Generative artificial intelligence is booming, the post-COVID economy wobbles on, and the climate crisis is growing. Amid this disruption, what practical problems are global businesses trying to solve in 2023?
Each year, the MIT Sloan Master of Business Analytics Capstone Projectpartners students with companies that are looking to solve a business problem with data analytics. The program offers unique and up-close insight into what companies were grappling with at the beginning of 2023. This year, students worked on 41 different projects with 33 different companies. The winning projects looked at measuring innovation through patents for Accenture and using artificial intelligence to improve drug safety for Takeda.
“This annual tradition is an insightful pulse check on the ‘data wish list’ of the industry’s top analytics leaders,” said MIT Sloan lecturerwho leads the Capstone program.
Here are three questions that companies are seeking to answer with analytics.
1. How can data help us identify growth in specific geographic regions?
Businesses looking to open new locations or invest in real estate are using data to find areas that are poised for growth.
Understanding urbanization is important for firms like JPMorgan Chase, which aims to reach new clients and serve existing customers by opening new bank branches in U.S. cities. To get a handle on what areas are likely to grow in the future, the company is using satellite images —including land-cover segmentation from Google — to predict urbanization rates and identify hot spots.
Small and medium-sized businesses account for about 99% of U.S. companies but only 40% of the U.S. economy. Using historic transaction data and U.S. census data, Visa is looking at what parts of the U.S. have the most potential for SMB growthand what levers it can use to help develop those areas, such as helping businesses accept digital transactions.
Asset management firm Columbia Threadneedle wants to identify promising areas for real estate investment in Europe by building a predictive tool for location growth, using factors such as economic drivers, livability, connectivity, and demographics. MBAn students created a tool that predicts long-term growth potential for more than 600 cities and identifies key factors used to make those predictions.
2. How can data help us empower front-line workers?
Employees working directly with customers or in the field often have to make educated guesses and snap decisions. Companies are turning to data analytics to create support tools that will improve efficiency, accuracy, and sales.
Coca-Cola Southwest Beverages is looking to improve how front-line workers assess store inventory and create orders —a process that is now time-consuming and prone to errors. Using demographics, consumption trends, historical sales data, and out-of-stock information, a sales forecast algorithm will improve forecasting, increase sales, and simplify operations.
Handle Global, a health care supply chain technology company, is looking to help hospitals estimate budget allocation and capital expenditures for medical devices, given the churn of assets, variations in types and models, and mergers and acquisitions between manufacturers and hospital systems. The company is looking to develop a decision support tool that uses historic data to make better purchasing decisions.
3. What’s the best way to get the most from large or unwieldy datasets?
While data analytics can produce powerful results, some data is still hard to process, such as unstructured data —data that does not conform to a specific format —or large datasets. Companies are looking for ways to efficiently process and gain insight from this kind of data, which can be time-consuming and inefficient to process.
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Information services company Wolters Kluwer’s compliance business unit helps firms meet regulatory requirements while managing risk and increasing efficiency. But verifying government documents, such as vehicle registrations, can be an error-prone and time-consuming process, and the documents have a high rejection rate. The company is looking to create a document classification system using natural language processing and computer vision that makes paperwork that is usually handled manually more accurate and easier to process.
CogniSure AI was created in 2019 to use technology to solve the problem of unstructured data, which makes it difficult to digitize the insurance underwriting industry. The company is looking to build a generic machine learning tool to process documents that are not yet automated, such as loss runs —claims histories of past losses —which have complex and varied formats and structures.
View all of the capstone projects
For more info Sara Brown Senior News Editor and Writer sbrown1@mit.edu
