business analytics research

Business analytics refers to the statistical methods and computing technologies for processing, mining and visualizing data to uncover patterns, relationships and insights that enable better business decision-making.

Business analytics involves companies that use data created by their operations or publicly available data to solve business problems, monitor their business fundamentals, identify new growth opportunities, and better serve their customers.

Business analytics uses data exploration, data visualization, integrated dashboards, and more to provide users with access to actionable data and business insights.

This IBM ebook uncovers the value of integrating a business analytics solution that turns insights into action.

Read the guide for data leaders

Business intelligence (BI) enables better business decisions that are based on a foundation of business data. Business analytics (BA) is a subset of business intelligence, with business analytics providing the analysis, while the umbrella business intelligence infrastructure includes the tools for the identification and storage of the data that will be used for decision-making. Business intelligence collects, manages and uses both the raw input data and also the resulting knowledge and actionable insights generated by business analytics. The ongoing purpose of business analytics is to develop new knowledge and insights to increase a company’s total business intelligence.

Business analytics can be used to answer questions about what happened in the past, make predictions and forecast business results. 1 An organization can gain a more complete picture of its business, enabling it to understand user behavior more effectively.

Data scientists and advanced data analysts use business analytics to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis which uses previous sales data to estimate customer lifetime value, and cluster analysis for analyzing and segmenting high-usage and low-usage users in a particular area.

Business analytics solutions provide benefits for all departments, including finance , human resources , supply chain , marketing , sales  or information technology , plus all industries, including healthcare , financial services and consumer goods .

Business analytics uses analytics—the action of deriving insights from data—to drive increases in business performance. 4 types of valuable analytics are often used:

As the name implies, this type of analytics describes the data it contains. An example would be a pie chart that breaks down the demographics of a company’s customers.

Diagnostic analytics helps pinpoint the root cause of an event. It can help answer questions such as: What are the series of events that influenced the business outcomes?  Where do the true correlation and causality lie within a given historical time frame? What are the drivers behind the findings? For example, manufacturers can analyze a failed component on an assembly line and determine the reason behind its failure.

Predictive analytics mines existing data, identifies patterns and helps companies predict what might happen in the future based on that data. It uses predictive models that make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season is projected to have warmer temperatures. Predictive modeling 2 also helps organizations avoid issues before they occur, such as knowing when a vehicle or tool will break and intervening before it occurs, or knowing when changing demographics or psychographics will positively or negatively impact their product lines. 

These analytics help organizations make decisions about the future based on existing information and resources. Every business can use prescriptive analytics by reviewing their existing data to make a guess about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use it to turn that data into insights. Here are some of the most common tools, disciplines and approaches:

• Data management: Data management is the practice of ingesting, processing, securing and storing an organization’s data. It is then used for strategic decision-making to improve business outcomes. The data management discipline has become an increasing priority as expanding data stores has created significant challenges, such as data silos, security risks and general bottlenecks to decision-making.
• Data mining or KDD : Data mining, also known as knowledge discovery in data (KDD), is the process of uncovering patterns and other valuable information from large data sets and is a significant component of big data analytics. The growing importance of big data makes data mining a critical component of any modern business by assisting companies in transforming their raw data into useful knowledge.
• Data warehousing : A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources, including apps, Internet of Things (IoT) devices, social media and spreadsheets into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI) and machine learning (ML). A data warehouse system enables an organization to run powerful analytics on large amounts of data (petabytes and petabytes) in ways that a standard database cannot.
• Data visualization : The representation of data by using graphics such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easier to understand, being especially helpful for nontechnical staff to understand analytics concepts, and helping show patterns in multiple data points. Data visualization can also help with idea generation, idea illustration or visual discovery.
• Forecasting : This tool takes historical data and current market conditions and then makes predictions as to how much revenue an organization can expect to bring in over the next few months or years. Forecasts are adjusted as new information becomes available. When companies embrace data and analytics with well-established planning and forecasting best practices, they enhance strategic decision-making and can be rewarded with more accurate plans and more timely forecasts.
• Machine learning algorithms : A machine learning algorithm is a set of rules or processes used by an AI system to conduct tasks, most often to discover new data insights and patterns, or to predict output values from a given set of input variables. Machine learning algorithms enable machine learning to learn, delivering the power to analyze data, identify trends and predict issues before they occur.
• Reporting : Business analytics runs on the fuel of data to help organizations make informed decisions. Enterprise-grade reporting software can extract information from various applications used by an enterprise, analyze the data and generate reports.
• Statistical analysis : Statistical analysis enables an organization to extract actionable insights from its data. Advanced statistical analysis procedures help ensure high accuracy and quality decision-making. The analytics lifecycle includes data preparation and management to analysis and reporting.
• Text analysis : Identifies textual patterns and trends within unstructured data by using machine learning, statistics and linguistics. By transforming the data into a more structured format through text mining and text analysis , more quantitative insights can be found.

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up frequently and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors who do not.

Faster and better-informed decisions: Having a flexible and expansive view of all the data an organization possesses can eliminate uncertainty, prompt an organization to take action faster, and improve business processes. If an organization’s data suggests that sales of a particular product line are declining precipitously, it might decide to discontinue that line. If climate risk impacts the harvesting of a raw material another organization depends on, it might need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies.

How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics enables them to use thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.

Single-window view of information: Increased collaboration between departments and line-of-business users means that everyone has the same data and is talking from the same playbook. Having that single pane of glass shows more unseen patterns, enabling different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.

Enhanced customer service: By knowing what customers want, when and how they want it, organizations encourage happier customers and build greater loyalty. In addition to an improved customer experience , by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.

Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent hands-on analytical and communication skills.

Here are some of the employees that they need to take advantage of the full potential of robust business analytics strategies:

Data scientists: These people are responsible for managing the algorithms and models that power the business analytics programs. Organizational data scientists  either use open source libraries, such as the natural language toolkit (NTLK) for algorithms or build their own to analyze data. They excel at problem-solving and usually need to know several programming languages, such as Python, which helps access out-of-the-box machine learning algorithms and structured query language (SQL) , which helps extract data from databases to feed into a model.

In recent years, an increasing number of schools offer Master of Science or Bachelor’s degrees in data science where students engage in degree program coursework that teaches them computer science, statistical modeling and other mathematical applications.

Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted, and placed into a master database. They are often responsible for helping to ensure that data can be easily collected and accessed by stakeholders to provide organizations with a unified view of their data operations.

Data analysts: They play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they might collect and analyze the data sets and build the data visualizations, or they might take the work created by other data scientists and focus on building strong storytelling for the key takeaways.

To maximize the benefits of an organization’s business analytics, it needs to clean and connect its data, create data visualizations and provide insights on where the business is today while helping predict what will happen tomorrow. This usually involves these steps:

First, organizations must identify all the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.

Unfortunately, much of a company's data remains uncleaned, rendering it useless for accurate analysis until addressed.

Here are some reasons why an organization’s data might need cleaning:

• Incorrect data fields: Due to manual entry or incorrect data transfers, an organization might have bad data mixed in with accurate data. If it has any bad data in the system, this has the potential to render the entire set meaningless.
• Outdated data values: Certain data sets, including customer information, might need editing due to customers leaving, product lines being discontinued or other historical data that is no longer relevant.
• Missing data: Companies might have changed how they collect data or the data they collect, which means historic entries might be missing data that is crucial to future business analysis. Companies in this situation might need to invest in either manual data entry or identify ways to use algorithms  or machine learning  to predict what the correct data should be.
• Data silos: If an organization’s existing data is in multiple spreadsheets or other types of databases, it might need to merge the data so it’s all in one place. While the foundation of any business analytics approach is first-party data (data the company has collected from stakeholders and owns), they might want to append third-party data (data they’ve purchased or gleaned from other organizations) to match their data with external insights.

Companies can now query and quickly parse gigabytes or terabytes of data rapidly with more cloud computing . Data scientists can analyze data more effectively by using machine learning, algorithms, artificial intelligence (AI ) and other technologies. Doing so can produce actionable insights based on an organization’s key performance indicators (KPIs) .

Business analytics programs can now quickly take huge amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders.

Data visualization best practices include understanding which visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to help ensure that the audience understands what they’re viewing.

Ongoing data management is conducted in tandem with what was mentioned earlier. An organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.

Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision-making.

• Financial and operational planning: Business analytics provides valuable insights to help organizations align their financial planning and operations more seamlessly. It does this by setting rules for supply chain management , integrating data across functions, and improving supply chain analytics and demand forecasting.
• Planning analytics: An integrated business planning approach that combines spreadsheets and database technologies to make effective business decisions about topics such as demand and lead generation, optimization of operating costs, and technology requirements based on solid metrics. Many organizations have historically used tools including Microsoft Excel for business planning, but some are transitioning to tools such as IBM Planning Analytics .
• Integrated sales and marketing planning: Most organizations have historical data about their lead generation, sales conversions and customer retention success rates. Organizations looking to create more accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data are using business analytics to allocate resources based on performance or changing demand to meet business objectives.
• Integrated workforce performance planning: As organizations undergo digital transformation and otherwise react to changing landscapes, they might need to ensure they have the right workforce with the right analytical skills. This is especially true in a world where employees are more likely to leave a company for a new job. Workforce performance planning helps organizations understand their workforce requirements, identify and address skill gaps, and better recruit and retain talent to meet the organization's needs today and in the future.

The flexibility of spreadsheets. Control of a database. The power of integrated business planning. Now available as a Service on AWS.

AI-powered automation and insights in Cognos Analytics enable everyone in your organization to unlock the full potential of your data. 

Detects application and business risks affecting the customer experience, enabling users to correlate application service level objectives with underlying infrastructure resourcing.

Learn more about business analytics by reading these blogs and articles. 

IBM Planning Analytics has helped support organizations across not only the office of finance but all departments in their organization.

A growing number of forward-looking companies are successfully navigating complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, and more.

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.

Scale AI workloads for all your data, anywhere, with IBM watsonx.data, a fit-for-purpose data store built on an open data lakehouse architecture.

1 Business intelligence versus business analytics  (link resides outside ibm.com), Harvard Business School. 2  How predictive analytics can boost product development  (link resides outside ibm.com), McKinsey, August 16, 2018.

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Business Analytics

Key concepts, who will benefit, college students and recent graduates, those considering graduate school, mid-career professionals.

What You Earn

Certificate of Completion

Boost your resume with a Certificate of Completion from HBS Online

Earn by: completing this course

Credential of Readiness

Prove your mastery of business fundamentals

Earn by: completing the three-course CORe curriculum and passing the exam

Describing and Summarizing Data

• Visualizing Data
• Descriptive Statistics
• Relationship Between Two Variables

Featured Exercises

Sampling and estimation.

• Creating Representative and Unbiased Samples
• The Normal Distribution
• Confidence Intervals
• Amazon's Inventory Sampling

Hypothesis Testing

• Designing and Performing Hypothesis Tests
• Improving the Customer Experience

Single Variable Linear Regression

• The Regression Line
• Forecasting
• Interpreting the Regression Output
• Performing Regression Analyses
• Forecasting Home Video Units

Multiple Regression

• The Multiple Regression Equation
• Adapting Concepts from Single Regression
• Performing Multiple Regression Analyses
• New Concepts in Multiple Regression
• The Caesars Staffing Problem

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Our difference, about the professor.

Janice Hammond Business Analytics

Dates & eligibility.

No current course offerings for this selection.

All applicants must be at least 18 years of age, proficient in English, and committed to learning and engaging with fellow participants throughout the course.

Learn about bringing this course to your organization .

Learner Stories

Business Analytics FAQs

How does the business analytics certificate program relate to the credential of readiness program.

In addition to being a standalone certificate program, Business Analytics is also one component course of the Credential of Readiness (CORe) program , which also includes Economics for Managers and Financial Accounting . Designed for those interested in learning business fundamentals more broadly, CORe program participants progress through the three courses in tandem, and the program concludes with a final exam.

What are the learning requirements in order to successfully complete Business Analytics, and how are grades assigned?

Participants are expected to fully complete all coursework in a thoughtful and timely manner. This will mean meeting each week’s deadline to complete a module of the course and fully answering questions posed therein, including satisfactory performance on the quizzes at the end of each module (earning an average score of 50% or greater). This helps ensure your cohort proceeds through the course at a similar pace and can take full advantage of social learning opportunities. A module is composed of a series of teaching elements (such as faculty videos, simulations, reflections, or quizzes) designed to impart the learnings of the course. In addition to module and assignment completion, we expect participation in the social learning elements of the course by offering feedback on others’ reflections and contributing to conversations on the platform. Participants who fail to complete the course requirements will not receive a certificate and will not be eligible to retake the course.

More detailed information on individual course requirements will be communicated at the start of the course. No grades are assigned for Business Analytics–participants will either be evaluated as complete or not complete.

For more information on grading, please refer to the Policies Page .

Are there grants for Business Analytics? How do I qualify?

Business Analytics participants may be eligible for financial aid based on demonstrated financial need. To receive financial aid, you will be asked to provide supporting documentation. Please refer to our Payment & Financial Aid page .

What materials will I have access to after completing Business Analytics?

You will have access to the materials in every prior module as you progress through the program. Access to course materials and the course platform ends 60 days after the final deadline in the program. At the end of each course module, you will be able to download a PDF summary of the module’s key takeaways. At the end of the program, you will receive a PDF compilation of all of the module summary documents.

How should I list my certificate on my resume?

Harvard Business School Online Certificate in Business Analytics [Cohort Start Month and Year]

List your certificate on your LinkedIn profile under "Education" with the language from the Credential Verification page:

School: Harvard Business School Online Dates Attended: [The year you participated in the program] Degree: Other; Certificate in Business Analytics Field of Study: Leave blank Grade: Complete Activities and Societies: Leave blank

For the program description on LinkedIn, please use the following:

Business Analytics is an 8-week, 40-hour online certificate program from Harvard Business School. Business Analytics introduces quantitative methods used to analyze data and make better management decisions. Participants hone their understanding of key concepts, managerial judgment, and ability to apply course concepts to real business problems. Business Analytics was developed by leading Harvard Business School faculty and is delivered in an active learning environment based on the HBS signature case-based learning method.

How does HBS Online Business Analytics relate to the Harvard Business Analytics Program?

HBS Online Business Analytics and the Harvard Business Analytics Program are completely separate programs.

HBS Online Business Analytics consists of approximately 40 hours of material delivered entirely online through the HBS Online course platform over an eight-week period.

The Harvard Business Analytics Program is an online certificate program offered through a collaboration between Harvard Business School, the John A. Paulson School of Engineering and Applied Sciences, and the Faculty of Arts and Sciences. The program consists of six core courses, two seminars, and two in-person immersions, and can be completed in as little as nine months.

How does Business Analytics differ from Data Science Principles and Data Science for Business?

These three courses cover different topics related to data and analytics and do so in different ways.

Business Analytics teaches participants to apply basic statistics to real business problems and includes hands-on practice implementing analyses in Excel. The course covers descriptive statistics, sampling and estimation, hypothesis testing, and regression analysis. The course is intended for individuals at all stages of their careers who would like to strengthen their analytical skills, including college students and recent graduates without a background in statistics, those considering an MBA or other graduate program, or professionals seeking data literacy.

Data Science Principles introduces key concepts in data science—such as prediction, causality, visualization, data wrangling, privacy, and ethics—but does so without coding or mathematical application. The course is intended for organizational leaders and managers to be prepared to act on data analysis and to decide whether data science applications are appropriate tools for their businesses or organizations. The course is also well suited for business operations specialists to understand the building blocks of basic data visualization.

Data Science for Business moves beyond the spreadsheet and provides a hands-on approach for demystifying the data science ecosystem and making you a more conscientious consumer of information. Starting with the questions you need to ask when using data for decision-making, this course will help you know when to trust your data and how to interpret the results. By the end of the course, you should understand how to create a data-driven framework for your organization or yourself; develop hypotheses and insights from visualization; identify data mistakes or missing components; and, speak the language of data science across themes such as forecasting, linear regressions, and machine learning to better lead your team to long-term success. You will learn how to create a compelling story that uses proven, collected data to make core business decisions, and explore coding environments such as R and visualization software.

Related Programs

Credential of Readiness (CORe)

Designed to help you achieve fluency in the language of business, CORe is a business fundamentals program that combines Business Analytics, Economics for Managers, and Financial Accounting with a final exam.

Financial Accounting

In this accounting fundamentals course, discover what's behind the numbers in financial statements, such as balance sheets and income statements.

Economics for Managers

See the world through the lens of economics and gain the knowledge and skills to craft successful business strategy.

Business analytics research

Business analytics research requires a rigorous approach to model formulation and estimation as well as the skills to analyse the outputs of these models. Our Business Analytics scholars regularly publish in leading international journals. Particular fields of interest include:

• big data analytics 
• applied econometrics
• electricity markets
• financial econometrics and quantitative risk forecasting
• Bayesian methods
• forecasting, sensitivity analysis
• micro-econometrics, multivariate statistical methods
• panel data methods and models
• scheduling problems
• statistical machine learning
• stochastic non-life insurance and actuarial problems
• supply chains
• testing and modelling structural change
• time series and forecasting.

We welcome approaches from potential PhD students with an interest in any of these areas.

Meet our academics and research students.

Head of Discipline

Associate Professor  Dmytro Matsypura

Deputy Head of Discipline

Professor Artem Prokhorov (Research & Recruitment)

Associate Professor Anastasios Panagiotelis (Education)

Professor  Junbin Gao

Professor  Richard Gerlach

Professor  Daniel Oron

Professor Peter Radchenko

Professor  Bala Rajaratnam

Associate Professor  Boris Choy

Associate Professor Erick Li

Associate Professor  Jie Yin

Associate Professor  Minh Ngoc Tran

Associate Professor  Andrey Vasnev

Senior Lecturers

Dr  Nam Ho-Nguyen

Dr  Stephen Tierney

Dr  Chao Wang

Dr Wilson Chen

Dr  Bern Conlon

Dr Qin Fang

Dr  Simon Loria

Dr  Pablo Montero-Manso

Dr Bradley Rava

Dr  Marcel Scharth

Dr Firouzeh Taghikhah

Dr Alison Wong

Adjunct Senior Lecturer

Dr  Steven Sommer

Adjunct Lecturer

Research associates, postdoctoral research associate.

Dr  Tomas Ignacio Lagos

Honorary and emeritus staff

Emeritus professor.

Professor Eddie Anderson

Professor Robert Bartels

Honorary Professors

Professor Robert Kohn

Professor Ganna Pogrebna

Professor Michael Smith

Honorary Associates

John Goodhew

Hoda Davarzani

John Watkins

David Grafton

Yakov Zinder

Higher degree by research students

View our current  higher degree by research students . 

Research groups

Time series and forecasting research group, productivity, efficiency and measurement analytics (pema), research seminars.

The Discipline of Business Analytics holds a regular seminar series. Seminars are usually held on Fridays at 11am in Room 5070, Abercrombie Building (H70) . The seminar organiser is Bradley Rava .

Please email  [email protected]  if you wish to be included in the BA seminar series mailing list.

Below is an outline of our recent and upcoming activity. 

2018 seminars

Finding critical links for closeness centrality.

• Date: 10 Aug 2018 at 11am
• Venue: Rm 3010, Abercrombie Building (H70)
• Speaker: Professor Oleg Prokopyev, Department of Industrial Engineering, University of Pittsburg

Risk management with POE, VaR, CVaR and bPOE: Applications in finance

• Venue: Rm 4150, Abercrombie Building (H70)
• Speaker: Professor Stan Uryasev, Department of Industrial and Systems Engineering, University of Florida

My experience as EIC of OMEGA

• Date: 9 Aug 2018 at 11am
• Venue: Rm 2240, Abercrombie Building (H70)
• Speaker: Prof Benjamin Lev, LeBow College of Business, Drexel University

Heterogeneous component MEM models for forecasting trading volumes

• Date: 27 Jul 2018 at 11am
• Venue: Rm 3190, Abercrombie Building (H70)
• Speaker: Professor Giuseppe Storti, Department of Economics and Statistics, University of Salerno UNISA

Realised stochastic volatility models with generalised asymmetry and periodic long memory

• Date: 1 Jun 2018 at 11am
• Venue: Rm 2290, Abercrombie Building (H70)
• Speaker: Professor Manabu Asai, Faculty of Economics, Soka University

Improving hand hygiene process compliance through process monitoring in healthcare

• Date: 24 May 2018 at 11am
• Venue: Rm 1080, Abercrombie Building (H70)
• Speaker: Associate Professor Chung-Li Tseng, Operations Management, UNSW Business School

Exact IP-based approaches for the longest induced path problem

• Date: 18 May 2018 at 11am
• Speaker: Dr Dmytro Matsypura, Discipline of Business Analytics, The University of Sydney

Bayesian deep net GLM and GLMM

• Date: 11 May 2018 at 11am
• Speaker: Mr Nghia Nguyen, Discipline of Business Analytics, The University of Sydney

Computational intelligence-based predictive snalytics: Applications with multi-output support vector regression

• Date: 13 Apr 2018 at 11am
• Speaker: Professor Yukun Bao, School of Management, Huazhong University of Science and Technology (HUST)

Entrywise functions preserving positivity: Connections between analysis, algebra, combinatorics and statistics

• Date: 5 Apr 2018 at 3.30pm
• Venue: Rm 3120, Abercrombie Building (H70)
• Speaker: Associate Professor Apoorva Khare, Department of Mathematics, Indian Institute of Science

Large-scale multivariate modelling of financial asset returns and portfolio optimisation

• Date: 23 Feb 2018 at 11am
• Speaker: Professor Marc Paolella, Department of Banking and Finance, University of Zurich

Statistical inference on the Canadian middle class

• Date: 16 Feb 2018 at 11am
• Speaker: Professor Russell Davidson, Department of Economics, McGill University

2017 seminars

Heterogeneous structural breaks in panel data models.

• Date: 1 Sep 2017 at 11am
• Venue: Rm 1050, Abercrombie Building (H70)
• Speaker: Dr Wendun Wang, Erasmus School of Economics, Erasmus University

Externalities, optimisation and regulation in queues

• Date: 25 Aug 2017 at 11am
• Speaker: Dr Nadja Klein, Melbourne Business School, University of Melbourne

A partial identification subnetwork approach to discrete games in large networks: An application to quantifying peer effects

• Date: 11 Aug 2017 at 11am
• Speaker: Professor Tong Li, Department of Economics, Vanderbilt University

An introduction to knowledge management and some common entry points

• Date: 4 Aug 2017 at 11am
• Venue: Rm 2090, Abercrombie Building (H70)
• Speaker: Prof Eric Tsui, Department of Industrial and Systems Engineering, Hong Kong Polytechnic University

Two applications of serial inventory systems

• Date: 21 Jul 2017 at 11:00am
• Venue: Rm 5070, Abercrombie Building (H70)
• Speaker: Associate Professor Ying Rong, Operations Management, Shanghai Jiao Tong University

Methods of matrix factorisation

• Date: 2 Jun 2017 at 11am
• Speaker: Professor Wray Buntine, Master of Data Science, Monash University

Optimisation and equilibrium problems in engineering

• Date: 26 May 2017 at 11am
• Speaker: Prof Steven Gabriel, Department of Mechanical Engineering, University of Maryland

Exact subsampling MCMC

• Date: 12 May 2017 at 11am
• Speaker: Dr Matias Quiroz, UNSW Business School, University of New South Wales

Effects of taxes and safety net pensions on life-cycle labor supply, savings and human capital: The case of Australia

• Date: 21 Apr 2017 at 11am
• Speaker: Dr Fedor Iskhakov, College of Business and Economics, Australian National University

Trial-offer markets with social influence: The impact of different ranking policies

• Date: 18 Apr 2017 at 11am
• Venue: Rm 5040, Abercrombie Building (H70)
• Speaker: Dr Gerardo Berbeglia, Melbourne Business School, University of Melbourne

Conditionally optimal weights and forward-looking approaches to combining forecasts

• Date: 7 Apr 2017 at 11am
• Speaker: Dr Andrey Vasnev, Discipline of Business Analytics, The University of Sydney

A flexible generalised hyberbolic option pricing model and its special cases

• Date: 31 Mar 2017 at 11am
• Speaker: Dr Simon Kwok, School of Economics, The University of Sydney

Scheduling with variable processing times: Complexity results and approximation algorithms

• Date: 24 Mar 2017 at 11:00am
• Speaker: Associate Professor Daniel Oron, Discipline of Business Analytics, The University of Sydney

Modelling insurance losses using contaminated generalised beta type-2 distribution

• Date: 17 Mar 2017 at 11am
• Speaker: Dr Boris Choy, Discipline of Business Analytics, The University of Sydney

How (not) to get what you ask for: Survey mode effects on self-reported substance use

• Date: 24 Feb 2017 at 11am
• Speaker: Dr Bin Peng, School of Mathematics, University of Technology Sydney

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Center for Business Analytics Research

What is business analytics.

Business analytics is the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques and the communication of these results to customers, partners and colleagues. Business analytics is used by organizations committed to data-driven decision-making.

Business analytics encompasses the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods.

• Learn more about AIO research and faculty

Business analytics vs. business intelligence

• To develop new techniques to solve business problems
• To explore the limits of existing techniques
• To build models for solving complex business problems
• To help businesses manage uncertainties by mining data and studying business processes
• To engage with organizations and help them with big data and other decision-making issues in order to achieve significant bottom line improvements
• To educate and develop the next generation of business leaders proficient in business analytics
• Courses for undergraduate and graduate students
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• Application and development of latest methods and tools of business analytics developed in artificial intelligence, operations research, management sciences and statistics communities

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Harvard Business Analytics Program / Blog

Business Intelligence vs. Business Analytics

Business intelligence and business analytics are two terms that are often used interchangeably by professionals. But business experts frequently debate whether business intelligence is a subset of business analytics, or vice versa, and there is often an overlap between how the two fields are defined. 

Understanding the differences between business intelligence and business analytics can help leaders choose the appropriate tools and talent to help grow their businesses. Current and aspiring business students can also use this knowledge to assess what educational programs can prepare them best for a successful career in their chosen field. 

What Is Business Intelligence?

Traditionally, business intelligence has been defined as the use of data to manage day-to-day operational management within a business. Leaders employ business intelligence tools and experts when they want to collect and house data about current operations, maximize workflow, produce informative reports, and achieve their current business goals.

Business intelligence tools can include a variety of software tools and other systems. Some of these include spreadsheets, online analytical processing, reporting software, business activity monitoring software, and data mining software. Some experts would also argue that business intelligence tools also include the more predictive and statistical tools used in business analytics.

Overall, business intelligence helps leaders navigate organizational and industry-related challenges and ensures that companies stay focused on their primary target to successfully get where they want to go.

What Is Business Analytics?

Business analytics has generally been described as a more statistical-based field, where data experts use quantitative tools to make predictions and develop future strategies for growth. 1  For example, while business intelligence might tell business leaders what their current customers look like, business analytics might tell them what their future customers are doing. Some experts use business analytics as a term to describe a set of predictive tools used within the realm of business intelligence.

Business analytics tools are employed for many functions, including correlational analysis, regression analysis, factor analysis, forecasting analysis, text mining, image analytics, and others. 2  Many of these tools require companies to hire or contract data scientists and have increased the demand for training in business analytics.

Business Intelligence vs. Business Analytics 

As noted above, there are several key differences between how experts define business intelligence versus business analytics. These variances reflect trends in business language and job growth, the size and age of an organization, and whether an organization desires to invest in a present or future focus. Business leaders must consider these differences when they decide how much to invest in contracting business intelligence and analytical tools for their organizations.

1. Trends in Language and Jobs

Business analytics is a newer, trendier term than business intelligence, even though there is significant overlap in their definitions and usage. More people have conducted Google searches of business analytics than for business intelligence, reflecting the growth of business analytics as an umbrella term rather than strictly a description of statistical and predictive tools. 3

This upsurge in references to analytics perhaps reflects the growth in the field of data science and analytics. There is a current talent shortage in the field, as companies compete to hire limited numbers of data scientists, data engineers, and directors of analytics. It is expected that this demand will grow by almost 40 percent by 2021. 4

2. Size and Age of the Organization

The size of an organization can also determine whether business intelligence or analytical tools are employed. Traditionally marketed toward larger enterprises, business intelligence tools may also be used at  smaller companies  that may lack staff with a background in data science but want to use corporate data to improve functioning or plan for the future. 5  Regardless of size, most organizations want tools that can help with both current operations and predictive planning.

The age of an organization can also influence a manager’s decision to use intelligence or analytics tools. If a business is brand new or has recently undergone massive changes, then predictions about business trends via business analytics may be the most useful. They can be particularly appealing for start-ups that have access to large amounts of data and want to be competitive with larger, more established companies.

For well-established organizations that simply want to learn more about organizational process or employee performance, business intelligence tools might be more appropriate. However, most organizations generally will want some combination of the two.

3. Present vs. Future Focus

A common school of thought for distinguishing between business intelligence and business analytics is the disparity between focusing on the present or future challenges of an organization. Some experts argue that business intelligence involves using historical data to make decisions about how a company should run in the present day, whereas business analysis may use historical data to predict what might happen in the future or how an organization can move forward. 6

A present focus using business intelligence may be more useful for leaders who are generally satisfied with business operations but want to identify “pain points” in workflow, increase efficiency, streamline processes, or meet a specific goal. But for those who want to change their business model or major functioning within an organization, business analytics might provide more useful insights. 7

Businesses have both a present and future focus—they want to maximize their existing strategies but also make space for exploring new ones.

Career Outcomes: Choosing Business Intelligence or Business Analytics

Career outcomes in business intelligence or business analytics will vary depending on your training and the type of position sought. Like their definitions, there is some inevitable overlap in whether certain positions are described as using business intelligence, business analytics, or both.

Many careers require education and training in business intelligence. Project managers, consultants, directors, analysts, and other specialists often use business intelligence tools to improve workflow, meet organizational goals, and reduce operating costs. All of these positions may require some knowledge of the tools used by data scientists, but they also require “soft skills” as well, such as communicating findings to project managers and ensuring that projects are completed in a timely and cost-effective manner. 

Business intelligence analyst is one of the most popular careers in the field of business intelligence. Analysts use available historical organizational data as well as market data to help companies maximize profit. They also must be able to effectively communicate this data to project managers and other leaders.

Business analytics  continues to be a rapidly growing field, living up to the  Harvard Business Review’ s  2012 declaration that it was the “sexiest job of the 21st century.” 8  This growth reflects the increased use of big data across all sectors to drive change and decision-making processes. In response, many business schools have added master’s programs or certificate programs in business analytics to match this demand, such as the Harvard Business Analytics Program. 9

The program is specifically for aspiring and current leaders who are interested in advancing their analytical skills to drive change and boost innovation using data. Students learn to analyze, interpret, and translate data to ultimately make strategic decisions at the highest levels within their organization.

As trends and terminology evolve and expand, business leaders will continue to debate how to define business intelligence and business analytics. But it is safe to say that most businesses will invest in both their current operations and future success, ensuring the need for both sets of tools and experts. 

• https://hbr.org/2013/12/get-a-better-return-on-your-business-intelligence
• https://www.educba.com/business-intelligence-vs-business-analytics/
• https://www.forbes.com/sites/bernardmarr/2016/02/04/the-18-best-analytics-tools-every-business-manager-should-know/
• https://www.betterbuys.com/bi/business-intelligence-vs-business-analytics/
• https://analytics.hbs.edu/blog/data-science-job-market/
• https://selecthub.com/business-intelligence/business-intelligence-vs-business-analytics/
• https://www.betterbuys.com/bi/business-intelligence-vs-business-analytics/ 
• https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
• https://analytics.hbs.edu/#descriptions

Citation for this content:  Harvard Business Analytics Program

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From April 14-16, join more than 700 leading analytics professionals and industry experts in discovering new mathematical solutions to business problems, networking to advance your career, and recognizing individual and team efforts within your field with the most prestigious awards in analytics and operations research.

Attendees will experience a conference schedule featuring three full days of more than 150 presentations and panels across more than a dozen tracks by leading analytics and data science professionals from around the world, as well as the opportunity to enjoy all that Orlando has to offer.

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Designed for analytics professionals and data scientists who want to empower organizations to make data-driven decisions, this conference provides access to more than  100 presentations and numerous networking opportunities  with other  leaders and experts  who are using analytics, data science, AI, machine learning, mathematics, and other decision sciences to  save lives, save money, and solve problems .

Unlike other conferences that focus on specific industries or software, the INFORMS Analytics Conference brings together insights and perspectives from a broad spectrum of organizations and  celebrates the   tremendous economic and social impact of analytics   through global competitions  such as the world-renowned Franz Edelman Award, Daniel H. Wagner Prize,  UPS George Smith Prize, Early Career Practitioner Award,  and Innovative Applications in Analytics Award.

Keynote Speakers

Tom Koulopoulos

Author, Futurist & Leader

Embracing the Revolution: Harnessing the Transformative Power of Data Science and Analytics in the Era of AI

Tom Koulopoulos is Chairman and founder of Delphi Group, a 30-year-old Boston-based think tank named one of the fastest growing private companies by Inc. Magazine, and the founding partner of Acrovantage Ventures, which invests in early-stage technology startups. He is also the author of 14 books, an inventor with several patents, an Inc.com columnist, the past Executive Director of the Babson College Center for Business Innovation, the past director of the Dell Innovation Lab, and a professor at Boston University. His insights have received wide praise from luminaries such as the late Peter Drucker, the father of modern management; Dee Hock, founder of Visa International; and Tom Peters, who called his writing, “a brilliant vision of where we must take our enterprises to survive and thrive.” His Inc. column is read by over one million people yearly. Mr. Koulopoulos’ fourteen books include  Reimagining Healthcare ,  Revealing the Invisible ,  The Gen Z Effect ,  The Innovation Zone , and  Cloud Surfing . His latest book,  Gigatrends , looks at the six tech trends that are shaping the future of how we live, work, and play. Tom’s keynotes blend humor, insight, and a cutting-edge view of the future in a way that’s customized to each audience. According to Tom’s mentor, Peter Drucker, Tom’s writing “makes you question not only the way you run your business but the way you run yourself.”

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Business Analytics Research

Our faculty explore many themes within business analytics, decision sciences and quantitative analysis: healthcare analytics, analytics-driven decision making, social media analytics, workforce analytics, sensor-based and real-time analytics, the wisdom of the crowd, online platforms and recommendation systems.

Healthcare analytics

This area explores healthcare issues related to patients, providers and payers. With respect to patients, our faculty have developed patient-centric healthcare and disease progression models. Relating to providers and payers, our faculty have developed decision support for hospitals and examined policies for healthcare coalitions including insurers and Medicare, respectively. Our faculty have provided analytical support to the Food and Drug Administration that enables the FDA to more accurately predict pharmaceutical product quality problems. Our faculty also examine social support of online health communities that can potentially connect patients with providers. Moreover, our faculty also conduct field studies to leverage social influence to motivate healthy behavior change.

Analytics-driven decision-making

Our faculty employ many different analytic techniques that support decision making. For example, in the context of healthcare operations, our faculty have employed Markov decision processes as a mathematical framework to model decision making. For designing service systems wherein customer heterogeneity information is available, our faculty have employed a queuing model to better manage customer expectations that are waiting for a service. In the context of humanitarian operations, our faculty have employed multi-period optimization to determine the location of global vehicle logistics hubs to respond to mega disasters. In the context of sustainable operations, our faculty have employed multi-period stochastic optimization models to analyze investments in renewable energy and energy storage.

Social media analytics

This area focuses on big data environments through the analysis of social media data available online. Our faculty are interested in establishing connections between data generated by users in social media platforms and business, organizational and public policy outcomes. Work in this area typically combines recent techniques in machine learning and statistics to extract information from large, often unstructured datasets. In e-commerce setting, our faculty have analyzed the role of live chatting tools, and quantified the substitution pattern between live chat conversations and seller reputation.

Workforce analytics

Providing actionable insights to address workforce issues such as acquiring, rewarding, and retaining talent are prominent on the corporate agenda. Our faculty have explored staffing and turnover issues at hospitals, designed the staffing strategy for the base agents and cloud-based agents in call centers, compared the effect of hiring IT and non-IT labor from structurally diverse network of firms, and examined the role of IT in the displacement of service workers.

Sensor-based and real-time analytics

Industry 4.0 has brought about a world that is increasingly connected. At the heart of many of these connections are sensors embedded in numerous technologies. These sensors play a pivotal role in society today, particularly in automation, smart homes, Internet of Things, and other environments. Our faculty focus on developing novel approaches for analyzing the data generated from these devices for numerous applications, including mobile health, ICS cybersecurity, and AI-based cybersecurity applications.

Wisdom of the crowd

This research includes the development of quantitative procedures for combining information from multiple experts to estimate uncertain or unknown variables. These methods allow a manager to improve decision making by harnessing the wisdom of the crowd. Our faculty have also explored factors that can enhance the crowd-based donation platforms for charitable fund-raising. Using a latent class model, we identify leaders in the crowd, and provide insights into how to alleviate the rich get richer problem.

Online platforms and recommendation systems

Online platforms—for example, Craigslist, Amazon Marketplace, Airbnb and Uber—connect buyers with sellers, thus, helping manage demand and supply in an aggregated fashion. Our faculty are interested in the design and operations of online marketplaces with an emphasis on optimizing supply-side operations. On such platforms, a recommendation system is an information filtering system that predicts preference, or, rating, that a user would give to an item. Our faculty research seeks to quantify recommendation stability, the measure of the extent to which a recommendation algorithm provides predictions that are consistent with each other, and proposes an approach to develop recommendation stability.

Examples of Research in Business Analytics, Decision Sciences, and Operations Research

A recipe for retail assortment.

As the variety of products on the market continues to expand, retailers are faced with difficult decisions about their stock assortments. Taking into account the heterogeneity of customer preferences, customer’s willingness to substitute a second or third choice if their first choice is unavailable, and the dissatisfaction customers experience when they cannot purchase their preferred brands, M.A. Venkataramanan and his colleagues propose and test a model for retail category assortment that allows managers to balance customer satisfaction with short-term profit. See the associated video here.

• Ravi Anupindi, Sachin Gupta and M.A. Venkataramanan, “Managing Variety on the Retail Shelf: Using Household Scanner Panel Data to Rationalize Assortments,”  Retail Supply Chain Management , N. Agarwal and S.A. Smith (ed.), pages 155–182, 2009.

Smart Nursing Scheduling Policies: Savings and Staff Satisfaction

Concerns over patient-care quality have prompted both state and national legislators to consider mandating nurse-to-patient ratios in hospitals. At the same time, the country continues to face shortages in qualified nursing staff. These dual constraints place a burden on hospital administrators to develop nursing schedules that not only meet specified ratios while keeping costs in check, but are also attractive to nurses who are in high demand. Kurt Bretthauer and his colleagues address this problem by proposing a scheduling model that takes into account not only costs and nurse-to-patient ratios but also the desirability of the schedule from the nurse’s perspective. See the associated video here.

• P. Daniel Wright, Kurt M. Bretthauer, Murray J. Côté, “Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages,”  Decision Sciences Journal , 37(1), pages 39–70, 2006.

Unblocking Patient Flow

The efficient flow of hospital patients between different units of care is not only a crucial element of effective treatment but also an important consideration in conserving resources and managing revenue. With this study, the researchers consider the “blocking” problem that occurs when at-capacity units cannot accommodate patients, causing some patients to remain in a higher unit of care than they require and others to be turned away from the hospital. By creating a simplified and highly accurate model of patient flow, Kurt Bretthauer and his colleagues provide a tool for determining the optimal mix of beds within a hospital’s budget constraints and specified management objectives.

• Kurt M. Bretthauer, H. Sebastian Heese, Hubert Pun, and Edwin Coe, “Blocking in Healthcare Operations: A New Heuristic and an Application,”  Production and Operations Management , 20 (3), pages 375-391, 2011.

Stability of Recommendation Algorithms

Recommendation stability measures the extent to which a recommendation algorithm provides predictions that are consistent with each other. Several approaches have been proposed in prior work to defining, measuring, and improving the stability of recommendation algorithms. Previous studies have focused primarily on understanding and evaluating recommendation stability in prediction-oriented settings, i.e., recommendation settings where it is crucial to provide the precise prediction of a user’s preference rating for an item. Jingjing Zhang and her colleagues build on prior work by generalizing the notion of stability to a broader set of recommendation settings and developing corresponding stability metrics. They provide a comprehensive empirical analysis of classification, ranking, and top-K stability performance of popular recommender algorithms on real-world rating data sets under a variety of settings.

• G. Adomavicius and Jingjing Zhang “Classification, Ranking and Top-K Stability of Recommendation Algorithms.”  INFORMS Journal on Computing , 28 (1), pages 129-147, 2016.

Simple and Effective Decision Support Policy for Mass-Casualty Triage

In the aftermath of a mass-casualty incident, effective policies for timely evaluation and prioritization of patients can mean the difference between life and death. While operations research methods have been used to study the patient prioritization problem, prior research has either proposed decision rules that only apply to very simple cases, or proposed formulating and solving a mathematical program in real time, which may be a barrier to implementation in an urgent situation. Alex Mills connects these two regimes by proposing a general decision support rule that can handle survival probability functions and an arbitrary number of patient classifications. The proposed survival lookahead policy generalizes not only a myopic policy and a cμ type rule, but also the optimal solution to a version of the problem with two priority classes. This policy has other desirable properties, including index policy structure. Using simple heuristic parameterizations, the survival lookahead policy yields an expected number of survivors that is almost as large as published methods that require mathematical programming, while having the advantage of an intuitive structure and requiring minimal computational support.

• A. F. Mills “A Simple Yet Effective Decision Support Policy for Mass-Casualty Triage.”  European Journal of Operational Research , 253(3), pages 734-745, 2016.

Extracting the Wisdom of Crowds When Information Is Shared

Using the wisdom of crowds—combining many individual judgments to obtain an aggregate estimate—can be an effective technique for improving judgment accuracy. In practice, however, accuracy is limited by the presence of correlated judgment errors, which often emerge because information is shared. To address this problem, Asa and his colleague propose an aggregation procedure called pivoting that adjusts a crowd's average judgment away from the average estimate of the judgment that all other respondents will provide on average. Data from four studies suggests that pivoting can significantly outperform classical averaging procedures.

• Asa Palley and J.B. Soll (forthcoming) “Extracting the Wisdom of Crowds When Information Is Shared.” Management Science .

Are All Spillovers Created Equal? A Network Perspective on IT Labor Movements

This study examines how characteristics of a firm’s labor-flow network affect firm productivity. Using employee job histories, Fujie and her colleagues construct inter-firm labor-flow networks for both IT-labor and non-IT labor and analyze how a firm’s network structure for the two types of labor affects firm performance. They find that hiring IT workers from a structurally-diverse network of firms can substantially improve firm productivity, but the same is not true for hiring non-IT labor. These results demonstrate the importance of incorporating a network perspective in understanding the full impact of spillover effects from organizational hiring activities.

• L. Wu, Fujie Jin, and Lorin Hitt (forthcoming) “Are All Spillovers Created Equal? A Network Perspective on IT Labor Movements.” Management Science .

Good Intentions, Bad Outcomes: The Effect of Mismatches in Social Support and Health Outcomes in an Online Weight Loss Community

Although social support has been recognized for its effectiveness in promoting health, that social support may not always lead to good outcomes. By analyzing participants of an online weight‐loss community, Lucy shows that providing and receiving support affects weight‐loss outcomes in different ways. While providing support is positively associated with weight‐loss progress, receiving support could hinder the weight‐loss outcome for a person with high self‐efficacy. She finds evidence that the match between needed and received social support also influences individuals’ performance in the weight‐loss process, and a mismatch of social support could affect weight‐loss outcomes negatively. These findings can help maximize the usefulness of social support for participants, clinicians who refer individuals to online weight‐loss communities, and for the online community designers.

• L. Yan (2018) “Good Intentions, Bad Outcomes: The Effect of Mismatches in Social Support and Health Outcomes in an Online Weight Loss Community.” Production and Operations Managemen t 27(1): 9-27.

Editorial Positions

Our faculty members hold several positions for the top journals in the field, including:

• Operations Research - Associate Editor: Rod Parker, 2012-present
• Decision Sciences Journal - Departmental Editors: Gil Souza, 2017-present; Alan Dennis, 2017-present
• Operations Management Education Review - Co-Editor: Kyle Cattani, 2018-present; Editorial Board: Doug Blocher, 2002-present
• Journal of Business Analytics – Associate Editors: Jingjing Zhang, 2018-present; Vijay Khatri, 2018-present
• European Journal of Operational Research - Guest Co-Editor, Special Issue on Humanitarian Operations Research: Alfonso Pedraza-Martinez, 2017.

Recent Selected Publications

• K. Bimpikis, W.J. Elmaghraby, K. Moon and W. Zhang (forthcoming) “Managing Market Thickness in Online B2B Markets.”  Management Science .
• H. Ahn, D.D. Wang, O. Q. Wu (forthcoming) “Asset Selling Under Debt Obligations.” Operations Research .
• S.A. Yang, N. Bakshi, C.J. Chen (forthcoming) “Trade Credit Insurance: Operational Value and Contract Choice . ” Management Science .
• A.C. Johnston, M. Warkentin,  A.R. Dennis , and M. Siponen (forthcoming) “Speak Their Language: Designing Effective Messages to Improve Employees’ Information Security Decision Making.”  Decision Sciences Journal, 50(2): 245-284 .
• J. Mejia and C. Parker (forthcoming) “When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms.” Management Science .
• S. Sharma and A. Mehra (forthcoming) “Entry of Platforms into Complementary Hardware Access Product Markets.”  Marketing Science . 
• S. Samtani, H. Zhu, and H. Chen (forthcoming) “Proactively Identifying Emerging Hacker Threats on the Dark Web: A Diachronic Graph Embedding Framework (D-GEF).” ACM Transactions on Privacy and Security (TOPS).
• S. Samtani, M. Kantarcioglu, and H. Chen (forthcoming) “Privacy Analytics.” ACM Transactions on Management Information Systems (TMIS).
• J. Mejia and C. Parker (2020) “Underrepresented and LGBT in the Sharing Economy: Bias and Financial Incentives in Ridesharing Platforms.” Management Science .
• B. Ata and X. Peng (2020) “An Optimal Callback Policy for General Arrival Processes: A Pathwise Analysis.” Operations Research, 68(2), 327-347.
• X. Cheng, J. Zhang , and L. Yan (2020) “Understanding the Impact of Individual Users’ Rating Characteristics on Predictive Accuracy of Recommender Systems.” INFORMS Journal on Computing, 32(2): 303-320.
• R. Kleber, M. Rainer, M. Reimann, G. C. Souza, and W. Zhang (2020) “Two-sided Competition with Vertical Differentiation in Both Acquisition and Sales in Remanufacturing.” European Journal of Operational Research , 284(2): 572-587.
• J. Schoenfelder, K. Bretthauer, P.D. Wright, and E. Coe (2020) "Nurse Scheduling with Quick-Response Methods: Improving Hospital Performance, Nurse Workload, and Patient Experience." European Journal of Operational Research , 283(1): 390-403.
• A.Borenich, Y. Dickbauer, M. Reimann, and G. C. Souza (2020) “Should a Manufacturer Sell Refurbished Returns on the Secondary Market to Incentivize Retailers to Reduce Consumer Returns?” European Journal of Operational Research , 282(2): 569-579.  
• D. Cho and K. Cattani (2019) “The Patient Patient.” Decision Sciences Journal , 50(4): 756-785.
• A. Palley and J.B. Soll (2019) “Extracting the Wisdom of Crowds When Information is Shared.” Management Science, 65(5): 1949-2443.
• J. Mejia , A. Mejia, and F. Pestilli (2019) “Open Data on Industry Payments to Healthcare Providers Reveal Potential Hidden Costs to the Public.” Nature Communications , 10(1): 1-8.
• P. Serex and J.D. Blocher (2019) “Teaching Critical Thinking and Problem Solving in a Business Analysis Course,” Operations Management Education Review , 13: 143-172.
• V. Khatri and B. Samuel (2019) “The Current and Future Use of Various Analytics Applications for Managerial Work: Trends in Four Business Functions.”  Communications of the ACM , 62(4): 100-108.
• B. Ata and X. Peng (2018) “An Equilibrium Analysis of a Multiclass Queue with Endogenous Abandonments in Heavy Traffic.” Operations Research 66(1): 163-183.
• J. Ferrer, J. Martin, M.T. Ortuño, A. J. Pedraza-Martinez, G. Tirado and B. Vitoriano (2018) “Multi-Criteria Optimization for Last Mile Distribution of Disaster Relief Aid: Test Cases and Applications.” European Journal of Operational Research, 269(2): 501-515.
• M. Besiou, A. J. Pedraza-Martinez , and L. N. Van Wassenhove (2018) “OR Applied to Humanitarian Operations.” European Journal of Operational Research, 269(2): 397-405.
• R. Kleber, M. Reimann, G. C. Souza , and W. Zhang (2018) “On the Robustness of the Consumer Homogeneity Assumption with Respect to the Discount Factor for Remanufactured Products.” European Journal of Operational Research, 269(3): 1027-1040.
• A. Aydin and R.P. Parker (2018) “Innovation and Technology Diffusion in Competitive Supply Chains,” European Journal of Operational Research, 265(3): 1102-1114.
• L. Wu, F. Jin , and L. Hitt (2018) “Are All Spillovers Created Equal? A Network Perspective on IT Labor Movements.” Management Science, 64(7): 2973-3468.
• D. Dey, A. Kim , and A. Lahiri (2018) “Online Piracy and the ‘Longer Arm’ of Enforcement.” Management Science, 65(3): 955-1453.
• S. Villa and J.A. Castañeda (2018) “Transshipments in Supply Chains: A Behavioral Investigation.” European Journal of Operational Research , 269(2): 715-729.
• S. Samtani, S. Yu, H. Zhu, M. Patton, J. Matherly, and H. Chen (2018) “Identifying SCADA Systems and their Vulnerabilities on the Internet of Things: A Text Mining Approach.” IEEE Intelligent Systems , 33(2): 63-73.
• M. Arikan, B. Ata, J. Friedewald, and R. Parker (2018) “Enhancing Kidney Supply Through Geographic Sharing in the United States.” Production and Operations Management, 27(12): 2103-2121.

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