Tag: CABI

Business leaders need to hone a diverse range of skills and qualities before they can raise a successful business. They need to open their toolbox and see an array of skills and lived experiences that they can select and wield in any new situation. Understanding and using big data is one of those tools that no leader should be without. 

While many skills can be learned through work experience, certain ones, such as big data management, can be learned through a classroom MBA experience with knowledgeable guidance and instruction. The faculty members at Olin Business School at WashU are well-versed in both managing data and making empowered decisions based on data.

The business value of big data and data-driven leadership skills

Why is the Managing Big Data course such a key feature of the Olin MBA?

The course evolved in response to the world becoming increasingly reliant on data. We live in a digital age, and data is one of organizations’ most valuable assets. Data-driven decisions guide modern successful businesses; they collect vast amounts of data from various sources—customer interactions, social media and almost every other online channel—in order to accurately interpret and predict what their audience needs and wants.

The business value of big data is massive because the benefits of data-driven decision-making are so various and transformative. Data on its own is pretty meaningless, but when mined for its insights, it can suddenly prove and demonstrate all sorts of truths about businesses and their audiences.

Big data management isn’t just how organizations work now; it’s also how they win. Companies that effectively manage and analyze their data gain a competitive advantage in the marketplace. They can make better-informed decisions, develop targeted marketing strategies and identify new business opportunities.

This is why the Managing Big Data course is a crucial stepping stone for students preparing to work in an environment where they will need data analytics skills to survive. At Olin, we have created many courses that put data at the heart of business, but Managing Big Data is one of the most foundational courses for learning how to gain insight into customer behavior and pursue better business strategies.

What does the managing big data course teach students?

As this course has evolved, it has reacted to how students are learning and thinking in today’s business world. When we first started talking about big data, students didn’t know how to store, manipulate and retrieve data that didn’t fit into their computing machines. The term “cloud computing” was around, but students didn’t yet understand its advantages.

We introduced the course to answer these questions. As industries race to get more data, the demand for such skills grows, and students need big data skills to get their feet in the door. To better prepare students to face the challenges and decisions they will make leading their organizations, Managing Big Data focuses on how to:

Continue learning as big data evolves

Students need to know how to learn data analytics. The learning process will continue as they graduate and grow their businesses because data itself is growing all the time. The course equips leaders with the skills to analyze large volumes of data and extract meaningful insights to inform decision-making. By learning to use these tools and techniques, students also learn how to apply their education in the real world and continue to learn from those real-life experiences.

Understand customer behavior

In today’s business environment, understanding customer behavior is crucial for success. This course teaches leaders how to analyze customer data and identify patterns and trends in customer behavior, which can inform marketing strategies and product development decisions. By understanding customer behavior, leaders can make more informed decisions that align with the needs and preferences of their target markets.

Effectively manage risk

Managing risk is a critical business function in today’s world, where bold decisions are often needed to sustain a company in a difficult economy. Managing Big Data teaches leaders to use data analytics to identify potential risks and develop mitigation strategies. By understanding the potential risks associated with different actions and decisions, leaders can gain a bird’s eye view of their businesses and chart the best course to address or avoid problems.

Big data is not just a current trend but also the future of business. The volume and complexity of data will only continue to grow and become an important influencing factor in business decisions. If students can learn to leverage big data, they will be well-positioned to succeed in the marketplace.

The Data for Good conference is an event for members of the business community at large. The concept of the event focuses on how businesses can move forward using data while also staying true to values that promote good in the world.

During this event, Olin’s Center for Analytics and Business Insights (CABI) shared its framework for developing data-driven strategies for the benefit of St. Louis companies looking to create their own sustainable prosperity in their communities and in the region as a whole.

The pillars of this conference are also core to the Olin MBA program. We are on a mission to discover a more values-driven approach to data and help shape the next generation of business leaders with “good data” leadership in mind. The conference asks us how we can move beyond using data for good as a concept and discover first-hand how data can be used to responsibly solve critical business, civic, and social issues.

Why has “data for good” become a call to action?

Business has reached a particular moment in history where the amount and quality of data available is unprecedented. Vast quantities of data pass through organizations every day, ready to be collected and used to their advantage.

Yet, businesses also stand at a crossroads in terms of how to best make use of data and how to maintain longevity. Corporate data responsibility is still in its formative years as leaders are realizing that not all data is created equal. Data can be used in negative ways, and misused data can expose a company to harmful attacks and breaches of privacy. What we realize is that we need leaders who are trained to understand the power and responsibility associated with the enormous amount of data available today.

In order to use data for good, businesses must become more aware of the changing landscape around the collection and utilization of data. Consumers are more educated on this issue than ever, and they have increasingly high expectations for how companies treat their information. And alongside consumer changes are public policy shifts. Regulations are being crafted all the time to outline data management ethics that protect consumers and provide guidance to businesses.

How can an online MBA program create better data leaders?

The same values-driven pillars that are guiding the Data for Good conference also guide us at Olin.

Students in Olin’s MBA program learn about social and civic goals at the same time as they practice creating positive business outcomes. They learn, from the beginning, to think of themselves as leaders of social change as well as leaders of prosperous businesses. They learn what it means to be a responsible business leader.

At the CABI research center, our research projects all have a “data for good” focus and aim to solve complex community and societal issues. For example, one of those projects was aimed at combating the opioid epidemic plaguing the United States. This is a current and demanding issue, one that surrounds many of our students’ hometowns and counties. During the project, we utilized advanced analytics and data modeling techniques very similar to those used in a business setting. The same strategies used to create growth in a business are used to create momentum for change.

The outlook we espouse is that successful leaders can take a consistent approach to very different goals, problems, and intended outcomes. That outlook involves first setting up a clearly defined objective or set of objectives. What do you want to achieve? What is the ideal scenario and the next ideal?

See the full 2023 symposium

From there, students learn to determine which key results must be achieved to realize those objectives. How will we know if we have succeeded? Then, with those core pieces of information at their disposal, leaders can establish the specific activities they can perform or initiate that are likely to enable those desired results.

What does data have to do with this methodology? Well, at each point of the strategy, data and technology can be used to help: to help guide the process, help speed things along and add insight. Most importantly, each step we take with data is guided by the objectives and values we’ve set up already; this is how we know what our priorities are.

Culture is key to the courses we teach and how we teach them. The word culture comes from the same French and Latin roots as the word cultivate; this is an action word — cultivation requires a continuous, careful force. This is how we think about crafting culture at Olin. 

We nurture a culture of leading with values. Everything — from the questions we ask to the data-driven actions we take — must be guided by values. Tools are there to help us, but we must first know how to use them for good. And we want to share the knowledge to carefully wield those tools with not just our students but also our community.

Pictured above: Olin’s Seethu Seetharaman, director of the Center for Analytics and Business Insights and W. Patrick McGinnis Professor of Marketing, outlines a data-driven process the center developed to guide economic development decisions in St. Louis. The presentation was at the March 2023 Data for Good symposium.

Data scientists from WashU Olin have developed a process for flagging suspicious transactions across 100 pharmaceuticals—a process with a stunningly high level of precision and one that can immediately take aim at curbing the country’s decades-long opioid epidemic.

Working with a US Drug Enforcement Administration database that tracked six years’ worth of pharmaceutical transactions, the five researchers developed an “anomaly detection” system that could flag future suspicious shipments with 100% precision.

In other words, as the researchers noted, when their process says a transaction is suspicious, it is. Basically, their anomaly detection system doesn’t flag a transaction unless it’s sure—which does mean some bad buys could sneak under the radar if they don’t meet the system’s criteria.

“The signals of anomaly detection are very strong for these egregiously suspicious buyers,” the study’s authors wrote. “This renders our algorithm very valuable for practical use.”

Built to guide the fight

The system was conceived as a tool to help deploy limited resources as authorities tackle illicit trafficking in narcotics.

“Having 100% precision is a very important feature of our (process),” the research team wrote in its paper, under review with the Journal of Marketing. “We are willing to sacrifice some recall (and increase false negative errors) in order to enable the practical adoption of our proposed algorithm.”

The research team—all associated with Olin’s Center for Analytics and Business Insights—includes Annie L. Shi, a doctoral student in marketing; Seethu Seetharaman, co-director of CABI and Olin’s W. Patrick McGinnis Professor of Marketing; Anthony Sardella, CABI senior research advisor; Michael Wall, co-director of CABI and a professor of practice in marketing; and Chenthuran Abeyakaran, BS ’21/SI ’23.

Their work comes under the auspices of the Olin Brookings Commission, a project operated by WashU Olin and the Brookings Institution to address critical policy issues affecting communities. The project is funded through a grant from The Bellwether Foundation.

Organizers of the first commission under the Bellwether grant focused on the opioid epidemic that’s killed half a million individuals in the US in the past two decades, according to the Centers for Disease Control. In July, the federal government reached a $26 billion settlement with the country’s three major drug distributors and pharmaceutical giant Johnson & Johnson for their roles in the epidemic.

“Addressing this issue and enabling distributors to have a predictive system that can be used to flag and halt suspicious orders of opioid drugs, is the central focus of this study,” the research team wrote in its paper, “Nip it in the Bud! Managing the Opioid Crisis: Supply Chain Response to Anomalous Buyer Behavior.”

Training the anomaly detector

The team “trained” its anomaly detection system by using a recently released DEA database called Automated Reports and Consolidated Ordering System—or ARCOS.

That database tracked millions of prescription drug transactions—their manufacture and distribution—spanning 2006 to 2012. By zeroing in on opioid transactions, with the guidance of a smaller database of known illicit transactions, the research team identified patterns of behavior across 40 different criteria. The scholars also developed a standard they called “morphine milligram equivalents”—or “MME”—to create reliable comparisons among various opioid transactions.

Ultimately, they found that seven criteria were enough to create an extraordinarily precise tool to flag suspicious transactions. For example, when looking at “average MME purchased per transaction,” suspicious buyers purchased almost 10 times as much as legitimate buyers. When they looked at “median MME purchased per transaction,” suspicious buyers purchased almost 20 times as much.

In the context of the research team’s detection and alert system, members of the Olin Brookings Commission will likely investigate proposals that affect public policy affecting the trafficking of illicit narcotics. Some of those policy areas could include:

• data sharing and cross-agency communication;
• revised and modernized data reporting;
• funding sources and spending needs for system maintenance;
• response guidance when transactions are flagged.

Pictured at top: top row, Seethu Seetharaman, Michael Wall, Anthony Sardella; bottom row, Annie L. Shi, Chenthuran Abeyakaran.

WashU Olin’s Center for Analytics and Business Insights is on the cusp of creating a machine-learning tool to flag suspicious opioid sales, just as government lawyers announced a multibillion-dollar settlement against three major drug distributors—a settlement that requires a database to track the destination of every opioid dose.

Analyzing a database of more than 400 million opioid transactions from the US Drug Enforcement Administration—a database that includes 277,000 buyers from 2006 to 2012—Olin researchers are building an algorithm that would help law enforcement officials identify shady opioid transactions in the future. The Olin scholars are working to understand key differences in the characteristics and behaviors of convicted buyers who they have identified in the data set to that of unconvicted buyers to inform their model-building approach.

“We want to ‘learn’ what variables distinguish the ‘bad’ buyers from the ‘good’ buyers,” said Seethu Seetharaman, Olin’s W. Patrick McGinnis Professor of Marketing and co-director of the Center for Analytics and Business Insights. “Once we learn the important variables that distinguish bad buyers from good buyers, we train a machine-learning algorithm to take these variables for a given buyer and give a probability score of that buyer being a bad buyer.”

Research to support policy recommendations

Seetharaman, along with CABI co-director Michael Wall, is collaborating with Luoyexin (Annie) Shi, an Olin PhD student in quantitative marketing, on the analysis. The research underpins the first of three projects by the Olin-Brookings Commission. This first project centers on the opioid crisis and what policy measures are needed to confront it long-term.

The entire initiative was made possible by a $750,000 grant from The Bellwether Foundation Inc. This first commission, like the next two, is charged with tackling topics affecting the quality of life for people in St. Louis and across the country.

Seetharaman said the team’s work on the DEA data has quickly shown promise as a law enforcement tool to flag transactions that divert often legitimate prescription therapies toward illicit uses.

“Using the predictive algorithm, the DEA could predict a buyer’s probability of being a bad buyer,” he said. “This way, the DEA can allocate their human and capital resources wisely among high-value leads.”

A well-timed approach

The results come just weeks after lawyers for states, cities and counties plagued by staggering numbers of opioid deaths announced a tentative $26 billion settlement against three distributors of pharmaceutical painkillers: McKesson, Cardinal Health and AmerisourceBergen. The settlement would also include Johnson & Johnson, which no longer supplies raw material for opioids or sells such painkillers in the United States.

“Under the deal, the three distributors, which control 85 to 90% of the market, are required to establish and fund a ‘clearinghouse’ that shows where every opioid dose is headed,” The Washington Post wrote in its report on the settlement. “They must check the database before sending out each shipment of pills and hold theirs back if it appears that the recipient is asking for an extraordinary amount of drugs, a typical sign that some are being diverted and sold on the street.”

According to The Post, more than 100 billion prescription hydrocodone and oxycodone pills were distributed in the United States from 2006 to 2014. Last year, approximately 69,700 people died of overdoses involving opioids in the United States.

Shi said the database she’s analyzing—known as ARCOS, or Automated Reports and Consolidated Ordering System—covers the sale of 14 main varieties of opioids. Those can be further broken down into 170 kinds of substances, and further broken down into 9,133 different products.

The inaugural Olin-Brookings Commission includes a dream team of data scientists, law enforcement authorities, medical professionals and addiction experts with years of industry and policy experience between them. Commission chair Anthony Sardella—founder of evolve24, Olin faculty member and CABI research advisor—serves as a critical conduit between research efforts and the expertise of the commission.

In their current project, supported by CABI, the group is charged with identifying strategies for combatting the epidemic of opioids and recommending any changes in local, state and federal policy that might help curb the problem and sharpen the response from experts.

The commission’s next meeting is set for August 19. The group intends to issues its final report and policy recommendations in early 2022.

In the United States, no manufacturing source exists for more than 80% of the active ingredients in medicines the US Food and Drug Administration deems essential for public health, according to a new study from the Center for Analytics and Business Insights (CABI) at Olin Business School.

“This creates an incredible vulnerability to our public health care system, our health care security,” said Anthony Sardella, an adjunct professor at Olin and senior research advisor at CABI. He conducted the study using proprietary data from across the industry.

Essential medicines include antibiotics, antivirals, blood pressure pills, steroids and many others.

“We have a national security issue related to being able to maintain our public health,” Sardella said, because the US is so reliant on foreign production of active pharmaceutical ingredients (APIs).

“The US Active Pharmaceutical Infrastructure: The Current State and Considerations to Increase US Healthcare Security” focuses on generic medications, which represent more than 80% of US prescriptions.

‘A fragile system’

APIs are the necessary components of medicines that provide patients with the drug therapy they need. The compounds are made into dosages of tablets, solutions and creams.

A June 2021 White House report on supply chain resiliency referenced an epidemic of national drug shortages occurring even before COVID and the pandemic, but “COVID really drew attention to the fragility of our pharmaceutical supply chain,” Sardella said.

The crisis highlighted US reliance on long, complex supply chains and drug shortages in the US. “We really have a fragile system.”

The first of its kind, the study relied on industrywide data from Clarivate, a data and benchmarking company in the healthcare industry that has developed a dataset—Cortellis Generics Intelligence—that provides insights across the sector.

“The data is staggering, as is the implication to our health security,” Sardella said.

A ‘race to the bottom’

His analysis shows the following:

• The majority of large-scale manufacturing sites of APIs are in India and China, while less than 5% of such sites are in the US. (In COVID times, both China and India have threatened to cut off or restrict shipments to the US.)
• Of 52 COVID-related medicines, 75% had no US source of API.
• Of the top 100 generic medicines consumed in the US, 83% had no US source of API.
• Of the 47 most-prescribed antivirals, 97% had no US source of API.
• Of the 111 most-prescribed antibiotics, 92% have no US source of API.

One cause for our weakness in API manufacturing is the “race to the bottom” on pricing against global players, Sardella said. Foreign manufacturers have structural advantages including greater government subsidies, lower costs and fewer regulatory burdens.

He said solutions to protect US healthcare security must address the risk by creating a critical mass of domestic manufacturing infrastructure to protect domestic interests; a level playing field for global competition; and sustainable domestic markets for American manufacturers.

“Tony’s outstanding research shows the impact of being both values-based and data-driven,” said Michael Wall, professor of practice in marketing and entrepreneurship and CABI’s co-director. “This principle is core to Olin and to CABI.”

The new study follows a previous one aimed at understanding the business, societal and governmental environment of the pharmaceutical supply chain. Sardella and Paolo De Bona, a consultant and formerly a staff scientist at WashU’s School of Medicine, conducted an extensive review of academic research, media reports and public policy statements to discern the causes of chronic pharmaceutical shortages in the United States and develop policy solutions to address them.

The work has gained the attention of policymakers in Washington, DC, and compelled the pair to join with the Brookings Institution in hosting a public forum on the subject. 

About the Center for Analytics and Business Insights: CABI serves as a  conduit between business, academia and the broader community, helping leaders better leverage analytics and technology to make a positive and principled difference in organizations, communities and society at large.