--Put your SQL code in this block
SELECT OrderID, ShipCountry
FROM Orders
WHERE OrderID BETWEEN 20000 AND 30000
LIMIT 20Analysis Report One - What’s Your Data Strategy
Executive Summary
Having a concise and thoughtful data strategy is crucial to the companies fundamental success, however with an abundance of information at the click of a button it is easy to get swept away. With the endless possibilities data hold, companies must carefully construct their data architecture to best support their long term goals. The data strategy that would be most effective is an offensive strategy focused on customer segmentation and satisfaction. Companies like Northwind, retailers and distributors, have far less government regulations than many other industries like banking, insurance, and healthcare, allowing them to step into the offensive position with far less risk. This customer focused approach allows the brand to leverage their customers demands and potentially establish a differentiation competitive advantage over some of it’s competitors.
Introduction
The first important step in deciding your data strategy is determining whether to take a defensive or offensive approach. While it is important to have both offensive and defensive analysis available it is nearly impossible for the average company to successfully prioritize both sides of the spectrum. This is why it is important to evaluate the business on the largest scale and evaulate the companies data architecture. Data architecture refers to how dat is collected, stored, transformed, distributed, and consumed (DalleMule and Davenport 2017). The best strategy for a comapny like this one is a to create a “data pool”, as it supports multiple types of data strategies.
Using the raw data providied by the “data pool” the organization must decide if they want to take a defensive position focuses on security, deadlines, and meeting regulations or an offensive approach focused on predictive analytics. Which approach to use will be determined based on industry and long term goals. For this company an offensive strategy would be best, to help build customer retention and allow the marketing helpful insight. Choosing a offensive approach means implementing a system that supports multiple versions of the truth or MVOTs. Using a MVOT system allows data to be used flexibly, moving around interchangeably with other easily accessible data sets. One downside of this approach is that it is riskier as the data is vulnerable. That is why it is important to build a system that is still capable of performing SSOT analysis as a safe guard.
Another key decision is what system processing machine to use and how you will collect data. While these two things sound vastly different, they are more interconnected now than even before thanks to AI. With the rise of artificial intelligence has come the integration of optimization. Every organization is looking at how they can use AI to increase productivity and fast-track mundane routine tasks. There are many ways AI can aid in the numerous faucets of business, however it is to be used with caution when talking about data strategy. AI learns data strategy from the various resources it can find off of the internet, learning more and more as it continues to collect digital snapshots. These digital snapshots are especially vulnerable to scammers and bad data. While AI can be used as tool to help you comb through or summarize existing data sets, relying on it for predictive and supportive analysis is a gamble not worth taking.
Research and Extensions
Developing a good data strategy in a ever changing market can be challenging. That is why it is best to create a strategy specifically aligned with the companies larger operational goals. There are 4 aspects of a data-driven enterprise and determining where to focus efforts will determine how productive you are as a company. Companies like this one have a low risk of cyber attacks, allowing them to focus their main data strategy on other faucets of data systems. For companies like this one strategic alignment and technical infrastructure are just as important as complying with governing regulation. Strategic alignment contains numerous objectives including performance metrics, resource allocation, and business goals (Al Khaldy 2025). Focusing on collecting data which measures these objectives helps the company to establish an offensive data strategy. Technical infrastructure refers to what we had previously talked about, determining how your organization will collect data and how it will use this information. These components along with strong leadership and governance compliance and security are how an retailer or manufacture can establish a numer driven company.
Data Visualizations
The first visual details which countries OrderID 22000-22020 were shipped to. This is relevant as it serves as a sample of the consumer base. This data is helpful as it segments the audience, allowing the company to advertise directly to the people buying the product. The second table shows the available Units In Stock versus the quantity reordered of that same product. This is helpful in predicting future demand and gives a point of reference for supply shipments.
Warning
One note of caution. I’m teaching you certain concepts this week. Your code must focus on the topics contained in the practice. I don’t want to see the best query AI can dream up. I want to see the best you are able to do with what I am teaching.
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
#ggplot visualization in this block. I prettied this up with labels to show what is possible.
ggplot(data = myquery4,
aes(y = ShipCountry)) +
geom_bar() +
theme_minimal() + # Cleans up the background grid lines
labs(
title = "Visualization of Orders by Country",
subtitle = "Data taken from Northwind (OrderID 22000-22020)",
x = "No. of Orders",
y = "Ship Country ",
caption = "Source: Northwind Sqlite"
)
Visualization Two - Defensive
--I've completed this for you as a placeholder. Replace this with your query.
--SQL code in this block
SELECT ProductName, UnitsInStock, ReorderLevel
FROM Products
WHERE UnitsInStock BETWEEN 0 AND 5
Important
Don’t forget to do you live edit when you present Visualization one. You must change your WHERE statement in some meaningful way, then rerun your visualization to show the change.
#ggplot visualization in this block. I prettied this up with labels to show what is possible.
ggplot(data = myquery3,
aes(x = ReorderLevel)) +
geom_bar() +
theme_minimal() + # Cleans up the background grid lines
labs(
title = "Visualization showing number of Units Reordered",
subtitle = "Data taken from Northwind",
x = "Reordered Level",
y = "Units In Stock",
caption = "Source: Northwind Sqlite"
)
Recommendations for Industry
In order to see increased sales ensure additional focus is placed on consumers in he USA and France as those were the counties with the most consumers from our sample. Additionally focus on Gorgonzola Telino and Louisiana Hot Spiced Okra as those are the most popular items with low stock. Make a query that sorts and separates discounted products so that inventory is easily trackable and supply orders are simple.
References
Getting your citations out of Google Scholar and into your references.bib file takes just a few clicks.
Go to Google Scholar and search for the paper you want to cite (for example: “What’s your data strategy”).
Look directly underneath the search result for the Cite button (it looks like a double quotation mark ”). Click it.
A pop-up window will appear showing standard citation styles (APA, MLA, etc.). At the very bottom of that pop-up, click the link that says BibTeX.
A new page or plain text block will open showing some bibtex code. Paste that block of text into your [references.bib] file.
If you have multiple sources, just keep pasting them one after another down the file. You don’t need commas between the different articles; just separate them with a blank line to keep it clean. You can then cite them like this (DalleMule and Davenport 2017) and they will automatically appear in the references section.
Al Khaldy, Mohammad. 2025. “Data Strategy Implementation in Organizations: A Framework for Building Data-Driven Enterprises.” Al-Basaer Journal of Business Research 1 (2).
DalleMule, Leandro, and Thomas H Davenport. 2017. “What’s Your Data Strategy.” Harvard Business Review 95 (3): 112–21.