About Data Shop

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 Data cleansing is like tidying up a messy room. Imagine you have a room full of scattered toys, clothes, and books. Some items are broken, some are duplicates, and some don't belong there. Cleansing data means sorting through all the information, throwing away what's incorrect or unnecessary, fixing what's broken, and organizing everything neatly. This way, just like a clean room, the data becomes useful and easy to understand.

Data de-duplication is like sorting through a deck of cards to remove any duplicates, leaving you with just one of each card. Imagine you have multiple copies of the same card scattered throughout the deck. De-duplication means finding and removing these extra copies, so each card is unique. This makes the deck easier to manage and play with, just like cleaning up data makes it simpler and more efficient to use for decisions or analysis. 

 Data modelling is like designing a blueprint for a house. Before you build, you decide where the rooms go, how they connect, and what purpose each serves. In data modelling, you're planning how to organize information in a system, defining how different pieces of data relate to each other, and setting rules for how they interact. Just like a good house design makes living easier, a good data model makes using and understanding data simpler. 

Data Encoding is like translating a menu from pictures to numbers so a computer can read it. For example, "pizza" is 1, "burger" is 2, this translation helps computers process faster. Data Partitioning is like dividing a big cake into smaller pieces. You keep one part to taste-test (train your model) and set aside another part for guests arriving later (test your model). This way, you ensure everyone enjoys the cake, similar to making sure a computer model works well on new, unseen data. 

 Data Scaling and Normalization is like adjusting weights and measures so they all use the same scale. Imagine if some recipes called for ingredients in pounds and others in kilograms; it'd be hard to compare or combine them directly. Scaling changes all the weights to the same unit. Normalization is like making sure all the ingredient amounts are proportionate for a single serving, so no single ingredient overwhelms the dish, ensuring a balanced and consistent taste across recipes. 

Customer master data is like having a detailed contact book for all your friends and family. Instead of scattered notes and addresses, you have one place where every person's information is up-to-date, organized, and complete. This includes names, addresses, phone numbers, and preferences. It means keeping one central, accurate record of each customer's details, ensuring communication and service are tailored and efficient, just like your neatly organized contact book helps you s

Product master data is like a catalog that lists every item in a store with detailed descriptions, prices, and stock levels. It's one central source that keeps all product information consistent and up-to-date across all systems. This ensures that whether you're looking online or in a physical store, the information about each product is the same, making it easier for everyone to understand what's available, how much it costs, and where to find it. 

Supplier master data is like having a contact list for all your go-to service providers and shops. It keeps track of essential details like names, services offered, contact information, and terms of engagement for each supplier. This centralized list ensures a business can easily communicate with, order from, and manage relationships with its suppliers efficiently, similar to how your contact list helps you quickly reach out to the right person when you need something.