Simplifying complex data concepts for professionals

Data can be overwhelming, but it doesn’t have to be. Today, we’ll break down some jargon-heavy terms into simple, relatable concepts. After this lesson, you’ll say, “Ah, now I get it!”

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Alright, I hope that video got you pumped up 💪 and ready to dive deeper! You’ve just scratched the surface with those three terms. Now, let’s get into the nitty-gritty.

Below is a comprehensive list of data terms you’ll encounter in the professional world, each paired with a real-world analogy to make them easier to grasp. Get ready to expand your data vocabulary!

Deep Dive: Understanding Data Governance, Data Masking, and Data Validation

Data Governance

Real-world Analogy:
Imagine a school where there are rules and policies in place to ensure the safety of students and the quality of education they receive. These rules govern everything from attendance to behaviour and even how teachers should conduct their classes.

  In Business: 

Similarly, in the corporate world, Data Governance acts as a framework for how data is handled, stored, and used within an organisation.

Take Airbnb, for example. They use data governance to ensure that data is used correctly across various departments for better decision-making. This involves setting up rules for data quality, data management, and data protection. By doing so, Airbnb can make more informed decisions, improve customer experiences, and even comply with legal regulations.

🧠 Click here to see some more data governance case studies

Data Masking

Real-world Analogy:
Think of a movie studio that changes the names and details in a script to protect the privacy of real individuals who may be depicted in the story. This way, the essence of the story remains, but the identities are protected.

In Business:

In a similar vein, data masking is used in technology and business to protect sensitive information.

Companies like GE Aviation use this technique during testing phases to prevent any unauthorised access to critical data. The data is scrambled or replaced with fictional but structurally similar data. This allows for effective testing without risking exposure to sensitive information.

🎭 Here are 5 more real-world data masking examples

Data Validation

Real-world Analogy:
Picture a cashier at a store meticulously checking the money handed over by customers to ensure it's not counterfeit. The cashier may use various methods like watermark checking, texture feeling, or even using a special machine for this purpose.


In Business:

Data Validation in the business context serves a similar purpose. Before any data is processed or analysed, systems check to ensure that the data entered is in the correct format and meets predefined criteria. This is crucial for maintaining the accuracy and reliability of data.

For example, an e-commerce website might validate that the email entered during the signup process contains an "@" symbol and a domain name, ensuring it's in a format that could receive a confirmation email.

✔ Read more on data validation

Quick Insights: Exploring other key Data terms

📊 Data Lake:
Imagine having a big storage room to keep all kinds of items until needed. Adobe Systems created a “storage room” or data lake to hold vast amounts of data for analytics purposes, making data access and analysis easier​.

📚 Data Virtualisation:
Think of a universal library catalogue that allows you to find books across many libraries without visiting them. In the corporate realm, companies use data virtualisation to access data from various sources through a single interface, saving time and resources.

💡 Data Wrangling:
Imagine a messy closet; data wrangling is like organising it so you can easily find what you need. Businesses often have to “tidy up” their data to make it usable for analysis, ensuring accurate insights​.

🗺️ Database Schema:
Think of a city map showing all the roads and buildings. A database schema is like this map for databases, showing how different data is related. Companies use it to organise their data efficiently, ensuring smooth operations​.

⭕️ ETL (Extract, Transform, Load):
Picture a moving company taking your belongings from your old to your new house, packing, and arranging them. In the business world, ETL processes help companies like Wells Fargo move and transform data to a structured format for easy analysis.

🌎 NoSQL:
Imagine a large field where you can place items anywhere, unlike a structured warehouse. NoSQL databases offer this flexibility, allowing companies like Uber to handle diverse data types efficiently. 

🌎 Master Data Management (MDM) :
Think of a central directory in a large family that has everyone’s contact info. In business, MDM helps companies like GE Aviation maintain consistent data across the organisation, ensuring everyone has access to accurate information​.

Still not enough? Check out the ultimate Data Glossary with 101 data terms.

Let's practice!

Now that you’ve got a grasp on these terms, let’s test your skills with a matching game! Your task is simple - match the data terms with their definition. Go ahead and give it a try!

🎁 Bonus: Use this ChatGPT prompt to learn more about new data terms in the context of your one use case: 

"Hello, I'm [Your Profession, e.g., 'a retail manager'] and I've heard a lot about [Complex Data Term, e.g., 'Data Wrangling']. Can you explain it in terms of its relevance and potential application to [Specific Business Scenario or Challenge, e.g., 'inventory management in retail stores']?"

Wrapping up

Navigating the world of data can often feel like deciphering a foreign language. However, by contextualising and breaking down these key terms, we can actually truly understand the foundational concepts that underpin many of today's business operations and decisions. Remember, in our digital age, data fluency is not just for tech professionals—it's a valuable asset in any role.

As you continue on your journey, keep these definitions handy and watch as the world of data becomes more accessible and actionable.

Good luck!