Towards data quality by design – ISO/IEC 25012-based methodology for managing DQ requirements in the development of IS

It is obvious that users should trust the data that are managed by software applications constituting the Information Systems (IS). This means that organizations should ensure an appropriate level of quality of the data they manage in their IS. Therefore, the requirement for the adequate level of quality of data to be managed by IS must be an essential requirement for every organization. Many advances have been done in recent years in software quality management both at the process and product level. This is also supported by the fact that a number of global standards have been developed and involved, addressing some specific issues, using quality models such as (ISO 25000, ISO 9126), those related to process maturity models (ISO 15504, CMMI), and standards focused mainly on software verification and validation (ISO 12207, IEEE 1028, etc.). These standards have been considered in worldwide for over 15 years.

However, awareness of software quality depends on other variables, such as the quality of information and data managed by application. This is recognized by SQUARE standards (ISO/IEC 25000), which highlight the need to deal with data quality as part of the assessment of the quality level of the software product, according to which “the target computer system also includes computer hardware, non-target software products, non-target data, and the target data, which is the subject of the data quality model”. This means that organizations should take into account data quality concerns when developing various software, as data is a key factor. To this end, we stress that such data quality concerns should be considered at the initial stages of software development, attending the “data quality by design” principle (with the reference to the “quality by design” considered relatively often with significantly more limited interest (if any) to “data quality” as a subset of the “quality” concept when referring to data / information artifacts).

The “data quality” concept is considered to be multidimensional and largely context dependent. For this reason, the management of specific requirements is a difficult task. Thus, the main objective of our new paper titled “ISO/IEC 25012-based methodology for managing data quality requirements in the development of information systems: Towards data quality by design” is to present a methodology for Project Management of Data Quality Requirements Specification called DAQUAVORD aimed at eliciting DQ requirements arising from different users’ viewpoints. These specific requirements should serve as typical requirements, both functional and non-functional, at the time of the development of IS that takes Data Quality into account by default leading to smarter and collaborative development.

In a bit more detail, we introduce the concept of Data Quality Software Requirement as a method to implement a Data Quality Requirement in an application. Data Quality Software Requirement is described as a software requirement aimed at satisfying a Data Quality Requirement. The justification for this concept lies in the fact that we want to capture the Data Quality Software Requirements that best match the data used by a user in each usage scenario, and later, originate the consequent Data Quality Software Requirements that will complement the normal software requirements linked to each of those scenarios. Addressing multiple Data Quality Software Requirements is indisputably a complex process, taking into account the existence of strong dependencies such as internal constraints and interaction with external systems, and the diversity of users. As a result, they tend to impact and show the consequences of contradictory overlaps on both process and data models.

In terms of such complexity and attempting to improve the developing efforts, we introduce DAQUAVORD, a Methodology for Project Management of Data Quality Requirements Specification, which is based on the Viewpoint-Oriented Requirements Definition (VORD) method, and the latest and most generally accepted ISO/IEC 25012 standard. It is universal and easily adaptable to different information systems in terms of both their nature, number and variety of actors and other aspects. The paper proposes both the concept of the proposed methodology and an example of its application, which is a kind of manual step-by-step guidance on how to use it to achieve smarter software development with data quality by design. This paper is a continuation of our previous study. This paper establishes the following research questions (RQs):

RQ1: What is the state of the art regarding the “data quality by design” principle in the area of software development? What are (if any) current approaches to data quality management during the development of IS?

RQ2: How the concepts of the Data Quality Requirements (DQR) and the Viewpoint-Oriented Requirements Definition (VORD) method should be defined and implemented in order to promote the “data quality by design” principle?

Sounds interesting? Read the full-text of the article published in Elsevier Data & Knowledge Engineering – here.

The first comprehensive approach to this problematic is presented in this paper, setting out the methodology for project management of the specification for data quality requirements. Given the relative nature of the concept of “data quality” and active discussions on the universal view on the data quality dimensions, we have based our proposal on the latest and most generally accepted ISO/IEC 25012 standard, thus seeking to achieve a better integration of this methodology with existing documentation and systems or projects existing in the organization. We suppose that this methodology will help Information System developers to plan and execute a proper elicitation and specification of specific data quality requirements expressed by different roles (viewpoints) that interact with the application. This can be assumed as a guide that analysts can obey when writing a Requirements Specification Document supplemented with Data Quality management. The identification and classification of data quality requirements at the initial stage makes it easier to developers to be aware of the quality of data to be implemented for each function during all development process of the application.

As future work thinking, we plan to consider the advantages provided by the Model Driven Architecture (MDA), focusing mainly on its capabilities of both abstraction and modelling characteristics. It will be much easier to integrate our results into the development of “Data Quality aware Information Systems” (DQ-aware-IS) with other software development methodologies and tools. This, however, is expected to expand the scope of the developed methodology and consider various feature related to data quality, including the development of a conceptual measure of data value, i.e., intrinsic value, as proposed in.

César Guerra-García, Anastasija Nikiforova, Samantha Jiménez, Héctor G. Perez-Gonzalez, Marco Ramírez-Torres, Luis Ontañon-García, ISO/IEC 25012-based methodology for managing data quality requirements in the development of information systems: Towards Data Quality by Design, Data & Knowledge Engineering, 2023, 102152, ISSN 0169-023X,

Our – EOSC TF “FAIR Metrics and Data Quality” paper “Towards a data quality framework for EOSC” is released!🍷🍷🍷

I am glad to announce the release of “Towards a data quality framework for EOSC” document, which we have been hard at work on hard for several months as the Data Quality subgroup of the “FAIR Metrics and Data Quality” Task Force European Open ScienceCloud (EOSC) Association) – Carlo Lacagnina, Romain David, Anastasija Nikiforova, Mari Elisa Kuusniemi, Cinzia Cappiello, Oliver Biehlmaier, Louise Wright, Chris Schubert, Andrea Bertino, Hannes Thiemann, Richard Dennis.

This document explains basic concepts to build a solid basis for a mutual understanding of data quality in a multidisciplinary environment such as EOSC. These range from the difference between quality control, assurance, and management to categories of quality dimensions, as well as typical approaches and workflows to curate and disseminate dataset quality information, minimum requirements, indicators, certification, and vocabulary. These concepts are explored considering the importance of evaluating resources carefully when deciding the sophistication of the quality assessments. Human resources, technology capabilities, and capacity-building plans constrain the design of sustainable solutions. Distilling the knowledge accumulated in this Task Force, we extracted cross-domain commonalities (each TF member brings his / her own experience and knowledge – we all represent different domains and therefore try to make our contributions domain-agnostic, but at the same time considering every nuance that our specialism can bring and what deserves to be heard by others), as well as lessons learned, and challenges.

The resulting main recommendations are:

  1. Data quality assessment needs standards to check data against; unfortunately, not all communities have agreed on standards, so EOSC should assist and push each community to agree on community standards to guarantee the FAIR exchange of research data. Although we extracted a few examples highlighting this gap, the current situation requires a more detailed and systematic evaluation in each community. Establishing a quality management function can help in this direction because the process can identify which standard already in use by some initiatives can be enforced as a general requirement for that community. We recommend that EOSC considers taking the opportunity to encourage communities to reach a consensus in using their standards.
  2. Data in EOSC need to be served with enough information for the user to understand how to read and correctly interpret the dataset, what restrictions are in place to use it, and what processes participate in its production. EOSC should ensure that the dataset is structured and documented in a way that can be (re)used and understood. Quality assessments in EOSC should not be concerned with checking the soundness of the data content. Aspects like uncertainty are also important to properly (re)use a dataset. Still, these aspects must be evaluated outside the EOSC ecosystem, which only checks that evidence about data content assessments is available. Following stakeholders’ expectations, we recommend that EOSC is equipped with essential data quality management, i.e., it should perform tasks like controlling the availability of basic metadata and documentation and performing basic metadata compliance checks. The EOSC quality management should not change data but point to deficiencies that the data provider or producer can address.
  3. Errors found by the curators or users need to be rectified by the data producer/provider. If not possible, errors need to be documented. Improving data quality as close to the source (i.e., producer or provider) as possible is highly recommended. Quality assessments conducted in EOSC should be shown first to the data provider to give a chance to improve the data and then to the users.
  4. User engagement is necessary to understand the user requirements (needs, expectations, etc.); it may or may not be part of a quality management function. Determining and evaluating stakeholder needs is not a one-time requirement but a continuous and collaborative part of the service delivery process.
  5. It is recommended to develop a proof-of-concept quality function performing basic quality assessments tailored to the EOSC needs (e.g., data reliability and usability). These assessments can also support rewarding research teams most committed to providing FAIR datasets. The proof-of-concept function cannot be a theoretical conceptualization of what is preferable in terms of quality. Still, it must be constrained by the reality of dealing with an enormous amount of data within a reasonable time and workforce.
  6. Data quality is a concern for all stakeholders, detailed further in this document. The quality assessments must be a multi-actor process between the data provider, EOSC, and users, potentially extended to other actors in the long run. The resulting content of quality assessments should be captured in structured, human- and machine-readable, and standard-based formats. Dataset information must be easily comparable across similar products, which calls for providing homogeneous quality information.
  7. A number of requirements valid for all datasets in EOSC (and beyond) and specific aspects of a maturity matrix gauging the maturity of a community when dealing with quality have been defined. Further refinement will be necessary for the future, and specific standards to follow will need to be identified.

We sincerely invite you to take a look at this very concise 76-pages long overview of the topic and look forward to your recommendations / suggestions / feedback – we hope to provide you with the opportunity to communicate the above conveniently very soon, so take your time to read, while we are making our last preparations 📖 🍷📖🍷📖🍷 But make sure you have a glass of wine at the time of reading it, as this will make sense at some point of reading, i.e. when we compare data quality with wine quality with reference to both flavour type and intensity (intrinsic quality), brand, packaging (extrinsic quality)… but no more teasers and bon appetite! 🍷🍷🍷
The document can be found in an Open Access here.

We also want to acknowledge the contribution and input of colleagues from several European institutions, the EOSC Association and several external-to-TF stakeholders who gave feedback based on their own experience, and the TF Support Officer Paola Ronzino, as well as to our colleagues – Sarah Stryeck and Raed Al-Zoubi, and the last but not the list – to all respondents and everyone involved.