SELECT CustomerID, ContactName, Country
FROM CustomersAnalysis Report One - What’s Your Data Strategy
Executive Summary
Today’s level of technology, some would say has increased, some say in a good way and others say in a bad way. Organizations face risks everyday related to AI and cyber security. In the readings, they talk about two major issues involving influences of recommendation algorithms on social media including TikToK and the threat of data poisoning against generative AI systems. The research that I have created shows a balance between defensive and offensive strategies. The offensive side is more about improving customer data, marketing and sales performance. The defensive side is more on protecting customer data through cyber security. Organizations should put their best foot forward when it comes to AI. This includes more training for the employees and are constantly looking at these AI systems.
Introduction
Monitoring data is one of the most important things that a business can do in order to stay successful. Organizations use data to improve different departments within it and it helps them better understand the customer’s wants and needs. Even though AI is becoming very popular and everyone loves using it because it is so helpful, it does come with some chances of threats. These risks can be related to cyber security and people’s personal information at risk. Again, the readings that we had to read were heavily focused on these two different types of strategies. Offensive includes using these types of data to increase customer performance and experience. Defensive includes protecting data through cybersecurity.As I said above, the readings also tell us about two current issues today involving influences of recommendation algorithms on social media including TikToK and the threat of data poisoning against generative AI systems. These two examples show us how important it is that a business knows how to keep track and manage and use data.
Research and Extensions
After these readings were published which were all over a year ago or longer, organizations have definitely increased the importance of AI and cyber security. After doing some research, I found an interesting article titled “Artificial intelligence and the impact of the EU AI Act in business organizations.” A huge event that happened was the passing of EC AI Act. “This legislative framework aims to ensure the responsible deployment of AI by classifying AI systems into risk categories and imposing compliance requirements. It also underscores both the opportunities and challenges that AI presents to businesses, particularly in enhancing consumer experiences through automation and data-driven decision-making processes.” Organizations are also investing in stronger data practices and strategies and closely monitoring the use of AI systems. These improvements help show more what the ideas in the readings actually were and shows that as AI keeps increasing, which it will, businesses need to be up to data on their data using and monitoring.
Visualization One - Offensive
ggplot(data = myquery1,
aes(y = Country)) +
geom_bar() +
theme_minimal() +
labs(
title = "Customers by Country",
x = "Numbers of Customers",
y = "Country"
)
My first offensive visualization includes data from the Customers table within the Northwind database. My SQL query selected the Customer ID, ContactName, and Country from the Customers table. The results were saved as myquery1. Finally, I used the ggplot code to create a bar chart. The visualization shows the number of customers located in the different countries. The y-axis shows the different countries where the x-axis shows the number of customers. The geom_bar() counts how many people are in each country and ends up forming a bar chart. This is a good example and is a relevant offensive visualization because a very important part of a business is to always keep track on customer data to see what is staying important in the market so your business never falls behind. Since this bar chart shows where the customers are located, it can help organizations make better decisions about sales and marketing.
Visualization Two - Defensive
SELECT CompanyName, Country, Region
FROM Suppliers
WHERE Region LIKE "%Europe%"ggplot(data = myquery2,
aes(x = Country)) +
geom_bar() +
theme_minimal() +
labs(
title = "Suppliers by European Country",
x = "Country",
y = "Number of Suppliers"
)
My second defensive visualization includes data from the Suppliers table. My SQL query selected the CompanyName, Country, Region from the Suppliers table. I used a WHERE statement to show the suppliers within the European countries.The results were saved as myquery2. Finally, I used the ggplot code to create a bar chart. The visualization shows the number of suppliers within each European country. The y-axis shows the number of suppliers where the x-axis shows the different countries.The geom_bar() counts how many suppliers are in each country and ends up forming a bar chart. This is a good example and is a relevant defensive visualization because it is so important to understand where each supplier is located to help put a stop to any risks that a business might face. Especially today, many organizations have cybersecurity issues that can cause concern for the suppliers. So knowing where the suppliers are located can help decrease the chance of any kind of risks.
Recommendations for Industry
What I learned from forming an offensive strategy and a defensive strategy is for an organization to be successful there needs to be a balance between them. This will help lower the chances of any kinds of risks while keeping a company’s value in check. My first recommendation would be for organizations to start monitoring the data to keep track and better understand their customers because it will help them make better marketing and sales decisions in the end. My second recommendation would be for organizations to increase their defensive strategies, especially today, because this world is being taken over by AI. The research and data that was shown when talking about generative AI shows how bad management can negatively affect online users. This is why so many people believe the web is a very bad place that should not be used anymore. My last recommendation would be to provide more training for the employees on AI so they are more prepared on how to handle it because of how fast it is increasing in this world.
References
(DalleMule and Davenport 2017) (Bernardo et al. 2024) (Wingate, Burns, and Barney 2025) (Snow 2024) (Cors and Thiébaut 2025)
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.
Cors, Marc Selgas, and Renata Thiébaut. 2025. “Artificial Intelligence and the Impact of the EU AI Act in Business Organizations.” AI Magazine 46 (4): e70039.
DalleMule, Leandro, and Thomas H Davenport. 2017. “What’s Your Data Strategy.” Harvard Business Review 95 (3): 112–21.
Snow, J. 2024. “As Generative AI Takes Off, Researchers Warn of Data Poisoning.” WSJ.
Wingate, David, Barclay L Burns, and Jay B Barney. 2025. “Why AI Will Not Provide Sustainable Competitive Advantage.” MIT Sloan Management Review 66 (4): 9–11.