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  • What Exactly is Data Engineering?

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    Brian Lyew

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    What Exactly is Data Engineering?

    Like oil to a car, data fuels your business

    In the digital age, data is the new oil. It powers decision-making, innovation, and even the products we use daily. But how does raw, unstructured data transform into actionable insights?

    The answer lies in data engineering. While it might not always be in the spotlight, data engineering is the backbone of the modern data ecosystem. Let’s break down what it is and why it matters.

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  • A Guide to the CRISP-DM (Cross-Industry Standard Process for Data Mining) Method

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    Brian Lyew

    In

    The Key Strength of CRISP-DM is its Flexibility

    The CRISP-DM (Cross Industry Standard Process for Data Mining) methodology is a widely used framework for structuring data mining and analytics projects. Developed in the late 1990s, it provides a systematic approach to tackling data-related problems across various industries.

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  • How to Clean and Prepare Your Data for Better Insights

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    Brian Lyew

    In

    Clean Data = the Foundation for your Company

    In the world of data analytics and business intelligence, clean and well-prepared data is the foundation for accurate insights. Poor data quality leads to misleading conclusions, flawed decision-making, and wasted resources. Before diving into complex analysis or visualization, it’s crucial to ensure your data is free from errors, inconsistencies, and redundancies. In this guide, Dieseinerdata will walk through the essential steps to clean and prepare your data for better insights.

    Step 1: Understand Your Data

    Before cleaning data, take the time to explore and understand it. This includes:

    • Identifying the source of your data (databases, spreadsheets, APIs, etc.).
    • Checking for missing or inconsistent values.
    • Understanding the format, structure, and expected ranges of data fields.
    • Identifying anomalies or outliers.

    Performing an initial exploratory data analysis (EDA) will give you a clearer picture of the data’s current state and guide your cleaning process.

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  • AI and Automation in Data Analytics: What’s Hype and What’s Real?

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    Brian Lyew

    In

    Examining where AI truly adds value and where expectations need to be tempered.

    In the rapidly evolving world of data analytics, artificial intelligence (AI) and automation have become buzzwords that dominate discussions. Companies across industries are investing heavily in AI-driven analytics, expecting transformative outcomes. However, not all promises of AI and automation in analytics hold up under scrutiny. While some applications genuinely revolutionize decision-making and efficiency, others are overhyped and fail to deliver tangible results.

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  • The ROI of Good Data: How Clean Data Boosts Profits

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    Brian Lyew

    In

    The Profits from Maintaining Clean, Accurate, and Well-organized Data

    In today’s digital economy, data is the lifeblood of any organization. Businesses collect vast amounts of information daily, from customer interactions to sales transactions and operational metrics. Clients can only realize the true value of this data when the data is accurate, well-organized, and effectively utilized. Poor data quality lead to costly errors, inefficiencies, and missed opportunities. Clean data empowers companies to make informed decisions, optimize operations, and increase profitability. In this article, Dieseinerdata will explore the financial benefits of maintaining clean data and how it directly impacts the bottom line.

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  • The Larger the Frontend, the Larger the Backend

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    Brian Lyew

    In

    What’s going on with that Backend?

    For a data analytics web application, the back-end is a critical component that powers the front-end application by handling data processing, storage, authentication, and API interactions. Below are the key components that make up the back-end:

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