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Phase 2 - Data Preparation

Phase 2: Data Preparation

Overview: Data preparation involves collecting, processing, and cleaning data to make it ready for analysis. In this phase, the focus shifts from understanding business requirements to fulfilling data needs. It's like preparing ingredients before cooking a meal – ensuring everything is ready for the recipe.

Key Processes:

  1. Preparing the Analytic Sandbox:

    • The team creates an analytic sandbox or workspace, a safe environment for exploring data without affecting live production data.
    • This sandbox collects various types of data, enabling organizations to conduct advanced predictive analytics beyond traditional analysis and business intelligence (BI).

  2. Performing ETLT (Extract, Transform, Load, Transform):

    • ETLT involves extracting data, transforming it based on business rules, and loading it into the sandbox.
    • It's a critical step in data preparation, ensuring that the data is structured and ready for analysis.

  3. Learning About the Data:

    • Data is acquired through three main methods: data acquisition from external sources, data entry within the organization, and signal reception from devices.
    • Understanding the sources and nature of the data is essential for effective analysis.

  4. Data Conditioning:

    • Data conditioning includes cleaning, normalizing, and transforming datasets to ensure their quality and consistency.
    • It's a preprocessing step before analysis, often performed by data owners, IT departments, or database administrators (DBAs).

  5. Involvement of Data Scientists:

    • It's crucial to involve data scientists in data conditioning, as they prefer having more data rather than too little.
    • Their expertise ensures that the data is prepared in a way that maximizes its potential for analysis.

  6. Common Tools for Data Preparation:

    • Hadoop: Enables parallel ingest and analysis of large datasets.
    • Alpine Miner: Provides a user-friendly interface for creating analytic workflows.
    • OpenRefine: A free, open-source tool for working with messy data.
    • Data Wrangler: An interactive tool for data cleansing and transformation, similar to OpenRefine.

In Simple Terms: Imagine you're getting ready to bake a cake. Before you start mixing ingredients, you need to prepare them properly.

First, you set up a clean workspace (the sandbox) where you can work without making a mess. Then, you gather all the ingredients you need – like flour, eggs, and sugar (data from various sources).

Next, you follow a recipe (ETLT) to prepare the ingredients – maybe sifting the flour, beating the eggs, and melting the butter. This ensures everything is in the right form for baking.

You also make sure you understand each ingredient – where it came from and how it behaves (learning about the data). Then, you clean, chop, and measure everything carefully (data conditioning) to ensure your cake turns out perfect.

And just like using the right tools in the kitchen, you have tools like Hadoop and OpenRefine to help you with data preparation, making the process smoother and more efficient.

MM - Phase 2 - Data PreparationMM - Phase 2 - Data PreparationStructured Notes on Phase 2: Data Preparation Phase 2: Data Preparation Overview: Data preparation is the process of assembling, organizing, and cleaning data for analysis. This phase transitions from business understanding to handling data specifics, akin to prepping ingredients for cooking to ensure they're ready for use. Key Processes: 1. Preparing the Analytic Sandbox: * Creation of Sandbox: Establish a dedicated analytic space for safe data manipulation. * Data Collection: Aggreg