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

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    Brian Lyew

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    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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  • Automating Business Intelligence Company Reports in a Mixed Reporting Environment

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    Brian Lyew

    In

    When some company data reports are already automated… but many others are not.

    Automating business intelligence (BI) reports is an essential step toward improving decision-making efficiency, reducing manual workload, and ensuring data consistency. In many organizations, reporting environments are fragmented. Some reports are automated, while others remain manually generated. This disparity can lead to inefficiencies, inconsistencies, and bottlenecks in business operations.

    When a client tasks Dieseinerdata to automate company reports with a mixed bag of reporting, strategic approach is necessary. Here, we outline a step-by-step method on how to automate data reports; how to assess, prioritize, and implement automation in such an environment.

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  • Everything is Becoming a Web App!

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    Brian Lyew

    In

    What Do We Mean by “Web Application” in the Context of Data Analytics, Business Intelligence, and Data Science?

    In today’s data-driven world, web applications play a crucial role in how organizations analyze and interact with their data. Whether it’s a dashboard with data visualizations providing real-time insights, a machine learning model delivering predictions, or a business intelligence (BI) tool assisting decision-making, web applications are the backbone of modern data workflows.

    But what exactly do we mean when we say “web application” in the context of data analytics, business intelligence (BI), and data science? This article explores the definition, key components, use cases, and best practices for developing web applications in these domains.

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  • What Exactly is the Difference Between Data Analytics and Business Intelligence?

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    Brian Lyew

    In

    What Exactly is the Difference Between Data Analytics and Business Intelligence?

    In today’s data-driven world, terms like “Data Analytics” and “Business Intelligence” (BI) are often used interchangeably.

    While both concepts revolve around making better use of data to drive decisions, they differ significantly in their scope, approach, and purpose. Understanding these distinctions is key for organizations looking to implement data solutions effectively.

    Let’s delve into the fundamental differences between Data Analytics and Business Intelligence.

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