How Data Quality Management restores control

marianfaryna
23 вер.
Читати 7 хв

In many organizations, "data" exists but doesn't work. It accumulates, duplicates, loses relevance, and creates chaos instead of value. Why is this the case? The core issue isn't a lack of technology but a lack of clear rules regarding responsibility, reconciliation, and what constitutes "good enough" data for decision-making. This article is about how to build such a system without unnecessary magic, relying on common sense, structure, and consistency.

When data becomes noise

In the modern world, where information is a key asset, the volume of data flowing into organizations is growing exponentially. Strategic decisions, marketing campaigns, financial forecasts, and operational efficiency depend on these datasets. However, data accumulation alone doesn't guarantee success. If data is "dirty," incomplete, or outdated, it loses its value and can cause significant damage. According to DAMA-DMBOK®, this damage can reach up to 30% of a company's total revenue.

Imagine a ship navigating a stormy sea. Its course depends on the accuracy of its maps, the reliability of its compass, and the clarity of its GPS signals. If the maps are outdated, the compass points the wrong way, and signals are distorted, the ship will likely not reach its destination and might even crash into rocks. In the business world, such "rocks" are incorrect decisions, lost customer loyalty, and operational failures, which are direct consequences of poor data quality.

This problem is particularly acute in large, sprawling structures, such as the public sector, where, as the author's experience shows, a lack of trust in data is chronic. In conditions where there is no single "source of truth" due to opaque processes and dispersed information, any attempt to compile a consolidated report turns into complete chaos. Disparate Excel files, "living" their own lives, multiply duplicates and inconsistencies, depriving the organization of the ability to see the complete picture.

From Chaos to Control

A Data Quality Management System (DQMS) is not just a set of tools, but, first and foremost, an integrated framework of solutions and an operational discipline designed to ensure that data is fit for its purpose. It is a systematic approach that encompasses all actions and decisions related to quality objectives, as well as the processes and resources for achieving them.

Data quality is defined by a set of properties that reflect the degree to which information is suitable for achieving user objectives. Key data quality indicators that need to be monitored include:

Accuracy: Does the data reflect the real state of affairs?
Completeness: Is all necessary data present? Are there no empty values indicating missing information?
Consistency: Does data from different sources contradict each other?
Timeliness: Is the data current for the present moment? Outdated data can lose its relevance.
Validity: Does the information comply with defined rules and formats?
Uniqueness: Are there no duplicate records?

Implementing a DQMS involves more than just technical tasks. It also transforms organizational culture, making data quality a common goal for the entire company and holding management directly responsible for it.

Architecture of reliability

An effective data quality management system is built on three main "pillars": people, processes, and technology.

People and Roles: Data does not exist in a vacuum; it is created, processed, and used by people. Clear definition of roles and responsibilities is critically important. This includes:

Data Owners: individuals or departments ultimately responsible for the quality of specific datasets.
Data Stewards: specialists who work directly with data, ensuring its quality and adherence to defined standards.
Data Users: those who make decisions based on data, and who must trust its quality.

Building an effective role model often requires adaptation. In the author's experience, a centralized mixed approach was successful. In this approach, business unit heads are designated as data owners, and data stewards are representatives of the analytics and data management department. However, potential "owners" often have natural apprehension about responsibility in the initial stages because they do not always understand data processes or trust the quality of the information.

To overcome this resistance, it is important to demonstrate the direct relationship between data quality and operational work. For instance, an error in the delivery date of goods under a contract in a supply chain agency will inevitably lead to disruptions in contract execution at subsequent stages. Such an error "surfaces," preventing subsequent departments from doing their job. In such situations, data management specialists must elegantly and accurately demonstrate that the error did not originate from the system or dashboard and that the data governance unit is not responsible for it. Rather, it arose from the actions of specific people in a specific process. It is important to emphasize that there should be no shaming for errors, only understanding and help from experts. This approach fosters an atmosphere of trust and encourages collaborative work on quality.

Processes: Data quality management is a cyclical and continuous process. It includes the following key stages:

Data Profiling: Identifying existing data quality issues, such as missing values, duplicates, and incorrect formats.
Data Cleansing: Correcting identified errors, standardization, and removing duplicates. This allows data to be aligned with company requirements and standardization rules.
Data Quality Monitoring: Continuous oversight and tracking of data quality indicators using automated tools and KPIs.
Data Validation and Enrichment: Verifying data compliance with established rules and adding additional information to increase its value.
Data Reconciliation: Ensuring consistency of data across different systems and sources, often through Master Data Management (MDM) systems.

These quality control procedures are applied at every step of working with data, from its collection to analysis.

Technologies: While technology is not a panacea, it is a powerful catalyst for an effective DQMS. Tools for profiling, cleansing, monitoring, and integrating data automate routine processes, allowing for quick responses to potential threats and errors. However, it is important to remember: technology only executes the rules set by people. A tool will not make your data quality. Only you can make your data quality. Even without the ability to use ready-made commercial solutions, organizations can achieve significant success. In such cases, as in the author's experience, one can create their own solutions, for example, a system for orchestrating business rule checks on data. Such custom-built systems allow for flexible adaptation to unique organizationals needs, and later, when processes become stable, they can be significantly improved, evolving into full-fledged data quality metadata management systems that 100% cover the assigned tasks. This confirms that the primary factor is the mindset of people and the organization's culture, and only lastly – the specific tool.

Quality as the foundation of success

When data becomes reliable, it has deep and far-reaching consequences for the organization. First, information asymmetry disappears. This occurs when some departments have certain data while others have different data, which leads to internal contradictions and inefficiency. This creates an opportunity to make decisions based on accurate, consistent, and timely data, which minimizes risks and increases the likelihood of success.

In practice, this significantly improves trust in data within the organization. When managers and employees see that reports are generated from a reliable, centralized repository rather than disparate Excel files, they begin to trust this data. This opens the way for reporting automation and a transition to self-service Business Intelligence (BI) systems, which significantly saves time and resources.

Implementing an effective DQMS leads to:

Reduced Costs: Eliminating duplicates, correcting errors, and preventing their recurrence reduces operational costs associated with rework and incorrect actions.
Increased Efficiency: Consistent and high-quality data optimizes internal processes, increases productivity, and accelerates business operations.
Increased Trust and Loyalty: Trust in data extends to trust in the company's services and products, increasing the loyalty of customers and business partners.
Increased Competitiveness: Companies that rely on quality data adapt faster to market changes, more accurately forecast trends, and more effectively build communication strategies.
Regulatory Compliance: Many industries have strict regulatory requirements for data, and a DQMS helps meet them.

A data quality management system is not just about control, but also about continuous improvement, innovation, and the organization's ability to respond to external challenges. It involves all production resources and all personnel, regular checks, and training.

Challenges on the Path

The implementation of a DQMS, like any significant systemic change, faces challenges. Often these are not technical difficulties, but rather organizational ones:

Insufficient clarity of goals: Without a clear understanding of what the enterprise aims to achieve with quality data, efforts can be scattered.
Resistance to change: People get used to certain ways of working. Changing approaches to data requires training, persuasion, and leadership.
Underestimation of cultural Importance: If data quality is perceived as a purely technical task, rather than a shared responsibility, the system is doomed to fragmentation.
Financial and organizational costs: Implementing and maintaining a DQMS requires investment of time and resources, which sometimes becomes an obstacle for small organizations.

However, these challenges are not insurmountable. They require consistent, disciplined work, clear strategies, and a willingness for continuous improvement.

The paradox of trust and culture

Implementing a data quality management system is a marathon, not a sprint, and its success is determined not so much by tools as by the organization's ability to adapt. The main lesson that emerges from practical experience: the most difficult but also the most important task is to demonstrate the first victories that help break the paradigm of thinking and people's beliefs. When data begins to be centrally used for analytics, and data domains are linked into a single structure, people gradually begin to trust the new system, moving from "disparate Excels" to certified datasets and BI analytics.

This process proves that the decisive factor is not a specific tool or technology, but people and the organization's culture. A culture that does not punish errors but uses them as a catalyst for learning and improvement is the foundation for successful data quality management.

Understand exactly where in the process an error occurred, show it, but never blame. Genuine trust in data can only be built through understanding and collaborative work on eliminating causes, not just consequences. This trust will become the cornerstone for making informed and effective decisions.

Instead of an epilogue

A data quality management system is not an additional luxury, but a fundamental building block for any organization that seeks to make informed decisions and achieve sustainable success. It's not about "trends" or "revolutions," but rather, it's about common sense, structure, and discipline. When implemented correctly, this "framework of solutions" transforms data from an adrift entity into a reliable compass that points the way to the goal, even in the most complex conditions.