Data Quality Management
Data quality is crucial when it comes to utilizing data in business development. Among other things, we help our clients in measuring data quality and correcting and preventing quality problems.
-Juha Loukola, Senior consultant
Choose Talent Base as your partner and grow with us to become data management forerunner.
Competitive advantage from high-quality data
First-class quality data helps companies to work efficiently, save costs, improve customers satisfaction and aids clients’ in making purchase decision. Quality data enables more reliable reporting that is prerequisite for information-based decision making, i.e. leading with knowledge.
Data Quality Management refers to methods and tasks, which ensure that data is error-free, seamless and fits the purpose as much as possible. This ensures that it meets requirements for business, standards, and information systems.
Only quality data brings a competitive advantage on the market, so it is worth investing in its management.
Data quality management benefits
Data quality problems aren’t easy to identify, because business is running, nonetheless. Small data problems lead to big expenses if, for example, salespeople can’t find the right customers, orders are going to the wrong destinations, or suppliers’ registers are abused.
Investing in data quality management
- helps processes to work efficiently and save costs
- improves customer satisfaction and experience and aids clients’ in making purchase decision
- enables more accurate and consistent reporting and data analysis, so it becomes easier to make a decision
- reduces the number erroneous and duplicate data, so overall data utilization is faster
- makes it easier to find the right information
- reduces the probability of business-critical risks’ realization
Development of data quality management
We design complete solutions that are leading to data quality improvements and which would help to build a foundation for developing the capabilities needed to manage data quality.
We define policies and responsibilities for data quality management and support their implementation. We help to develop an operating model aimed at proactively initiating actions in the organization to maintain data quality.
As a technology independent operator, we are always looking for the best client-oriented solution.
Data quality current state analysis
We identify and document the requirements for the data and create quality from which we analyze the current state of data quality.
We review the current state of the existing policies and technologies and map the organization’s capabilities for data quality management. We identify key data users and their requirements for data at different stages of the lifecycle.
Data harmonization is topical as part of information systems’ development projects (ERP, MDM, PIM or CRM projects), when it is desired to unify data from several different resources into one common information system.
We define conversion rules and identify and document new or changed information required by the processes to provide new systems with high-quality that better supports business needs from the very beginning.