Generative artificial intelligence is booming, the post-COVID-19 economy is teetering, and the climate crisis is growing. Amid this turmoil, what practical problems will global companies be trying to solve in 2023?
Each year, the MIT Sloan Master of Business Analytics Capstone Project partners students with companies looking to solve business problems with data analysis. This program provides unique and detailed insight into what companies were working on at the beginning of 2023. This year, the student worked on 41 different projects with his 33 different companies. The winning projects focused on measuring innovation through patents at Accenture and using artificial intelligence to improve drug safety at Takeda.
“This annual tradition is an insightful pulse check on the 'data wish lists' of the industry's top analytics leaders,” said MIT Lecturer Sloan. The person who leads the capstone program.
Here are three questions companies are trying to answer with analytics.
1. How can data help identify growth in specific regions?
Companies looking to open new locations or invest in real estate are using data to find areas for growth.
Understanding urbanization is important for businesses that: JP Morgan Chaseaims to acquire new customers and serve existing customers by opening new bank branches in U.S. cities. To understand which regions are likely to grow in the future, the company uses satellite imagery, including Google's land cover segmentation, to predict urbanization rates and identify hotspots.
Small businesses make up about 99% of U.S. businesses, but only 40% of the U.S. economy. Using historical transaction data and US Census data, visa considers which regions of the U.S. have the highest potential for small business growth and what tools are available to support development in these areas, including helping businesses embrace digital transactions. doing.
Asset management firm Columbia Threadneedle identifies promising regions for real estate investment in Europe by building a tool that predicts location growth using factors such as economic factors, livability, connectivity and demographics. I would like to specify. MBAn students have created a tool that predicts the long-term growth potential of more than 600 cities and identifies the key factors used to make those predictions.
2. How can data help empower frontline workers?
Employees who work directly with customers or in the field often have to make educated guesses and make snap decisions. Businesses are turning to data analytics to create supporting tools that improve efficiency, accuracy, and sales.
Coca-Cola Southwest Beverages is trying to improve the way frontline employees evaluate store inventory and create orders. This process is currently time-consuming and error-prone. Sales forecasting algorithms that use demographics, spending trends, historical sales data, and out-of-stock information improve forecasts, increase sales, and simplify operations.
handle global, a healthcare supply chain technology company, helps hospitals estimate medical equipment budget allocations and capital expenditures, taking into account asset liquidity, type and model variations, and mergers and acquisitions between manufacturers and hospital systems. I am thinking of doing so. The company is looking to develop decision support tools that use historical data to make better purchasing decisions.
3. What is the best way to get the most out of large or unwieldy datasets?
Although data analysis can produce powerful results, some data remains difficult to process, such as unstructured data (data that does not adhere to a specific format) and large datasets. Businesses are looking for ways to efficiently process and derive insights from this type of data, but processing can be time-consuming and inefficient.
Thanks to new U.S. government regulations, health insurance pricing data is now available to competitors. However, accessing this information is not easy due to the sheer volume of data, compliance with disclosure requirements by insurance companies, and the classification of data into several different categories. Wellmark Blue Cross and Blue Shield is looking to create a coverage transparency tool that recommends pricing and negotiation room to maintain a competitive advantage and ensure optimal profits.
information service company walters kluwer's Compliance business unit helps companies meet regulatory requirements while managing risk and improving efficiency. However, validating government documents such as vehicle registrations is a time-consuming process that is error-prone and has high document rejection rates. The company is looking to use natural language processing and computer vision to create a document classification system that can more accurately and easily process paperwork that is typically processed by hand.
Cognitive AI was founded in 2019 to use technology to solve the unstructured data problems that make it difficult to digitize the insurance underwriting industry. The company is looking to build general-purpose machine learning tools to handle documents that have not yet been automated, such as loss executions (past loss claims history), which have complex and diverse formats and structures.
See all capstone projects