility of the sprawling cast’s every member. The high-profile deaths have arrived with such regularity that one could conceivably predict who goes next. In fact, somebody did.

Several predictive analyses have come before, but data scientist Taylor Larkin wanted to take a crack at it himself. Larkin took info on the show’s bajillions of characters, scraped from a fan-made wiki–including data points like house, gender, nobility status, age, and number of relatives already killed–and used automated machine learning from DataRobot to determine who is most likely to die in the show’s final season, which is scheduled to premiere in April 2019. (The entire complicated process is detailed in full here.)

Have a look at the results below, but keep in mind that the data comes from the Song of Fire and Ice books, rather than the show, which has taken some necessary liberties in its later seasons.

Daenerys Targaryen–83.77% chance of death
Jaime Lannister–72.91% chance of death
Tyrion Lannister–70.76% chance of death
Bran Stark–66.02% chance of death
Cersei Lannister–60.39% chance of death
Jon Snow–58.99% chance of death
Euron Greyjoy–54.95% chance of death
Sansa Stark–50.28% chance of death
Arya Stark–49.04% chance of death
Gendry–39.87% chance of death

Now, if only the algorithm could predict when George R.R. Martin will finish The Winds of Winter, the achingly anticipated final book in the series.

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Using Data Science to Help Predict Adverse Drug Reactions


PhD students, undergrads develop visual analytics system for FDA

May 9, 2018

Six WPI students, under the direction of computer science professor Elke Rundensteiner, have integrated natural language processing and deep learning techniques to develop a visual analytics system by processing the more than one million reports to adverse drug reactions gathered annually by the U.S. Food and Drug Administration (FDA). The research is aimed at better prediction of harmful reactions from drug-drug interactions.

The implications of this accomplishment—which includes work done by four undergraduates as their Major Qualifying Project (MQP)—are important because adverse drug reaction events cause more than 100,000 deaths a year in the United States and cost over $170 billion in annual added expenses. Drug to drug interactions are also a major cause of emergency room visits and hospitalizations. Without big data tools such as those developed by the WPI team to support safety evaluators at the FDA to sift through this huge volume of adverse event reports, dangerous incidents will remain unchecked.

Elke Rundensteiner (left) and her students have developed a visual analytics system to help better predict harmful reactions from drug-drug interactions.

Elke Rundensteiner (left) and her students have developed a visual analytics system to help better predict harmful reactions from drug-drug interactions.

“This project is important to me because it has impactful real-world implications,” says Brian Zylich '19 . He says the new technology “may very well be used to improve the capabilities of FDA safety evaluators to identify harmful drug-drug interactions. “This means that the FDA will be able to detect problems and warn patients and consumers in a more timely manner and potentially save lives.”

The students’ efforts have already resulted in the submission of several academic research papers, including one presented last month at the IEEE International Conference on Data Engineering in Paris. Other papers are being reviewed for publication.

Rundensteiner says machine learning and natural language processing capabilities from the system may be worked into the Adverse Event Reporting System (FAERS) software for use by the FDA. She and her PhD students, Tabassum Kakar and Xiao Qin, will meet with their FDA collaborators, safety evaluators, and the deputy director of the Office of Surveillance and Epidemiology this month in Washington, D.C., to discuss the WPI project and see whether it might serve as a model for a wider improvement of the adverse drug reaction review process.

Detecting all potential drug interactions in clinical trials for new drugs is impossible, since it would require testing all potential drugs against the one being used by patients in the trial. Consequently, FDA evaluators rely on the reporting of adverse drug reactions from the field—from healthcare professionals, manufacturers, and consumers—that are submitted to the agency.

The FDA uses FAERS to collect the semi-structured adverse event reports into a database. FAERS then enables the safety reviewers to browse through the list of reports related to a given disease by date or sorted by other criteria. But the system currently does not employ machine learning algorithms for detecting adverse drug-drug interactions and then recommending particular interactions to the safety reviewers for in-depth analysis. The visual analytics capabilities of the current review system also are limited—missing the opportunity to bring the human in the loop of the analysis process.

The WPI students have worked to develop a system that uses text mining and deep learning to effectively sort through and compare the reports and then present the results on a series of interactive visualizations, so analysts can more readily identify possibly serious drug interactions.

Research into the system began several years ago when WPI Marni Hall '97, now a university trustee, suggested that Rundensteiner and her students undertake a project with the FDA to bring WPI’s data science expertise to solve the FDA’s challenging big data problems. At the time Hall was a senior program director at the FDA, overseeing the branch responsible for the surveillance and epidemiology.  

Rundensteiner, Kakar, and Qin began a collaboration with the FDA with fellowships funded by the agency through the Oak Ridge Institute for Science and Education. These multi-year graduate fellowships have amounted to nearly $200,000 in funding.

Rundensteiner and the students hold weekly conferences with their FDA collaborators and developed a close rapport learning about the big data challenges the FDA faces.

Qin developed natural language processing and machine learning strategies that extract information from unstructured text and allow it to be compared to data from other reports, scoring the relative interest of the mined interactions for their relevancy. Kakar, with guidance from computer science professor Lane Harrison, has designed novel data visualizations to display critical information on the computer screen and empower safety evaluators to visually interact with the data to facilitate discovery.

Research innovations by Kakar and Qin will be brought to fruition by developing an integrated web-based system that incorporates these innovations within one platform. Four undergraduates took on this task with Kakar and Qin as their mentors. The Multiple Drug Interaction Analytics Platform (MIAP) developed by Zylich, along with Andrew Schade, Brian McCarthy, and Huy Quoc Tran and their mentors is a fully working web-based prototype system, Rundensteiner says.

They realized the research concepts by the graduate students by developing the technologies in a common programming language and then combining them into an integrated web-based client-server system. They tackled difficult data integration challenges developing algorithms for detecting and correcting data ambiguities, such as differences in names for the same drug and imprecise naming of adverse drug events. This empowers the MIAP system to distinguish between newly discovered drug-to-drug interactions versus those previously known by the community. And they overlaid scientific information about known drug-to-drug interactions extracted from external web sources to those discovered by the system to provide better context for safety evaluators.

The undergraduates also enhanced visualizations of the drug interactions and developed a web-based human computer interface for the system so it can be accessed by evaluators more easily.

The new system presents its results on a series of visual displays that allow an evaluator to see myriad interactions among drugs, to drill down on a specific drug and to access additional data on the drugs, down to the specific reports that serve as evidence for the learned drug-to-drug interaction.

The undergraduates have put together a video demonstrating the highlights of the new technology.

“The undergraduates really did an amazing job to show what is feasible by bringing the two distinct innovations together in one integrated system,” says Rundensteiner. “In terms of potential vision for the FDA, this is huge.”

“The undergraduates really did an amazing job to show what is feasible by bringing the two distinct innovations together in one integrated system. In terms of potential vision for the FDA, this is huge.” -Elke Rundensteiner

Kakar thinks the WPI system is an improvement over the query-based approach now in use at the FDA, which requires evaluators to ask explicit questions about the report data to draw out conclusions about interactions.

“The [WPI] system is very interactive,” she says. “They can find out what are potential interactions with ease. They don't have to run manual queries each time to find certain information.

“It can help them find out something alarming very quickly, as compared to their current system, where they have to run queries. We received positive feedback from the FDA—from the safety evaluators and the supervisors I work with.”

Rundensteiner says this innovative WPI system could serve as a model for improvements in the review system in place at the FDA.

“I believe that our proposed techniques have the potential to fundamentally revolutionize how science and safety review is done in the future,” Rundensteiner says.

“Having developed a fully working prototype lets the FDA safety evaluators and their staff play with this as a prototype and see what they like and what they don’t like. This will speed up the development cycle for future technologies. It will guide the FDA in putting together requirements for which features a review system of the future should have.”

Undergraduates Schade and McCarthy see real-world value in what they have produced, making the process of evaluating drug interaction information more efficient, allowing evaluators to get important information and connections easier and prioritize actions more quickly.

“It's important because it allows FDA drug evaluators to understand the risk of combining prescriptions between two different drugs and really making that public knowledge,” says Schade. “Our project creates a way to interpret that data quickly.”

“That’s important because these adverse effects that happen when you're taking a drug or multiple drugs can be life threatening,” adds McCarthy. “And yet they're rather common.”

Rundensteiner also sees tremendous benefit from bringing together both undergraduate and graduate students to work jointly on this larger vision in a mixed team.

“This project showcases the value of the capstone projects at WPI because it's going from critical societal problems to building a real-world working solution,” she continues. The students’ work demonstrated “the wide spectrum of skills that computer science graduates trained at WPI are equipped with from system building to human interface design.”

Zylich, Schade, and McCarthy say the project has taught them invaluable lessons that will last well after their time at WPI.

Schade, who starts work this week as a data engineer for an insurance company in New York, says the FDA effort taught him how to keep the large vision of a project in focus while he broke down the project into individual tasks, prioritized the tasks, and worked on them. Beyond the technical aspects of the project, the effort allowed him to practice both his teamwork and leadership skills. He says he has mentioned this project experience and its lessons in all his job interviews.

Zylich, a junior in a BS/MS program who wants to get his computer science PhD and go into research, says the project “allowed me to apply the theoretical concepts I learned in my classes to a practical project, incorporating WPI’s pillars of theory and practice.” He said it also provided "a fantastic opportunity to learn about graduate school, work with graduate students, and experience conducting research and developing a scholarly manuscript and video-describing the research and its impact.

And McCarthy, who starts a software engineering job in July at Constant Contact in Waltham, says the project exposed the team to “many state-of-the-art techniques and technologies—ranging from data visualization, human computer interaction, client-server systems, web development, database processing, to machine learning” and diverse challenges of software development.

On top of all that, the project also sharpened their team working skills, he adds. 

“There were so many different software components that were designed in different ways and used distinct technologies,” he says. “It was critical for the success of the project that we were able to work together, that we were able to divide up that work, but also communicate well so that these different pieces that were being developed independently would work together well.”

- By Thomas Coakley

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Loyola launches data science major for undergraduates

Loyola University Maryland will offer a bachelor’s in science in data science of undergraduates starting the fall of 2018.

The major is an interdisciplinary program that consists of 15 courses across various departments, including mathematics, statistics, business, and computer science. Students in the program will gain analytic knowledge that will prepare them for careers as business analysts, domain-specific managers, data mining analysts, and business intelligence specialists.

“According to Glassdoor, data science was the highest paid field in 2016, and the demand for data scientists continues to increase. Data-driven decision making is becoming increasingly popular--and important. To help meet that demand, Loyola decided to start an undergraduate major in data science,” said Christopher Morrell, Ph.D., director of the data science master’s program. “The goal of data science is to discover and extract actionable knowledge from data, knowledge that can be used to make predictions and decisions. Achieving these goals requires computational and statistical modeling skills. Loyola's major in data science provides students with applicable skills and expertise to collect, process, and analyze data to benefit organizations and businesses.”

As a Jesuit institution, the program also ensures students learn about the ethical implications of the work they will do as data scientist. The University introduced a graduate program in Data Science in the spring of 2017. For more information, go to

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How is Data Science Transforming Web Development?


Web app development is about to undergo a significant revolution triggered by the rise of data science. So far, developers have created apps based on focus groups, surveys and educated guesses about the needs and wants of users. This old way of working is biased and cannot include the input of a statistically significant number of users.

This is turning around, due to the zettabytes of available data made available by IoT. Instant and continuous access to the internet has triggered an unprecedented wave of user-generated data that can be turned into actionable insights.

Web development companies are now utilizing Artificial Intelligence to make sense of all these data points and incorporate the findings into apps, starting right from the design phase. This approach helps companies save time and costs by looking at specific behaviors and preferences of their target groups.


Currently, software development involves programmers coding or repurposing existing modules to create a working app that satisfies some pre-set requirements. Deep learning is about to change this for good.

No longer will a developer decide what goes where in the app menu. Data from analyzing the use of similar apps could suggest what is essential for users and what should be highlighted. It is a step forward from Google’s autocomplete feature that has already gone mainstream.


App upgrades will also be dictated by data, not by intuition or focus groups feedback. Users communicate their desires either by interacting with the app or by stating their demands online – both on forums and social media. To put this information to use, development teams should collect both streams of data and turn them into actionable insights.

In fact, Nvidia’s vice president and general manager Jim McHugh suggested that upgrades will no longer be the strategic team’s concern, but will emerge naturally from data. Machine learning algorithms grow smarter when there is more data available for training. When this happens, new versions appear.

For example, a new version of a chatbot will be consistently upgraded with user-generated input to include answers to searches or inquiries that previously returned no satisfactory results. In this kind of upgrade, developers have little to no input.


Since their current mode of work is drastically changing, web app developers are likely to fear losing their jobs in the next several years. However, it is not a matter of a lower demand for coders, but more a need for a different skill set. Programmers and coders will be in a higher demand than ever, but they will likely have to upgrade their expertise with data science and data analysis.

Web development is no longer only about writing code but more about structuring data, cleaning it, curating it and making sure it is ready to teach algorithms. These skills are incredibly different compared to what object-oriented or web programming meant a decade ago, but in this industry, progress is a given. The trend is now all about Python scripting and data analysis in R or Matlab.

As the code grows more abundant, it could mean the end of an era for developers as we know them now. Machines will have the needed piece of code at hand, and they will also know how to collate these pieces into a working program.


Right now, making sense of data can still give organizations a competitive advantage, but it will soon become the minimum operational requirement.  

There are several areas in which data science can have a real impact, including productivity, efficiency and personalization.


Web apps that remember our preferences and help us pick up where we left off can save time and energy. AI can learn about our spending habits, time usage and lifestyle. By crunching the numbers behind those experiences, it can offer personalized advice and simplify our choice.

These apps have the potential to become kind of private assistants, trustworthy partners, intelligent databases or smart repositories. Some apps give you reminders about important tasks, identify gaps in your schedule that you can use to your advantage or even block certain harmful habits like procrastination.


AI-powered apps will soon be by your side like faithful assistants, but they can also get into your mind better than friends and family. Already today, our smartphone can give us excellent tips based on geolocation, past likes, and interaction with specific brands.

Much like Netflix and Amazon, recommendation engines can be extended to other web apps that need to provide customized responses.

This is not only the next fad of the consumerist world, but the general direction of app development. The new generation of smartphones, like the iPhone X and the Galaxy S8, have come with built-in AI capabilities.  


The changes triggered by using data science in web app development will impact both consumers and developers alike. The cookies stored in browsers, as well as any data provided by the user during their web sessions, will become a hint about preferences and a way to customize the apps they interact with. For developers, the same data can be a primary source for upgrades and enhancements. Speed, reliability and functionality are still in high demand, but the difference will be made by incorporating a user’s own data into the look, feel and functionality of an app.

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