Johnson & Johnson | Case Study

Tamr for Master Data Management

Johnson & Johnson Masters Global Consumer Product Data Using Tamr Data Mastering

Creating Commercial Hierarchy for Global Consumer Products on AWS

Tamr Key Results:

  • Created a global commercial hierarchy for consumer products across 175 markets and countries
  • Accelerated time to insights from consumer data to improve product sales strategies
  • Fresh product data led to better supply chain visibility and sales & operations planning
  • Data resources are more efficiently used with machine learning; automating much of the data hygiene work that was previously done manually

Johnson & Johnson makes products that are bought and used by billions of people around the world every year. Some of the best-known and beloved consumer brands like, Tylenol, Band-Aid, Johnson's Baby, Listerine, Neutrogena, and Aveeno make up J&J’s portfolio of 250+ brands and subbrands sold in 175 markets.

Like any consumer health product company, the ability to answer critical business questions like “which product is selling best in which geographic region?” and “what is our best sales channel for a particular sub-brand?” is crucial to J&J’s success. However, answering these types of questions becomes more complicated with a company like J&J, given the scale, distribution, and complexity of their businesses.

To solve this problem, J&J needed to classify its consumer products using a global taxonomy in order to have a unified view of their sales across regions, product brands, and product types.

Challenge: Mastering Diverse Distributor and Third-Party Data From Around the World To Answer Key Business Questions

To try and solve their consumer products data problem, J&J first invested in a traditional, rules-based Master Data Management (MDM) solution. Once in place, J&J hoped to create Golden Records, or fully mastered data, for its key data entities like customers, products, and suppliers.

However, getting their complex and divergent product data into a ready-to-analyze state with a rules-based approach proved difficult for J&J’s analytics team. The data feeding their MDM solution varied too much in structure to be easily tied together and analyzed.

This is primarily because company sales are driven through various global distribution partners and retailers, with each partner sending the organization data in a unique format with regional product naming conventions and other variations.

Instead of focusing on high-value analytic projects, the team spent most of their time on data hygiene and manual fixes. In short order, the team was overwhelmed by the number of rules it needed to write and maintain in order to make the disparate data harmonious.

J&J’s inability to master global customer data impacted the company’s ability to:

    • Provide leadership with a global view of individual and regional product performance.
    • Analyze past performance to improve future sales and marketing efforts.
    • Act quickly on data-informed insights to improve supply chain performance and manage resources more efficiently.

Needing an efficient and scalable way to unify and master their disparate data sets, J&J turned to Tamr’s human-guided machine learning.

“With Tamr we can effectively master data from internal and external sources, including key data about distributors and retailers. This drives new insights about product sales performance and impacts our business.”Peter Tsang, Worldwide Head Data & Analytics Product Line - Consumer & CMD

Tamr’s Solution: Using Machine Learning and the Cloud To Master Product Data at a Global Scale


Unlike traditional MDM solutions, Tamr uses human-guided machine learning to create models capable of mastering product data at the scale required by J&J. By applying predictive machine learning models to problems of data matching and mastering, Forrester Research Total Economic Impact study found that Tamr is able to reduce manual efforts by data engineers and analysts by 70% and 80%, respectively.

By moving to a modern data mastering solution from Tamr, Johnson & Johnson was able to effectively create a global taxonomy of products and their appropriate hierarchies. Brand experts were then assigned through a single Tamr user interface to train the machine learning models and improve the performance.

As Peter Tsang, J&J’s Worldwide Head Data & Analytics Product Line - Consumer & CMD said.

“The subject matter experts can easily map products per branch through the user interface where they can see all the product information to make the right decision. And they can do so without the need to write any code or any expertise in data engineering. With one mapping per branch, the machine would do the rest of the jobs technically. Over time, as they keep training the model, it will become smarter and can make better decisions.

If there is some existing hierarchy data curated over the years, it can be used as training data as well. The team can utilize the API to perform mass training to speed up the process even further.”

Driving Better Business Decisions With Tamr and AWS

Currently, J&J runs Tamr on AWS, allowing the company to take advantage of the platform’s flexibility and scalability and provide RedShift with high-quality data. The combined Tamr and AWS solution provides a singular access point for all of the different data sets in the company and enables all of their analytics, visualization, and tools to point to the same place for those data assets. The solution provides Johnson & Johnson with accurate data that is readily available to use in business decisions.
“Cloud computing lets us increase and decrease our storage and compute usage based on demand, allowing us to more efficiently master data,” Peter Tsang, Worldwide Head Data & Analytics Product Line - Consumer & CMD

The Bottom Line: Advanced Analytics Are Continually Fed With Enriched and Competitive Entity Data Improving Business Efficiency

Using Tamr’s modern data mastering capabilities, the J&J team delivered:

  • Improved analytics for sales and marketing to drive growth by applying best practices to pricing and promotions
  • Better supply chain visibility and sales and operations planning to reduce supply chain risks (excesses and shortages) through richer insights into what’s being sold where, and to whom.
  • Improved the analytics’ team efficiency through machine learning by automating work that would otherwise be done manually, reducing costs, and accelerating the time-to-value of the businesses’ data.

With the help of Tamr, Johnson & Johnson was able to boost sales operations and improve marketing efficacy with a global view of product sales. Today, Johnson & Johnson is partnering with Tamr to create a more seamless and end-to-end automated ecosystem to minimize human efforts as much as possible -- to co-innovate the best practices around data quality control and expand the machine learning footprint throughout the entire organization.


About Johnson & Johnson

Location: Global

Industry: Life Sciences and Healthcare

Johnson & Johnson (J&J) is a multinational medical devices, pharmaceutical, and consumer health organization. The company has over 130 years of innovation history, and more than 130,000 employees around the world.


About AWS

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