Analysis Report One - What’s Your Data Strategy

Executive Summary

As industries continue to evolve with the advancement of modern technologies, now more than ever a company’s ability to manage its data is critical to its success. Finding the right balance between offensive and defensive data strategies will be unique for every organization and depends on its goals, industry, and regulations. By first ensuring that data is secure and accurate while also continuing to govern it properly, organizations can use that same data to support better decision-making, putting the data to use in real-time to achieve their organizational goals.

Recent healthcare research, along with a review of Northwind data, shows the value of balancing offensive and defensive data strategies. Northwind can use customer data to better understand its customers and make decisions on marketing and sales, while inventory data can be used to help track products with low stock.

By balancing both offensive and defensive data strategies, Northwind should review customer and inventory data regularly to make better business decisions, improve operations, and to prevent product shortages.

Introduction

A data strategy refers to how an organization uses, protects, and manages its data to support its overall business goals. The reading What’s Your Data Strategy? explains that there isn’t one traditional approach to managing data that works for every organization. Rather, organizations should find the right balance between offensive and defensive data strategies based on their industry, regulations, business goals, and competitive environment. A defensive strategy focuses on protecting an organization’s data by ensuring it is accurate, compliant with regulations, secure, and governed properly. An offensive data strategy, on the other hand, focuses on using that data to help with decision making and improve overall performance (DalleMule and Davenport 2017).

Research and Extensions

A systematic literature review on data governance in healthcare emphasizes the importance of accurate data in healthcare so providers can make better decisions (Ngesimani, Ruhode, and Harpur 2022). The review explains that electronic health records are the foundation of digital healthcare, but they only work if the information is accurate and reliable. This shows why organizations first need a defensive data strategy and a Single Source of Truth (SSOT) to ensure their data is reliable before using offensive data strategies (DalleMule and Davenport 2017).

As healthcare organizations continue to collect more data, the integration of business intelligence (BI) tools is becoming increasingly common (Shittu et al. 2024). According to the article, BI tools help providers view data in real time, make better decisions, enhance patient care, and operate more efficiently. Although healthcare organizations typically place their focus on defensive data strategies because ensuring data accuracy and protecting patient information are top priorities, this article shows the growing need to balance those strategies with offensive ones. Because of the benefits BI tools provide, the authors even suggest that healthcare education and professional development be reevaluated to include data literacy and proper utilization of BI tools. This supports the idea that as healthcare continues to evolve, focusing on defensive data strategies isn’t enough. Organizations also need to know how to use that data through offensive data strategies to get the most value from it (DalleMule and Davenport 2017).

Data Visualizations

Visualization One - Offensive

SELECT CustomerID, ContactName, Country
FROM Customers

ggplot(data = myquery1,
       aes(y = Country)) +
  geom_bar(fill= "pink",color= "black" ) +
  theme_minimal() +  
  labs(
    title = "Number of Northwind Customers by Country",
    x = "Number of Customers",
    y = "Country",
    caption = "Source: Northwind Sqlite"
  )

Using the customer data from the Northwind database this visualization compares the number of customers in each country. The bar chart makes it easy to quickly identify which countries have the largest and smallest customer bases. This information could help Northwind make decisions on where to focus its marketing, whether that’s targeting countries with a low number of customers or continuing to invest in markets with an already strong customer base.

Visualization Two - Defensive

SELECT ProductName, UnitsInStock
FROM Products
WHERE UnitsInStock < 10

ggplot(data = myquery2,
       aes(x= UnitsInStock, y=ProductName)) +
  geom_col(fill="pink", color="black") +
  theme_minimal() +  
  labs(
    title = "Products with Fewer Than 10 Units in Stock",
    x = "Units in Stock",
    y = "Product",
    caption = "Source: Northwind Sqlite"
  )

This visualization identifies products with less than 10 units in stock. It gives Northwind a more efficient way to monitor inventory by allowing management to quickly see which products are running low. Using the data in this way can help reduce the risk of lost sales and improving inventory management.

Recommendations for Industry

To improve its operational data strategy, Northwind should regularly review its customer data to more easily see changes and trends in its customer base by country. Reviewing this information on a regular basis could help management make more informed decisions on where to focus marketing efforts. To improve its defensive data strategy, Northwind could benefit from a dashboard that highlights active products with low inventory by excluding discontinued items, making it easier to identify active products that need to be restocked before they run out. This could help reduce the risk of lost sales while also making inventory management more efficient. Overall, by utilizing both offensive and defensive data strategies, Northwind can improve its overall performance by making more informed business decisions, improve operational efficiency, and keeping products stocked for customers.

References

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

Ngesimani, Nomputumo L, Ephias Ruhode, and Patricia-Ann Harpur. 2022. “Data Governance in Healthcare Information Systems: A Systematic Literature Review.” South African Journal of Information Management 24 (1): 1475.

Shittu, Rahman Akorede, Anita Jumai Ehidiamen, Opeyemi Olaoluawa Ojo, SJC Zouo, J Olamijuwon, BM Omowole, and AQ Olufemi-Phillips. 2024. “The Role of Business Intelligence Tools in Improving Healthcare Patient Outcomes and Operations.” World Journal of Advanced Research and Reviews 24 (2): 1039–60.