The Pillars of Data Quality Management: A Guide to Mastering Them All

According to Gartner, poor data quality issues can generate an
additional $15 million in annual costs on average. In fact, it is not only
about financial losses, but also affects other levels, such as less reliable
analysis, poor governance and risk of non-compliance, loss of brand value,
slowing down corporate growth, etc.

For all these reasons, having quality data has become a
fundamental value for companies that want to continue to innovate and stand
out from the competition. We analyze its principles, best practices, and the
keys to avoid falling into poor-quality data.

Data quality refers to the degree of accuracy, consistency,
completeness, reliability, and relevance of data collected, stored, and used
in an organization or in a specific context.

High-quality data is essential for making informed
decisions, performing accurate analyses, and developing effective
strategies. It is also necessary to properly function other
technologies, such as artificial intelligence or IoT solutions.

Maintaining their high quality is a crucial factor for companies
to obtain valuable and correct information, make the best decisions, and
achieve their objectives. In fact, data quality has a direct influence on
operational efficiency, as it gives departments the accurate information they
need for day-to-day tasks, such as inventory management and order processing.
It also affects customer satisfaction and new business opportunities
by enabling more effective marketing and sales strategies
based on accurate customer segmentation and
targeting.

Data quality dimensions are critical aspects used to assess the
health and usability of each organization’s data. They provide a framework
for effectively identifying and correcting quality problems.

The most important dimensions are:

• Completeness: refers to whether a
  data set contains all the necessary records, as a complete data set allows
  for more comprehensive analysis and decision-making.
• Accuracy: refers to the degree to
  which the data accurately represents real-world values or events. To ensure
  accuracy, it is necessary to identify and correct errors in the data set,
  such as incorrect entries or misrepresentations. To improve it, data
  validation rules can be implemented to help prevent inaccurate information
  from being entered into the system.
• Consistency: represents whether the
  same information stored and used in multiple instances matches. It ensures
  that analyses correctly capture and leverage the value of the data. It is
  difficult to assess requires planned testing across multiple data sets, and
  is often associated with the accuracy of the data.
• Timeliness and topicality: these
  ensure that data is current and relevant when used for purposes such as
  analysis or decision-making. Outdated information can lead to incorrect
  conclusions, so it is essential to keep data sets up to date.
• Singularity: refers to the absence
  of duplicate entries in a data set. Duplicate entries can distort the
  analysis by overrepresenting specific data points or trends. The primary
  action taken to improve the uniqueness of a dataset is to identify and remove
  duplicates.
• Granularity and relevance: these two
  ensure that the level of detail in the dataset is fit for purpose. Too much
  granularity can lead to unnecessary complexity, while too little can render
  the data useless for specific analyses. Striking a balance between these two
  aspects ensures that you get relevant and actionable information from the
  data.

Both data quality and data governance are two indispensable
factors for companies wishing to become a data-driven enterprise. They may be
independent practices, but they are highly related.

In summary, you cannot have data quality
without good governance. In fact, organizations need proper
data governance before even considering an enterprise-scale data quality
tool.

Data governance affects security, privacy, accuracy,
compliance, roles and responsibilities, management,
integration, and so on. It is used for different tasks such as
increasing transparency around data, standardizing systems, policies, and
procedures, solving problems, and ensuring regulatory and organizational
compliance.

All these tasks are necessary to improve and monitor data quality,
as good governance allows creators and users to work on the same platform,
which enables better communication and shared understanding of data
quality.

Although the data may need a massive overhaul to improve its
quality, this experience can be leveraged to adjust data
governance policies and procedures to incorporate new data.
Thus, using this overlapping perspective is the most useful when designing
joint strategies on data governance and data quality.

To achieve successful incorporation of both practices, data teams
must ask themselves questions (Where to start? Which data to focus on? Which
data may be out of scope? Which has the greatest business impact?) from two
different angles:

• Critical data elements: identify
  what is critical to the business, either through a regulatory report, a KPI,
  etc.
• Value of data: calculate the
  lifetime of poor-quality data or the risk associated with it, focusing first
  on those areas with the highest risk.

In both cases, once organizations identify and prioritize areas of
concern, they can use data governance to create a collaborative framework for
managing and defining policies, business rules and assets to provide the
necessary level of data quality control.

Once it is clear how data flows through the organization and what
the standards are, it is easier to ask the data quality team to translate
these standards into data quality rules and enforce them on the data in those
systems.

To maintain and improve data quality, it is necessary to
incorporate techniques and best practices into daily data management
routines.

The most effective techniques include:

• Data profiling: review existing data
  to detect anomalies, patterns, or inconsistencies.
• Standardization: applying uniform
  formats across all data sets.
• Cleaning: correcting or removing
  inaccurate, incomplete, or irrelevant data records.
• Data enrichment: enhancing data from
  internal and external sources for greater context and value.

Regarding best practices:

• Periodic data quality assessments to proactively detect and
  address problems.
• Clear business rules that guide data inputs and avoid common data
  errors.
• Hire experts as data analysts who can use advanced analytics
  tools.
• Zero-defect data approach to achieve data quality that borders on
  perfection.

Establishing data quality standards is essential to ensure
consistency and accountability in your organization’s data. Some of the
principles of data quality management are as follows:

1. Focus on business needs: The primary
   focus of data quality is to meet the requirements of the data quality
   dimensions according to business needs.
2. Leadership: It is important that
   leaders from all departments align on a common set of strategies, policies,
   processes, and resources.
3. Stakeholder engagement: Data quality
   is everyone’s responsibility and to achieve this, all employees must work
   within a framework where they can raise issues that cause poor data quality
   and have clear ways to address and prevent them.
4. Process-based approach: A
   comprehensive and successful data quality and management program must take
   into account all business and technical processes that acquire, produce,
   maintain, transform, or disseminate data. Understanding how they interact
   with each other and what results they produce will be key to optimizing the
   data ecosystem.
5. Continuous improvement: data
   management should be understood as a program that needs to be continually
   re-evaluated and adapted to keep up with internal and external
   conditions.
6. Data-driven decision-making:
   Decision-making can be challenging, but with useful data, facts, evidence,
   and reliable analysis, more objective decisions can be made.
7. Relationship management: Data
   quality management not only encompasses internal stakeholders but also
   extends to data management tool providers, suppliers, and consumers.

These data quality management principles can be applied in many
different ways. As such, how each organization implements them will depend on
the specific nature and challenges they face. What is common to all is that
they will find many benefits in establishing a management program based on
these principles.

A data quality framework provides a structured approach to
managing and improving data quality across all business
operations. It ensures that data is accurate, complete,
and reliable.

To create a data quality framework, you will need to consider
aspects such as:

• Define roles and responsibilities
• Establish data quality rules
• Periodic evaluations

This framework must be adaptable to changing business needs while
remaining robust to the challenges posed by new types of data and emerging
technologies.

Implementing a comprehensive data quality framework ensures a
reliable foundation for your information systems, fostering confidence in
your data and the decisions derived from it. That’s why at Plain Concepts we
offer you a Data Adoption Framework to become a data-driven
enterprise.

We help you discover how to get value from your data, control and
analyze all your data sources, and use data to make smart decisions and
accelerate your business:

• Data analytics and strategy
  assessment: we evaluate data technology for architecture
  synthesis and implementation planning.
• Modern analytics and data warehouse
  assessment: we provide you with a clear view of the modern
  data warehousing model through understanding best practices on how to prepare
  data for analysis.
• Exploratory data analysis
  assessment: we look at the data before making assumptions so
  you get a better understanding of the available data sets.
• Digital Twin Accelerator and Smart
  Factory: we create a framework to deliver integrated digital
  twin manufacturing and supply chain solutions in the cloud.

We will formalize the strategy that best suits you and its
subsequent technological implementation. Our advanced analysis services will
help you unleash the full potential of your data and turn it into actionable
information, identifying patterns and trends that can condition your
decisions and boost your business.

Extract the full potential of your data now!