Data Management ★★ Practitioner Level
Are you struggling to make heads or tails of your data? Perhaps you’re experiencing analysis errors due to poor quality datasets? If you are running into these problems in your organization then Data Management is the training you need to improve your future management.
DATA BUILDING & MODIFICATION
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About the courseData is one of the key assets for companies, which makes managing data a crucial competency in business today. It has become standard that business teams use at least some data to make their decisions in the everyday business processes. Having a lacking or faulty data management process in place can, therefore, lead to major errors and inconsistencies across analyses, with major impact both short and long term. Therefore, it is important that all members understand what is needed to manage data well to avoid these common mistakes. This Data Management training will teach you how to identify errors in your data and ensure that the results are accessible and comprehensible by other teams. With an extensive focus on the basics of data management and methods to execute quality control in your business
Why this is for youHave you experienced working with datasets that were lacking in explanation, quality, or that were contradictory? Many organizations don’t apply best practices in data management often rendering them ineffective. In this module, you will learn to discuss data strategies, how to ensure your data contains no errors, and is both comprehensible and functional in your company.
For whomThis module is designed for business leaders or anyone looking to get the most out of their data! Does learning best practice methods and streamlining your data management sound like what you need? Then this is the course for you!
What you’ll learn
- The difference between defensive and offensive data strategies and the impact on data management
- The evolution of the organizational data strategy
- The roles and responsibilities of data management within an organization
- Methods and techniques for business experts to create alignment on business definitions and to ensure a scalable data integration layer
- The dimensions of data quality
- The steps in the data quality management process
- Toolbox for business experts to manage the quality of own data and results
- Defensive/offensive strategy – Recognize and experience the (dis)advantages of defensive versus offensive strategy
- Achieve a centrally managed data understanding – Able to explain the steps and role of the business in achieving alignment on centrally organized data management
- Dealing with data quality – Capable of explaining the role of business experts in the data quality management cycle, and identifying major data quality issues in datasets and analyses