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Data Quality Audit – We offer you expert and individual advice
Are you facing the following challenges?
- Cost-cutting through minimizing direct mail returns and returned debit items
- Reduction of a high customer churn rate
- Planning of a pending data migration project caused by the implementation of a CRM system
These are just a few examples of the challenges which relate to data quality. We help you to overcome these challenges. We provide you with expert support with an efficient solution - the Data Quality Audit. An individual status analysis is the first step in preparing a plan for the optimization of your data with a high potential for success.
The goals of the Data Quality Audit are:
- Precise information on the current status of the data quality.
- Recommendations for action and a solution concept to optimize the data quality in the short term and to maintain the data in good condition in the long term.
- Overview of potential cost-savings, estimated costs, ROI and advantages of a DQ solution.
Because high-quality customer data is the prerequisite for successful sales, a reliable basis for business planning, efficient marketing and good customer service.
The three Data Quality Audit stages - depending on the project and the requirements
The Data Quality Audit has a modular structure and can be implemented in different stages depending on your requirements and expectations:
Data Quality Check
Address validation and duplicate detection as an indication of the data quality
The Data Quality Check carries out an address validation or validates the address quality of a pool of data and determines the proportion of data records which are duplicated.
The data is checked for:
For this we use:
The result of the Data Quality Check helps you to gain an initial impression of the actual condition of your data on the basis of a representative extract of the data. The results are presented in your company by our staff in the form of statistics and detailed examples. The degree of contamination is an indication of the need for data cleansing.
The Data Quality Check carries out an address validation or validates the address quality of a pool of data and determines the proportion of data records which are duplicated.
The data is checked for:
- Completeness
- Uniqueness
- Accuracy
- Data integrity
For this we use:
- Postal validation
- Name evaluation
- Duplicate checking
The result of the Data Quality Check helps you to gain an initial impression of the actual condition of your data on the basis of a representative extract of the data. The results are presented in your company by our staff in the form of statistics and detailed examples. The degree of contamination is an indication of the need for data cleansing.
Data Quality Analysis
A process analysis focused on data qualityA process analysis focused on data qualityDetailed Data Quality Analysis in association with business rules
The Data Quality Analysis provides an extensive detailed evaluation of the customer data.
An opening workshop specifies the framework and the goals of your data validation and analysis. In this respect, a discussion about the data structure, its meaning, consistency, uniqueness, clarity, correctness and completeness in association with the business rules for which it is required is very important. This is necessary to prevent apples being compared with oranges.
The results of the analysis are documented with regard to the following points:
In addition to a management summary, the documentation includes detailed descriptions of the analysis, which provides clear-cut information down to the field level. The results are presented in quantities (percentages and amounts) and graphics. Selected examples underpin the statistics.
The documentation provides important information about your current data quality and is an important basis for the further planning for a short- and long-term improvement in your data quality.
The Data Quality Analysis provides an extensive detailed evaluation of the customer data.
An opening workshop specifies the framework and the goals of your data validation and analysis. In this respect, a discussion about the data structure, its meaning, consistency, uniqueness, clarity, correctness and completeness in association with the business rules for which it is required is very important. This is necessary to prevent apples being compared with oranges.
The results of the analysis are documented with regard to the following points:
- Uniqueness of the data
- Validity of names
- Validity of addresses
- Results of the inspection of the data extract
- Format check
- Particular features and anomalies
In addition to a management summary, the documentation includes detailed descriptions of the analysis, which provides clear-cut information down to the field level. The results are presented in quantities (percentages and amounts) and graphics. Selected examples underpin the statistics.
The documentation provides important information about your current data quality and is an important basis for the further planning for a short- and long-term improvement in your data quality.
Data Quality Process Analysis
A process analysis focused on data quality
The DQ Process Analysis is carried out by the Uniserv Data Quality Consultants using the following analysis steps alternatively or in combination:
There is a close interaction between the quality of the data and the processes. Processes influence the way data is generated, changed and used. The efficiency of the processes depends to a high degree on the quality of the data they use. The process analysis is therefore used to carry out a systematic investigation into how the data errors came into being. Measures are derived from this and defined and prioritised, in order to eliminate these error sources.
The processes are explained and presented in an opening workshop. Amongst other things, the following questions should be answered here:
The analysis includes the following:
The most important results are presented, examined in more detail and correlations and causes discussed in a closing workshop.
Further steps or a follow-up project can be agreed upon on the basis of the results of the process analysis and the resulting procedure.
Finally and immediately following the workshop, all the results and findings are compiled in documentation with a data quality solution concept and a Management Summary.
The DQ Process Analysis is carried out by the Uniserv Data Quality Consultants using the following analysis steps alternatively or in combination:
- Review of the existing process or system description with regard to the DQ aspects
- Random accompaniment of the consumers while they work with the system (data input, data maintenance and data use)
- Application-specific assessments with consumers and stakeholders
There is a close interaction between the quality of the data and the processes. Processes influence the way data is generated, changed and used. The efficiency of the processes depends to a high degree on the quality of the data they use. The process analysis is therefore used to carry out a systematic investigation into how the data errors came into being. Measures are derived from this and defined and prioritised, in order to eliminate these error sources.
The processes are explained and presented in an opening workshop. Amongst other things, the following questions should be answered here:
- Which processes are relevant for the work with the respective data?
- Have these processes been clearly defined?
- How is the data acquired?
- How is data updated?
- How, for which purposes and by whom is the data used?
The analysis includes the following:
- Identification of the problem areas with regard to data quality
- Combination of the results of the data quality analysis with those of the process analysis
- Investigation of the specific interactions which can arise between the data quality and processes.
- Calculation of the potential cost-savings, the estimated costs and the return on investment and presentation of the advantages of a data quality solution.
The most important results are presented, examined in more detail and correlations and causes discussed in a closing workshop.
Further steps or a follow-up project can be agreed upon on the basis of the results of the process analysis and the resulting procedure.
Finally and immediately following the workshop, all the results and findings are compiled in documentation with a data quality solution concept and a Management Summary.
