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Data quality dimensions – from Accuracy to Uniqueness

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Gathering data for KPI results is one of the most common challenges that professionals face when measuring performance. An effective data gathering process should not only provide timely performance data, but also highly qualitative data. Designing such a process is indeed a challenge in itself, and establishing what quality data means is also a demanding task.

Data quality dimensions can differ from one company to another, as they indicate what characteristics are important in order to evaluate a set of data as being at the desired standards. There are a variety of features that can be explored, such as:

  • Accuracy;
  • Auditability;
  • Completeness;
  • Conformity;
  • Consistency;
  • Coverage;
  • Duplication;
  • Integrity;
  • Security;
  • Specifications;
  • Timeliness;

In practice, when collecting data for KPIs, only 3 to 6 characteristics are selected as criteria for evaluating data quality. In this context, I will present more details for some of the most popular data quality dimensions.

1. Accuracy – it indicates the extent to which data reflects the real world object or an event.

Examples:

  • The temperature shown by the thermometer is accurate if it is the same with the real temperature;
  • The addresses from the client datadase are accurate when they indicate the real location of customers;

Inaccuracy can be reflected by incorrect values, whether numbers or descriptive data (gender, location, preferences etc.) or other information that is not updated.

2. Completenesss – it refers to whether all available data is present. When data is due to unavailablity, this does not represent a lack of completeness.

Examples:

  • When performance data for $ Sales is required for the last six month, but results are submitted for the last five months only;
  • Customer details repository consists in name, surname, address and email. However, data for surname is missing in more than one client, even if this infomation should be available.
3. Consistency – it refers to providing the same data for the same object, even if this data appears in different reports, for example. It implies a syncronization of data across the organization.

Examples:

  • An employee status is terminated but his pay status is still active;
  • There are sales registered in January, but no orders registered in that month.
4. Conformity – it consists in ensuring that data is following a standard format, such as YYYY/MM/DD.

Examples:

  • Possible values for % Transaction processed are from +0% to +100%, the data for this KPI cannot be an absolute or negative value;
  • For customers gender there are only two possible values: Feminin and Masculin
5. Timeliness – it indicates wheather the data was submitted in due time, respecting the data gathering deadline. For example, in order to ensure that the monthly performance review meetings take place, data for KPIs has to be submitted one week in advance to the meeting.

6. Uniqueness – points out that there should be no data duplicates reported. Each data record should be unique, otherwise the risk of accessing outdated information increases. For example, we may have in our database two customers that were registered as Tom Adams and Thomas Adams, which in fact are the same person, but the latter has the latest details. Now this situation poses the risk that a Customer Service representative may access outdated information under Tom Adams and will not be able to contact the client.

We have to keep in mind that these dimensions are not always 100% met, meaning that data can be accurate but incomplete, or it can meet all 5 criterias except for timeliness. As managers have to make decisions based on data, it is very important to perform a short audit of data before compiling KPI results in a performance report, based on the quality dimensions presented above. Therefore, if data is not complete or there is an uniqueness issue, data users must be informed in order to keep this in mind when deciding.

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Comments (27)

  • Wegdan Hugaira

    |

    The most important criteria to make data quality dimensions:
    (Accuracy;Auditability;Completeness;Conformity;Consistency;Coverage;Duplication;Integrity;Security;Specifications;Timeliness)

    Reply

  • Houda Hedi Baccouche

    |

    The main characteristics to evaluate a set of data as being at the desired standards are:
    • Accuracy: the extent to which data reflects the reality.
    • Completeness: whether all available data is present or not.
    • Conformity: the data is following a standard format.
    • Consistency: always providing the same data for the same object.
    • Timeliness: the data is submitted in due time, respecting the data gathering deadline.
    • Uniqueness: Each data record should be unique, no duplication.

    Reply

  • Mohammad Jarrash

    |

    Data quality dimensions – from Accuracy to Uniqueness:
    – Accuracy
    – Completenesss
    – Consistency
    – Conformity
    – Timeliness
    – Uniqueness

    Reply

  • Muidh AlWuthainani

    |

    The most important characteristics of data gathers are:
    Accuracy, Auditability, Completeness, Conformity, Consistency, Integrity, Specifications & Timeliness.

    Reply

  • Nada Ahmed

    |

    Poor data quality can have a critical impact on the overall efficiency and effectiveness of an organization. In order to deliver a good quality of data you have to make sure that your data is corresponding to the desired standards, such as:
    • Accuracy,
    • Auditability
    • Completeness
    • Conformity
    • Consistency
    • Coverage
    • Duplication
    • Integrity
    • Security
    • Specifications
    • Timeliness.

    Reply

  • munirah almalki

    |

    Data quality dimensions – from Accuracy to Uniqueness:
    – Accuracy
    – Completenesss
    – Consistency
    – Conformity
    – Timeliness
    – Uniqueness

    Reply

  • Fahad Bin Shuayl

    |

    The important six dimensions of data quality are:
    • Accuracy
    • Completenesss
    • Consistency
    • Conformity
    • Timeliness
    • Uniqueness

    Reply

  • Khalid Nour

    |

    This article is tackling the challenge of gathering data for KPIs by a data custodian to provide timely and highly qualitative data. In order to evaluate a set of data as being at the desired standards, the following data quality dimensions need to be considered:
    1) Accuracy
    2) Auditability
    3) Completeness
    4) Conformity
    5) Consistency
    6) Coverage
    7) Duplication
    8) Integrity
    9) Security
    10) Specifications
    11) Timeliness
    12) Uniqueness

    Reply

  • Reem Alqnayah

    |

    The important six dimensions of data quality are:
    • Accuracy
    • Completeness
    • Consistency
    • Conformity
    • Timeliness
    • Uniqueness

    Reply

  • Iris Daisy De Jesus

    |

    The article is explaining how we need to have a certain format for gathering and presenting the data for our KPIs. Even though there are many criteria’s usually 3 to 6 are used in data quality. These are some of the ones mentioned:
    • Accuracy
    • Auditability
    • Completeness
    • Conformity
    • Consistency
    • Coverage
    • Duplication
    • Integrity
    • Security
    • Specifications
    • Timeliness

    Reply

  • Salem

    |

    No doubt Data are the backbone for any analysis and decision, moreover are challenging for organizations. Therefore. Accuracy of data are essential to have more accurate results, analysis, targets, etc.. and guide management to take suitable decision .

    The provides thoughtful ideas about the criteria of data gathering.

    Reply

  • Drissa TRAORE

    |

    Cet article nous parle des dimensions de la qualité des données – de la précision à l’unicité
    Dans un système de gestion de performance, la collecte de données de haute qualité pour les résultats de KPI, est l’un des défis majeurs auxquels font face les professionnels. Les dimensions de qualité varient d’une entreprise à une autre car Il existe une variété de fonctionnalités qui peuvent être explorées telles que: Précision; Audibilité; Exhaustivité; Conformité; Cohérence; Couverture; Reproduction; Intégrité; Sécurité; Caractéristiques; Opportunité;
    Les principales caractéristiques sélectionnées dans la pratique lors de la collecte de données pour les KPI comme critères d’évaluation de la qualité des données sont :
    1. Précision – il indique dans quelle mesure les données reflètent l’objet du monde réel ou un événement.
    2. Exhaustivité – il s’agit de savoir si toutes les données disponibles sont présentes. Lorsque les données sont dues à une indisponibilité, cela ne représente pas un manque d’exhaustivité
    3. Cohérence – il s’agit de fournir les mêmes données pour le même objet, même si ces données apparaissent dans différents rapports, par exemple. Cela implique une synchronisation des données dans toute l’organisation.
    4. Conformité – il consiste à s’assurer que les données suivent un format standard, tel que AAAA / MM / JJ.
    5. Actualité – cela indique à quel moment les données ont été soumises en temps voulu, en respectant la date limite de collecte des données
    6. . Unicité – souligne qu’il ne devrait y avoir aucun doublon de données signalé

    Reply

  • Sawsan Ghandour

    |

    The 10 characteristics of data quality found in the AHIMA data quality model are Accuracy, Accessibility, Comprehensiveness, Consistency, Currency, Definition, Granularity, Precision, Relevancy and Timeliness.

    Reply

  • Jorge Roman Torres

    |

    El artículo nos plantea la importancia de la recopilación de datos para los resultados de KPI, lo cual puede representar un desafío al medir el desempeño. La calidad de los datos es una de las dimensiones de mayor relevancia y cada empresa establece sus criterios. Las dimensiones de calidad de datos más comunes son: Precisión, completo, consistencia, conformidad, oportuno y singularidad.

    Reply

  • lama Labani

    |

    This article summarize the most important characteristics which we should follow when gathering kpis as:
    -data should be accurate , reliable, consistent , timeliness, complete and unique because based on this data top management will make important decisions to improve performance.

    Reply

  • Samir MohamedAbdelghafar

    |

    The importance of the data that will be used in the performance management process because it represents the right start to prepare a strong system that helps to achieve the goals of the organization, and therefore these data should be as follows:
    Accuracy;
    Auditability;
    Completeness;
    Conformity;
    Consistency;
    Coverage;
    Duplication;
    Integrity;
    Security;
    Specifications;
    Timeliness;
    And if there is the opposite, the results will be catastrophic

    Reply

  • Raoum

    |

    The main characteristics to evaluate a set of data as being at the desired standards are:
    •Accuracy;
    •Auditability;
    •Completeness
    •Integrity;
    •Security;
    •Specifications;
    •Timeliness

    Reply

  • Ghaida

    |

    The following data quality dimensions need to be considered:
    1) Accuracy
    2) Auditability
    3) Completeness
    4) Conformity
    5) Consistency
    6) Coverage
    7) Duplication
    8) Integrity
    9) Security
    10) Specifications

    Reply

  • Dorie Martija Saliao

    |

    Data quality dimensions can differ from one company to another, as they indicate what characteristics are important in order to evaluate a set of data as being at the desired standards. There are a variety of features that can be explored, such as:
    •Accuracy;
    •Auditability;
    •Completeness;
    •Conformity;
    •Consistency;
    •Coverage;
    •Duplication;
    •Integrity;
    •Security;
    •Specifications;
    •Timeliness;

    Reply

  • Njoud Badah

    |

    The 10 characteristics of data quality found in the AHIMA data quality model are Accuracy, Accessibility,
    Comprehensiveness,
    Consistency,
    Currency,
    Definition,
    Granularity,
    Precision,
    Relevancy and Timeliness.

    Reply

    Reply

  • Nada Alomair

    |

    Automation of processes and applying the proper policies and procedures can be assuring the accuracy of data.

    Reply

  • AlHanouf

    |

    Data quality dimensions is a challenging process, and each business has its own criteria when it comes to evaluating its data quality, as it could be 3 out of the following characteristic:
    • Accuracy;
    • Auditability;
    • Completeness;
    • Conformity;
    • Consistency;
    • Coverage;
    • Duplication;
    • Integrity;
    • Security;
    • Specifications;
    • Timeliness;

    Reply

  • Aseel Alrobaian

    |

    Focusing on Data quality dimensions is that what can be helpful to guide management to take suitable decision, when we gathering KPI’S with as:
    Accuracy, Auditability, Completeness, Integrity, Security, Specifications, Timeliness.

    Reply

  • Hadeel

    |

    The article is explaining how we need to have a certain format for gathering and presenting the data for our KPIs. Poor data quality can have a critical impact on the overall efficiency and effectiveness of an organization. Even though there are many criteria’s usually 3 to 6 are used in data quality. and therefore, these data should be as follows:
    • Accuracy
    • Auditability
    • Completeness
    • Conformity
    • Consistency
    • Coverage
    • Duplication
    • Integrity
    • Security
    • Specifications
    • Timeliness

    Reply

  • Hanouf

    |

    In addition, when we meet with the data custodian and collect the KPI data we have to make sure that the KPI data are accuracy, auditability, completeness, conformity, consistency, coverage, duplication, integrity, security, specifications, and timeliness.

    Reply

Leave a comment

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