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  • Excel VBA vs. Custom Data Solutions: When to Upgrade to a Dieseinerdata Web-based Analytics Platform

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    Brian Lyew

    In

    When to Upgrade Your Analytics

    Excel has long been the go-to tool for businesses managing data, running reports, and performing basic analytics. It is familiar, flexible, and accessible to employees across different departments. However, as businesses scale, Excel’s limitations become apparent, making the transition to a more robust system necessary. Dieseinerdata has upgraded several clients In this article, Dieseinerdata will explore the drawbacks of relying solely on Excel and identify the right time for businesses to upgrade to a web-based analytics platform.

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  • A Beginner’s Guide to Key Data Analytics Terms

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    Brian Lyew

    In

    Key Analytics Terms to Make Informed Decisions

    In today’s data-driven world, business professionals must understand key analytics terms to make informed decisions. Whether you’re working with data analysts or just starting your journey in business intelligence, knowing these fundamental concepts will help you communicate effectively and leverage data insights. We here at Dieseinerdata wrote a glossary of essential analytics terms every business professional should know.

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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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