This factsheet describes knowledge about Awareness of Data Quality in a nutshell. Awareness of Data Quality is highlighted in this factsheet from different angles in a structured way.
Awareness of Data Quality is an acknowledgement or realisation by everyone in an organisation, that their current skills and competencies are (becoming) ineffective, resulting in data of inadequate quality.
Awareness is attained when people understand their responsibilities and how their actions contribute to the achievement of the organisation’s objectives (ISO 9000:2015).
Knowledge is generally, expertise; familiarity gained through experience or association; recognition of a situation and familiarity with its complexity. Understanding of the significance of information. (DAMA-DMBOK Guide, 1st edition, page 3.)
Situational awareness is the perception of an environment's state and conditions at a point in time. (DAMA-DMBOK Guide, 1st edition).
|Skill||Specific learned abilities one requires to perform a given task successfully||Programming-skills, problem-solving skills|
|Competency||Knowledge and behaviours that lead one to be successful at a task||Conscientious, accurate, knowing about standards and audits applicable in certain sectors|
Awareness and Knowledge are also used interchangeably, whereas in training & development, these are considered as categories or labels for proficiency, each with their own specific levels for proficiency:
|Proficiency Category||Proficiency Level||Skill||Competency|
|Mastery||Level 4.x Level 4.1||(out of scope for this factsheet)||(out of scope for this factsheet)|
|Skilled||Level 3.x Level 3.1||(out of scope for this factsheet)||(out of scope for this factsheet)|
|Knowledge||Level 2.x Level 2.1||Skills to e.g. inspect and correct data quality issues using tools with some help or guidance from others.||Conscientious, accurate approach, etc|
|Awareness||Level 1||Skills to acknowledge, recognise or become familiar with certain terminology, procedures, policies, other people’s work, etc, as a foundation to improve data quality.||General interest in data quality developments, challenges, or having a “helicopter view”, etc.|
Awareness of Data Quality has only one proficiency level and is about leadership and staff having the skills and competencies to recognise or acknowledge the occurrence of inadequate data quality and its impact on the organisation.
After having awareness, the next step is “Knowledge of Data Quality”. This is about having the skills and competencies to take effective actions to improve the quality of data to an adequate level with some guidance from others.
The labels of the proficiency categories “Skilled”, “Mastery”, the number of proficiency levels, and descriptions of the skills and competencies may vary from organisation to organisation.
The purpose of awareness of Data Quality from leadership and staff, is to positively contribute to:
|Plan||1. to measure awareness 2. to prepare awareness program|
|Do||3. to execute awareness program (raise awareness) 4. to repeat awareness program periodically|
|Check||5. to monitor awareness 6. to evaluate awareness program|
|Act||7. to adapt awareness program|
Managing awareness of Data Quality may have different objectives (i.e. creating awareness, maintaining awareness, or increasing awareness):
When mentioning ‘improving awareness’ or ‘raising awareness’, the above objective needs to be clearly stated, as well as for whom (leadership and/or staff), over which period of time and through what medium (i.e. trainings, interactive workshops, awareness campaigns, internal audits, coaching, etc).
Improving or raising awareness through communications requires the communications to:
This will allow the intended audience to internalise the awareness message and reduce the risk of ignoring the message.
|Effectiveness of “Awareness of Data Quality”||Leadership and staff understand the consequences of inadequate data quality of the organisation. Their understanding meets the required proficiency level for awareness. Leadership and staff attained the required proficiency level, which results in data of adequate quality.|
|Cost-effectiveness of “Awareness of Data Quality”||Awareness of Data Quality leads to a positive business case, i.e., the benefits are higher than the costs.|
Figure 1: Relationships of Awareness of Data Quality with other concepts.
Below are three stories set in three different contexts, high-lighting the occurrence of awareness and its implications for meeting an overall objective. Text shown in bold indicates what the awareness means, and the purpose is shown in italic.
One day you decide to take a brisk walk in a park. Just before reaching home, you see a pedestrian collapsing at the corner of the street. You quickly rush towards the pedestrian to see if you can help. Having been trained in basic first-aid, you are able to quickly recognise the key-signs of a cardiac arrest. You immediately call for an ambulance and explain the situation to the operator. Acknowledging, that you have not been trained to provide mouth-to-mouth resuscitation, you suddenly remember that an Automated External Defibrillator (AED) may be of help: AEDs can assist users to help stabilise patients. You ask a bystander to locate an AED device. While attaching the AED device to the patient’s body, the ambulance responders arrive. Several days after this incident, you decide to take an advanced course in first-aid.
Consuming alcohol now and then may be considered a relaxing pastime. Driving a car can be comfortable, quick and safe. But: driving shortly after consuming alcohol may cause unsafe situations for all traffic participants, possibly causing traffic casualties. Governments can use communication campaigns specifically aimed at youngsters. These campaigns can help youngsters to change their behaviour towards drinking and driving, so that the number of traffic casualties will reduce. The awareness message may, for example, focus on how alcohol affects youngsters’ ability to safely control a vehicle.
An organisation in the financial sector had been collecting data on separate storage repositories for several years, without regularly looking at the quality of the data. To simplify the annual reporting process, managers of various business-units suggested to first migrate all data to a central database and to improve any issues with data quality after the migration. One manager suggested the opposite: performing a data migration has risks and requires a careful planning and execution, so why not first understand how the quality of the data impacts the final report? Over the next few weeks, she prepared and organised several interactive workshops with leaders and representative staff of the business-units. She could relate to how participants grappled with interpreting the data quality levels in their business-units. She then compared these results with results for data-lineage and presented this to the entire team. Now everyone became aware of how the quality of the data was impacting the overall quality of the reported data.
ALARP (As Low as Reasonably Practicable), Wikipedia article.
DAMA (2017). DAMA-DMBOK. Data Management Body of Knowledge. 2nd Edition. Technics Publications Llc. August 2017.
DAMA Dictionary of Data Management. 2nd Edition 2011. Technics Publications, LLC, New Jersey.
DAMA-DMBOK Guide, 1st edition.
Fairness, Accountability, Confidentiality, and Transparency (FACT) in AI, A. Lucic, 9 April 2020, University of Amsterdam.
ISO 9000:2015(en). Quality Management Systems – Requirements.
ISO 9001:2015(en). Quality Management Systems – Fundamentals and vocabulary.
Members of the Working Group of Data Quality of DAMA-NL