How to manage data quality in the Data Warehouse?
With the aim of building a subject-oriented and integrated database, a data warehouse is the key ingredient in taking quick and intelligent business decisions. The quality of data and data warehousing is thus a critical concern for organizations. Moreover, since the data in the warehouse gets collected from multiple disparate sources, it needs attention in standardizing and cleansing the data before loading it to the destination. All in all, data quality is a must-have while dealing with such large amounts of data and maintaining it in a centralized warehouse.
Data Warehouse Tutorial Video
Mainly, data quality focuses on including duplicate-free data, conforming to standards and creating conformed data marts, and cleaning data at each stage before final loading. The system must check for any duplicates or near duplicates to insist on a clear understanding of the data warehouse and receive appropriate results when the DWH is queried. Data should also be meaningful while it transfers from one system to another and during the formation of OLAP cubes. Further, data cleaning in batch and real-time processing benefits both the business groups and clients on the whole.
Also, check out the difference between a data lake and a data warehouse.
The Data Warehouse Quality Management:
- Delivers end-to-end quality solutions
- Enforces Data Quality and Data Profiling as important processes during the implementation of the data warehouse
- Keeps a check on the metadata and its storage repository to ensure
- Generates mappings for data correction based on business rules and ethics.
Phases in data quality management lifecycle:
There are primarily four phases in the data quality management lifecycle:
- Quality Assessment
- Quality Design
- Quality Transformation
- Quality Monitoring
In the Quality assessment phase, the quality of the source data is determined by adopting the process of Data Profiling. Data profiling discovers and unravels irregularities, inconsistencies, and redundancy occurring in the content, structure, and relationships within data. Thus, you can assess and list down the data anomalies before proceeding further.
The next phase refers to Quality design, which enables business people and groups to design their quality processes. For instance, individuals can enumerate legal data and relationships within data objects complying with the data standards and rules. In this management step, the managers and administrators also rectify and improve the data using data quality operators. Similarly, they can also design data transformations or data mappings to ensure quality.
Next, the Quality Transformation phase runs correction mappings used for correcting the source data.
The last phase of this cycle includes Quality Monitoring, which refers to examining and investigating the data at different time intervals and receiving notification if the data breaches any business standards or rules.
The data Profiling process integrates with ETL processes in the data warehouse including the cleaning algorithms and other data rules and schemas specified. It helps users to find:
- a domain of valid product codes
- product discounts
- columns having email address patterns
- data inconsistencies and anomalies within columns
- relations between columns and tables
Such findings will enable you to manage data and data warehousing in better way.
Enrich your knowledge of Snowflake Schema in Data Warehouse, its features, and benefits by reading this insightful tutorial!
OLAP vs OLTP Tutorial Video
DWH Quality Management Tools
Lately, there are various quality solutions provided to feed data warehouses and improve their inherent quality. Informatica, Pentaho, and Talend Open Studio are a few to mention.
Talend Data Quality for a Data Warehouse
Advantages of using Talend Open Studio as a Data Warehouse Management tool:
- Providing a powerful open-source data quality solution, Talend Open Studio is one of the most widely used solutions to manage data warehouses. It ensures accurate project management by using advanced profiling techniques to understand the data challenges early and better and think of cost-effective solutions.
- Talend is also referred to unified data management platform as it combines the three major data processes, i.e. data profiling, data integration, and data quality in a single user-friendly interface, enabling you to run multiple applications without manual coding.
- It ensures real-time data quality processes and high-quality data feeds for accurate business intelligence. The unified methodology will ensure the scalability, flexibility, and reliability of data in the organization.
- Talend’s profiling processes and ETL functionality are extremely useful to quickly load the data warehouse and operational data sources. It provides a detailed list of database and business application connectors. Talend is capable of tracking the key performance data quality metrics.
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Talend data quality allows users to take advantage of the most time-saving and cost-effective processes as it promotes the following features:
- Accurate profiling, which understands the data disputes early and thus, help users create a real-time plan to implement.
- Matching allows business people to eliminate duplicate records and redundant information.
- Consolidating records is yet another important feature of Talend facilitating accurate data by the best integration of multiple records.
- Standardization of data fields and values makes data sharing effective and easy.
- Referential Integrity helps in meeting business objectives by enriching consumer data with external sources.
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