Course

H32488

Successfully managing data quality («H32488»)

In this seminar, you will learn strategies, measures, and methods for sustainable data quality management for future-proof, data-based decision-making.
Duration 2 days
Price 1'800.–
Please note:  This is a reseller course and as such excluded from any discounts (excluding promo codes).
Course information This course is held in cooperation with Haufe Akademie. For the purpose of conducting the course, the participants' data will be transmitted there and processed there under their own responsibility. Please take note of the relevant Privacy notice.
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Course facts

  • Recognizing the need to introduce data quality management from a legal and economic perspective
  • Learning how to improve the quality of your own data in a targeted and sustainable manner
  • Recognizing how opportunities and risks related to data quality can be identified and evaluated within the company and which investments are worthwhile
  • Learning how data quality criteria can be defined and measured
  • Learning how improvement measures in data management can be derived with corresponding cost-benefit analyses
  • Receiving a guide on how data quality management can be set up and sustainably established in the company
 1 Introduction and definition
  • From data to information to competitiveness
  • Definition of data quality and data quality management (DQM)
  • Why DQM? – Drivers for the introduction of company-wide DQM
  • Added value of data quality management for organizations
  • DQM maturity model (Where would you classify your company?)
2 Poor data quality
  • Data quality requirements (legal/economic)
  • Causes and effects of poor data quality on the organization as a system
  • Impact of poor data quality on key performance indicators
  • Data quality classes and subjects of investigation for assessing poor data quality
3 Defining and measuring data quality criteria (data profiling)
  • Overview of data quality dimensions for optimal measurement of data quality
  • Defining data quality dimensions (practical exercise)
  • Applying data quality dimensions (practical example)
4 Development of logic trees for determining and evaluating cause/effect
  • Principles of logic trees and added value
  • Applying logic trees (practical exercise)
  • Evaluating the findings from logic trees
5 Deriving improvement measures and analyzing costs/benefits
  • Further developing logic trees for decision-making for an optimal cost/benefit analysis (practical exercise)
  • Deriving improvement measures (practical exercise)
6 Setting up data quality reporting and a data quality index
  • DQM control loop
  • From prototyping to DQ standard reporting
  • Developing and implementing a process-oriented DQ index
7 Data quality organization and processes
  • Roles and responsibilities
  • Standard processes: data profiling, data quality monitoring, error tracking, and improvement
  • Classifying data quality in the data governance model
 This course is aimed at data quality managers, specialists and executives from controlling, finance, marketing, data quality management, and anyone involved in digitization and data analysis. There are no formal requirements for participation in this course. To ensure that you receive any necessary documents by mail in good time, we recommend booking at least 14 days before the seminar date.

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