Analysis Report One - What’s Your Data Strategy

Executive Summary

This report looks at how our organization uses data. It follows ideas from DalleMule and Davenport (2017). These authors say it is important to protect data through rules and safety (called “defense”) and also to use data for new business and finding ways to grow (called “offense”). In healthcare or related fields, having one main place for all data (Single Source of Truth or SSOT) helps us follow laws and keep operations running safely. At the same time, letting teams use different data sets (Multiple Versions of the Truth or MVOTs) can make daily work and growth analysis easier.

For this report, I looked at the Northwind database to address two specific questions:

Offensive: Which countries in our target regions are generating the highest volume of orders to guide our expansion?

Defensive: Which employees are responsible for late shipments that violate our service standards?

The report ends with easy steps we can take to make our data rules even better based on these findings.

Introduction

In “What’s Your Data Strategy?”, DalleMule and Davenport (2017) say that every organization needs a plan for how they use and protect data. They break this plan into two simple parts: defense and offense.

Data defense is about keeping data safe, following rules (like HIPAA in healthcare), and tracking when standards are missed. To do this, companies use something called a Single Source of Truth (SSOT). This means there is one main place where the most important information is stored, so everyone uses the same, correct data. In this report, our defensive question asks: “Which employees consistently have order shipment dates that miss their required delivery deadlines?” By querying the core Orders table for late shipments (ShippedDate > RequiredDate), we can spot operational failures and protect the company from compliance or customer service risks.

On the other hand, data offense is about using data to help the company grow, make more money, and track high-performing markets. Offense needs flexibility, so teams sometimes use their own versions of the data (called Multiple Versions of the Truth, or MVOTs) to isolate specific regions or timeframes. In this report, our offensive question asks: “Which specific countries are driving our highest order volumes over recent years?” By analyzing the regional distribution of orders, sales teams can easily spot where the business is growing fast and decide where to expand next.

Good companies do not choose only defense or only offense. They use both, changing their focus depending on what their business needs or what rules they must follow.

Data Visualizations

Include the code and graphs for your two visualizations here (One offensive, defensive). You should describe your data and the visualization, together with an explanation of why the example is relevant for organizations today.

Your queries may be loosly based on Practice queries, but they must extend or adapt the practice in interesting ways (again sticking to the practice concepts) if you want to score highly. Your goal is to show mastery of the week’s material.

Visualization One - Offensive

--I've completed this for you as a placeholder. Replace this with your query.
--SQL code in this block

SELECT ShipCountry, OrderID, CustomerID, OrderDate, ShipRegion
FROM Orders
WHERE OrderDate >= '2025-01-01'
AND ShipRegion LIKE '%Europe%'

#ggplot visualization in this block. I prettied this up with labels to show what is possible.
ggplot(data = myquery1,
       aes(x = ShipCountry)) +
  geom_bar() +
  theme_minimal() +  # Cleans up the background grid lines
  labs(
    title = "Visualization showing number of orders by ship country in North America",
    subtitle = "Data taken from Northwind",
    x = "Country",
    y = "No. of Orders in Country",
    caption = "Source: Northwind Sqlite"
  )

Visualization Two - Defensive

--Put your SQL code in this block
SELECT * FROM Orders
WHERE ShippedDate > RequiredDate
AND RequiredDate >= '2025-01-01'

#Put your ggplot visualization in this block
ggplot(data = myquery2,
       aes(x = EmployeeID)) +
  geom_bar() +
  theme_minimal() +  # Cleans up the background grid lines
  labs(
    title = "Visualization showing number of late shipped orders by employee",
    subtitle = "Data taken from Northwind",
    x = "EmployeeID",
    y = "No. of Late Sent Orders",
    caption = "Source: Northwind Sqlite"
  )

Recommendations for Industry

Based on what I found in the research and data, here are some simple steps healthcare and logistics leaders can take:

Set up two teams to handle data: Have one main leader, like a Chief Data Officer, in charge of a secure central database to keep things safe and organized. At the same time, let local data experts use smaller, separate tools to quickly analyze and use data for daily needs.

Find and fix shipping slowdowns: Use a simple dashboard that shows when employees are late with shipments. This helps managers step in early to offer help or extra resources before there are bigger problems with customers.

Grow in areas with high demand: Spend more on marketing and supply efforts in the countries where the data shows the most customers. This way, you can make the most of where the business is already popular.

References

(article?) {dallemule2017s, title={What’s your data strategy}, author={DalleMule, Leandro and Davenport, Thomas H}, journal={Harvard business review}, volume={95}, number={3}, pages={112–121}, year={2017} }