Analysis Report One - What’s Your Data Strategy

Executive Summary

This report looks at how organizations can use data in two different ways. Data offense focuses on using information to increase revenue, understand demand, improve decision-making, and find new opportunities. Data defense focuses on protecting information, improving data quality, following regulations, and reducing operational risks. Organizations need both approaches, but the right balance depends on their industry and goals.

Healthcare organizations especially need a strong balance between offense and defense. They can use analytics to improve inventory planning and supply chain decisions, but they must also make sure their data is accurate, secure, and properly managed. In this report, Northwind is treated as a healthcare supply company that provides products to hospitals, clinics, and other healthcare organizations.

The offensive visualization compares the number of Northwind customers in selected countries. This information could help managers identify markets where the company already has a strong customer base or where additional marketing may be useful. The defensive visualization examines active products with fewer than 15 units in stock. This could help the company recognize inventory risks before products completely run out.

Introduction

Data is an important part of how organizations make decisions, but collecting large amounts of information does not automatically create value. Organizations need a clear strategy for how their data will be organized, protected, and used. In What’s Your Data Strategy?, DalleMule and Davenport divide data strategy into two main areas called data offense and data defense (DalleMule and Davenport 2017).

Data offense focuses on using information to improve performance and create value. This can include increasing revenue, understanding customers, forecasting demand, improving marketing, and identifying new opportunities. Offensive data activities often use reports, visualizations, and analytics to help managers make better decisions.

Data defense focuses on reducing risk and protecting the organization. It includes data security, privacy, regulatory compliance, data quality, and making sure information is accurate and consistent. Defensive data practices help prevent errors, fraud, conflicting reports, and poor decisions caused by unreliable information.

Every organization needs both offense and defense, but the balance will not be the same for every company. A highly regulated organization, such as a hospital or health insurance company, may need to place more attention on data defense. A retail company may place more attention on offensive analytics because it needs to respond quickly to customers and competitors. However, even organizations focused on growth still need accurate and secure data.

Another important idea from the article is the difference between a single source of truth and multiple versions of the truth. A single source of truth means that important information comes from one reliable and agreed-upon source. Multiple versions of the truth allow departments to analyze and present the same trusted data in different ways depending on their goals. This allows flexibility without creating conflicting information.

For this report, Northwind is viewed as a healthcare supply company that provides products to hospitals, clinics, and other healthcare organizations. The offensive analysis uses customer location data to identify possible markets for growth. The defensive analysis uses inventory data to identify active products with low stock levels. These examples demonstrate how a healthcare supplier can use data to support growth while also reducing operational risk.

Research and Extensions

Recent research shows that data governance and data quality have become even more important since What’s Your Data Strategy? was published. Organizations now collect information from more systems, devices, departments, and outside sources. Bernardo and colleagues explain that data governance and quality management help organizations create reliable information and support innovation across different fields (Bernardo et al. 2024). This supports the idea that organizations must first manage their data properly before they can confidently use it for analysis.

This issue is especially important in healthcare. Healthcare organizations may work with patient information, financial records, inventory data, supplier information, and treatment data. Tse and colleagues identify major challenges involving healthcare data governance, including privacy, security, data quality, and the management of large amounts of information (Tse et al. 2018). These challenges connect to data defense because healthcare organizations must protect sensitive information and make sure that employees are using accurate records.

Healthcare organizations are also using analytics to strengthen their supply chains. Tyagi’s research explains how analytics can support healthcare supply chain management and improve decision-making (Tyagi 2024). For example, organizations can examine past orders, product demand, supplier performance, and inventory levels when planning future purchases. These uses represent data offense because the organization is using information to improve performance and make better decisions.

However, offensive analytics are only useful when the underlying information is reliable. A forecast created from incomplete or inaccurate inventory records may lead managers to order too much or too little. This shows why offense and defense must work together. Defensive practices create trustworthy data, while offensive practices use that trusted data to improve organizational performance.

Data Visualizations

Visualization One - Offensive

SELECT CustomerID, ContactName, Country
FROM Customers
WHERE Country IN ("USA", "Canada", "Mexico")

Important

During the presentation, change the WHERE statement to include Brazil. Then rerun the SQL query and the graph.

ggplot(data = myquery1,
       aes(x = Country)) +
  geom_bar() +
  theme_minimal() +
  labs(
    title = "Healthcare Customers by Country",
    subtitle = "Customers in selected North American markets",
    x = "Country",
    y = "Number of Customers",
    caption = "Source: Northwind SQLite Database"
  )

This visualization represents an offensive data strategy because it uses customer information to identify possible opportunities for growth. The query selects the customer ID, contact name, and country from the Customers table. The WHERE statement uses IN to limit the results to customers located in the United States, Canada, or Mexico.

For this example, Northwind is viewed as a healthcare supply company, and its customers could represent hospitals, clinics, or other healthcare organizations. The graph shows that the United States has the largest number of customers in this group, followed by Mexico and Canada. Managers could use this information to compare the company’s presence in different markets and decide where continued customer support or additional marketing may be useful.

Visualization Two - Defensive

SELECT ProductName, UnitsInStock, Discontinued
FROM Products
WHERE UnitsInStock < 15
AND Discontinued = 0

ggplot(data = myquery2,
       aes(x = UnitsInStock)) +
  geom_bar() +
  theme_minimal() +
  labs(
    title = "Active Healthcare Products with Low Inventory",
    subtitle = "Products with fewer than 15 units in stock",
    x = "Units in Stock",
    y = "Number of Products",
    caption = "Source: Northwind SQLite Database"
  )

This visualization represents a defensive data strategy because it helps the company identify an operational risk. The query selects the product name, units in stock, and discontinued status from the Products table. The WHERE statement limits the results to products with fewer than 15 units in stock. The AND condition removes discontinued products because those items may not need to be reordered.

For a healthcare supplier, running out of an active product could delay orders to hospitals and clinics. The graph shows how many low-inventory products are available at each stock level. Managers could use this information to recognize products that may need attention before their inventory reaches zero. This could reduce shortages, delayed orders, lost sales, and service problems for healthcare customers.

Recommendations for Industry

Healthcare organizations should create a reliable single source of truth for important information. Customer, supplier, product, inventory, and financial data should be defined consistently across the organization. This can prevent employees in different departments from making decisions based on conflicting or outdated information.

Executives should connect data analysis to clear organizational goals. Offensive data analysis can help identify customer markets, understand demand, improve marketing, and support growth. Defensive data analysis can help protect sensitive information, improve data quality, and identify operational risks.

Healthcare suppliers should regularly review active products with low inventory. Managers should not wait until a product reaches zero units before taking action. A regular inventory report could help purchasing employees recognize low-stock products and contact suppliers earlier.

The company should also review customer location data when making marketing decisions. Areas with many customers may deserve continued support, while areas with fewer customers could provide opportunities for future growth. However, managers should consider other information, such as market size and customer demand, before making a final decision.

Finally, organizations should regularly reconsider the balance between data offense and data defense. Changes in technology, regulations, customer expectations, and supply chain conditions may require the organization to adjust its priorities over time.

References

Bernardo, Bruno Miguel Vital, Henrique São Mamede, Joao Manuel Pereira Barroso, and Vı́tor Manuel Pereira Duarte Dos Santos. 2024. “Data Governance & Quality Management—Innovation and Breakthroughs Across Different Fields.” Journal of Innovation & Knowledge 9 (4): 100598.

DalleMule, Leandro, and Thomas H Davenport. 2017. “What’s Your Data Strategy.” Harvard Business Review 95 (3): 112–21.

Tse, Daniel, Chung-kin Chow, Ting-pong Ly, Chung-yan Tong, and Kwok-wah Tam. 2018. “The Challenges of Big Data Governance in Healthcare.” In 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications / 12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), 1632–36. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00240.

Tyagi, Sapna. 2024. “Analytics in Healthcare Supply Chain Management in the New Normal Era: A Review and Future Research Agenda.” Benchmarking: An International Journal.