CRM is the core system for most companies as for their ability to grow and their efficiency in...
Design your quality policy
Design a Data quality policy
To ensure data high quality in a company or organization, it is necessary to have a comprehensive and detailed Data Quality Policy that includes planning, execution and continuous monitoring of actions, procedures and rules. This should be a holistic approach aiming at preventing problems, improving and maintaining a high level of quality in the company's data. This policy will only be effective if it relies on management's commitment and if it is imposed across the company as well as on its relations with third parties.
Data quality manual
The data quality manual should be the main reference for all related issues and thus it is the task to start with. This can be implemented in different ways, depending of the case.
Integration into an existing quality system
For organizations already implementing a total quality management system (usually with an ISO EN 9001 certification) applying to all or part of its operation, the design of a Data Quality Policy takes into account the existing quality policy in an effort to complement it. This about adding additional procedures and work instructions, amend existing ones, if necessary, making a substantial review of the quality system in all aspects related to data quality.
The advantage in this case is that there is a comprehensive and coherent quality system that is more efficient for the company but also better exploitable due to its certification. In this case it is recommended to integrate other relevant policies such as the data protection policy with the same advantages.
Create a standalone policy
A Data Quality Policy is designed from scratch, addressing only data management and related processes, rules and functions affecting data quality. All the data quality goals an strategies, related processes and instructions will be included in the Data Quality Manual which will be the reference for all data quality issues..
ISO EN 8000 certification
Whether a total quality management certification is in place or not, the Data Quality policy could be certified with ISO EN 8000. This certification is specialized for data quality and could enhance the policy, offering more value and validity.
Setting the data quality framework
A consistent Data Quality Policy must include at minimum the following:
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Quality objectives and rules
Defining the rules and standards that the data must meet in order to fulfill operational and commercial targets. -
Quality assessments schedule
A regular evaluation program is drawn up and the cases of extraordinary evaluations are defined, including the method and terms of their execution. -
Solving existing problems
It refers to tasks to actively improve the quality of data by correcting, enriching, improving / reformulating or deleting data.?
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Monitoring and continuous improvement
The systematization, monitoring of the implementation of the policy, the periodic revision of objectives, rules, processes and instructions and the continuous training of the staff.
Related posts
You can see below some related articles from our blog
There is a common experience of people who have been working with CRMs for a long time: the problem...
Why defining rules and standards is critical?
The definition of error and the correction is usually subjective, based on norms set up for the specific case. This will much depend on the intended use of every piece of information but also on the system's setup & capabilities as well as the related processes.
For example, mandatory information is very different across industries, segments or even companies. Even a common field such as the the customer's first and last name are not always treated in the same way. For a financial institutions those are part of the legal identity of the customer and have to completed and accurate while for an online service provider, they are just a name to call them in the newsletters.
An other interesting example are the formatting rules: A specific typo or format issue may be a critical error for a company's system that needs immediate correction, because it may cause problems to the system. This is the case of phone numbers that are synced / linked to call center or telephony services. while may be negligible for other cases.
Prepare data & configure tools
Depending on the case, it will probably be needed to export data from one or more sources, to make some initial filtering or transformation and make a first analysis to make sure that we are ready to proceed to the next stage.
Next job is to to configure our analytics tool to fit the specific data and to serve the controls we want to make. Depending of the tool used, this may include writing a few code scripts.
Usual data correction tasks
From a technical point of view, there are some specific tasks that can be done during the data cleansing process, depending on the outcome of the previous stages. Some usual tasks are:
- correcting typos based on grammar or other generic rules
- correcting format errors based on specific standards
- homogenize information by transforming data according to rules
- deduplicating information by deleting or merging data records
- data removing by deleting or just flagging records that can not be corrected
- matching data from different fields, tables or systems