Tags
Language
Tags
July 2025
Su Mo Tu We Th Fr Sa
29 30 1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 31 1 2
    Attention❗ To save your time, in order to download anything on this site, you must be registered 👉 HERE. If you do not have a registration yet, it is better to do it right away. ✌

    ( • )( • ) ( ͡⚆ ͜ʖ ͡⚆ ) (‿ˠ‿)
    SpicyMags.xyz

    Data Quality Fundamentals for Beginners

    Posted By: lucky_aut
    Data Quality Fundamentals for Beginners

    Data Quality Fundamentals for Beginners
    Duration: 49:15 | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 193 MB
    Genre: eLearning | Language: English [Auto]

    Data quality is a key business challenge of the 21st century.
    What you'll learn
    What is Data Quality?
    Why data quality is important?
    What are the important roles within Data Quality Management
    What are the different Data Quality Management techniques
    Requirements
    Any previous experience with data will be helpful but not necessary.
    Description
    Hello there,

    Data quality is a key business challenge of the 21st century. Quality has been an issue since the dawn of the IT profession and becomes increasingly challenging as the volumes of data increase and the uses for data expand. Regardless of your roles and responsibilities in IT - manager, developer, or technologist - you will at some time be challenged by data quality issues. And quality is an equal opportunity issue, imposing itself in every aspect from transaction systems to Web content and business intelligence

    Why is data quality important?
    Data quality matters because it significantly impacts your business performance. High-quality data helps you make better decisions and perform better actions, leading to increased revenue, decreased costs, and reduced risk. Low-quality data has the opposite effect, resulting in poor profitability and an increased risk that your business will fold prematurely.

    At the end of the course, you'll be able to grasp an all-round understanding of below concepts:
    Data Quality
    Data Strategy
    Data Governance
    Data Architecture
    Data Lifecycle Management
    Analytics and Data Science
    Data Quality Objectives
    6 Data Quality Dimensions and Examples
    Roles and Responsibilities of Data Owners and Data Stewards
    Data Quality Management Principles
    Data Quality Management Process Cycle
    Data Quality Issue Management
    Data Cleansing
    Outcome:
    Understand the various Data Quality areas
    Explore Data Quality policies and procedures
    Discuss data profiling, validation rules, facets, data cleansing, and more…
    Understand how Data Quality is linked to other data disciplines

    Thank you!

    Who this course is for:
    Professionals that want to undertand what is Data Quality and how it can help their organization
    Data Scientists
    Management that wants to gain knowledge in the importance of Data Quality Management
    Big Data Developers/Administrator

    More Info