Data quality in business
The increasing volume of data in organisations is in most cases not matched by its quality. Different data sources, different formats, different owners, inconsistent data management processes, all generate a growing problem with the quality of information and the quality of decisions based on it. As part of the training, participants will learn about the business processes involved in managing data, establishing responsibility for data and measuring the quality of information presented on reports.
Training scope
- Relevance, selection and processing of data
- Defining data quality
- Evaluation of stored data quality
- Improving data quality
- Data profiling
- Practical examples of data set quality assessment
- Systems to help maintain data quality: MDM, DQM and others
- Demonstration of a data quality support system and its impact on information quality
- Discussion on the acquired knowledge and experiences of Cogit implementations
Training audience
- Individuals responsible for data management within the company,
- Chief Data Officer,
- members of Business Intelligence competence teams,
- architects of data warehouses and Business Intelligence solutions
- and others for whom data quality is a key element of their work.
Training format
The training is planned in the form of a workshop (theoretical lectures combined with practical exercises). Duration: 2 days of 8 lessons of 45 minutes each.
In the case of dedicated training, it is possible to extend or shorten the content.
During the training, participants will be introduced to the theoretical underpinnings of each section of the training and most of these sections will allow them to consolidate the acquired knowledge in the form of exercises.
Fill in the form
Filling in the form is a preliminary declaration of participation, i.e. it does not oblige you to do anything until the final arrangements have been made.
Training programme
1. General information on the relevance, selection and processing of data
The first part of the training will present the assumptions of information theory in describing business processes through data; the definition of data; its relevance to the realisation of current and future business needs.
The origin of data by source (manual entry, import (push vs pull), integration with other systems, internal transformations, business and technical inventory (metadata) and data usability assessment vs business requirements will also be discussed.
The first section will conclude with an overview of data storage systems: local and distributed databases, text files, spreadsheets, XML, Big Data, relational, multidimensional and document databases, EAV.
2. Defining data quality
During this section, participants will learn the basics about data description procedures in relation to the information they are supposed to store:
- Data types, domains, ranges
- This section will conclude with exercises in presenting data types and each of the data error types discussed.
This section will conclude with exercises in presenting data types and each of the data error types discussed.
3. Data quality assessment
This section will give participants a broader view of data quality as part of the data management process by working with entire data sets (compared to the previous section focused on single attributes). Its topic will cover:
- Definition of “measurability” of data quality (quality measures, contractual validity ranges)
- Reporting on data quality measurements
- Analysing errors and investigating the causes of poor data quality
With the knowledge of how to create a data quality analysis, participants will build a sample data quality report.
4. Data management in the organisation
In this short section, the basics of building data management processes from the business side will be presented
- Data catalogue, attribute grouping, entities and the origin of their attributes, evaluation of attributes against data quality criteria (accuracy, availability, completeness, distribution, duplication, etc.).
- Formal management of data as an asset within the organisation (availability, usability, integrity and security)
- Role of the data steward
Finally, participants will be introduced to examples of data classification sheets as tools to assist in information management.
 |  |
5. Quality improvement, metadata and data profiling
Due to the fact that the possibility of correcting data in the source systems is sometimes limited, this operation must be performed during data processing. This part of the training will focus on methods to enhance data quality. These will include:
- Data profiling
- Evaluation and validation
- Data cleaning strategy
- Cleaning and enrichment
- Monitoring
Participants will also have the opportunity to look at an example of a tool themselves, which allows for the provision of comprehensive data information, including data quality.
6. Data quality maintenance support systems: MDM, DQM
In this practical part, students will become familiar with the practical impact of a data steward’s work on the quality of data presented on management reports. In particular, exercises will be conducted using:
- Master Data Management – managing the organisation’s “golden record”
- Data Quality Management – automatic data correction
7. Discussion of acquired knowledge and Cogit’s experience with implementations
The final part of the training will cover questions and practical approaches to data quality in different types of organisations (including financial institutions).
Trainer
We ensure practitioners, not theoreticians.
 | Hubert Kobierzewski Hubert works at Cogit as BI Practice Lead. He helps customers to gather their data and convert it into valuable information using Microsoft Data Platform technologies. For years he has been involved in wide range of Business Intelligence solutions – from gathering requirements, integration, data quality, data warehousing, to advanced analytical and reporting systems. He has carried out projects for many customers in Poland, Ireland, UK and Switzerland.Hubert manages two UG’s: Data Community Poland (Warsaw Chapter) and Warsaw Power BI User Group.In his spare time, he teaches DataViz on two universities: at Kozminski University, majoring in Big Data and Data Science in Management, and Warsaw University of Technology, Visual Data Analytics postgraduate program. |
Date
- A date will be set with the participants – once the minimum number of applications has been taken.
Location
- In the current situation (COVID), trainings take place online
- In the case of a dedicated training or gathering a group interested in a different location – the place can be determined individually
Price
Training price: 1600 pln + VAT. (Price includes: participation in on-line classes, training materials).
