Why digital transformation is impossible without quality data

Introduction: data as the foundation of transformation

5.8.2025 Digital transformation is impossible without reliable, high-quality data. This article explains how poor data slows business growth, why technology delivers no results without information management, and which metrics and practices help improve data quality for successful digital transformation.

Reading time: 11 min

87.5% of digital transformation projects fail. The main reason is poor data.

Organizations invest in cloud platforms, automation, and machine learning but see no return because they rely on fragmented, outdated, and unreliable data.

Without a reliable data foundation, technology does not work for the business.

Digital transformation is not just automation.

It is a rethink of business logic and the operating model, a shift to working with real-time digital data and to fact-based decision-making.

But for data to truly drive transformation, it must be systematically collected, verified, cleaned, and used.

Why Data Is the Foundation of Digital Transformation

Transformation starts not with technology, but with information.
Without quality data: analytics accuracy drops; forecasts and strategic decisions become flawed; customers receive irrelevant offers; resources are allocated inefficiently.
The key to transformation is integrating new technologies, ensuring business continuity, and building a data-driven culture.
Data is both the fuel and the compass for transformation.
Digital transformation requires accurate, complete, consistent and up-to-date data from multiple sources: CRM, ERP, logistics, marketing and sales systems.
The problem is that many companies use fragmented, incompatible systems.
This complicates integration, hampers analysis and distorts information.
Gartner estimates that 20% of all data companies work with is of poor quality.
An Experian study found that 55% of company executives do not trust the data they work with daily.
These leaders argue that poor-quality data wastes resources because it causes losses; reduces analytics quality; lowers customer service quality; slows order fulfillment; hinders compliance with government and industry regulations; and slows digital transformation.

Examples

: Send a marketing campaign to an outdated database and the result is undelivered emails, lost leads and lower ROI. Make an error in a customer's address and the sale ends in failed delivery, a customer complaint and falling loyalty.

Comparing high-quality and low-quality data

Parameter	High-quality data	Low-quality data
Accuracy	Accurate and reflect the real situation	Contains errors, typos, or misleading information
Completeness	Present and fully accessible	Contain missing or duplicate elements
Consistency	Comply with rules and standards, helping avoid inconsistencies	Contradict each other or established rules
Reliability	Trustworthy and verifiable	Questionable and not verified
Relevance	Relevant and aligned with current needs	Outdated
Relevance	Match the specific task or analysis being performed	Not suitable for the task at hand
Availability	Easy to access and extract	Difficult to access or extract

How data drives transformation failure

Poor data quality slows growth and leads to inefficiency, disruptions, and operational problems.
There are several reasons why data slows digital initiatives: low data quality.
Transformation relies on data from fragmented systems, including legacy ones.
Low-quality data, whether inaccurate, incomplete, or inconsistent, leads to flawed analysis and decision-making, wasted resources, frustrated customers, and lost sales.
The lack of a data strategy.
Without a clear strategy, an organization cannot properly collect, manage and use data.
This leads to fragmented and inefficient use of data and a lack of alignment between data initiatives and business goals.
Even when data is analyzed, companies struggle to turn the results into concrete actions that increase business value. Inadequate

Data management

. During digital transformation a vast amount of diverse data is generated quickly.

Organizations struggle to manage data: they cannot properly collect, sort, store, and analyze it. As a result, they gain no meaningful insights and fail to spot trends, patterns, and opportunities.

Another aspect of the problem is focusing on data collection rather than data use. Security issues.

For stable operations and customer trust, a company must effectively protect confidential data.

According to a Fujitsu survey, 74% of retailers see security and privacy as a key transformation challenge.

Inadequate security measures expose organizations to cyber threats and regulatory penalties.

Ignoring the human factor.

Transformation requires changes in organizational culture and employee behavior.

If employees do not know how to work in the new systems and understand what transformation means for them, they will resist change.

Without a culture that values data and encourages data-driven decisions, you cannot embed data analytics into daily operations.

Focusing on technology rather than business needs

Management may not have enough data on how new technologies solve specific business problems or improve customer service quality.

This leads to wasted resources, failed implementations and no return on investment.

Data quality metrics: an overview

Data quality metrics are standardized indicators that assess accuracy, consistency, and reliability. They reflect the state of the data and help teams identify and resolve issues that can affect business operations. By tracking data quality metrics, companies ensure their data is reliable and fit for purpose, make more informed decisions, and improve operational efficiency.

Data management

helps ensure its integrity and security.
To do this, you define and implement policies, quality standards and procedures for collecting, owning, storing, processing and using data.
Data quality dimensions are points of potential inefficiency: inaccuracy reduces marketing quality, incompleteness hinders analytics, and inconsistency distorts forecasts.
They help organizations assess progress toward the standards set within management practices. These include: Accuracy.
High-quality data accurately reflects real phenomena and events.
Accuracy yields trustworthy information and improves decision-making. Completeness. Ensures all relevant data is available, so analysis has no gaps.
This enables a deep understanding of the situation. Consistency.
Ensures consistency across data sets or indicators.
Consistent data is free of contradictions, so it does not threaten reliability or interpretability. Timeliness. Means facts are presented to the right audience, in the right format, at the right time.
This enables optimal decisions and timely responses to changing conditions. Accuracy.
Reliable data correctly reflects the real situation and is always available, which increases trust in it. Uniqueness.
Each unique data point reflects a distinct object or event, providing a single source of reliable information.
It is important for resolving discrepancies and preserving data reliability.

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Data observability

is the practice of monitoring and managing data.

It ensures their quality, availability, and reliability

At its core is tracking data quality metrics that explain how they are measured, quantitatively or qualitatively.

Observability practice tracks the following metrics: data-to-error ratio.

To calculate this ratio, measure the number of known data errors in the dataset and compare it to the total dataset size.

When errors decrease while the data volume remains constant or increases, data quality improves. Number of empty values.

Empty values indicate that important information is missing or was entered into the wrong field. Data transformation errors.

Problems converting data from one format to another indicate quality issues. Volume of dark data.

Dark data is collected and stored by a company but never used.

Large volumes of dark data signal data quality problems, since no one bothers to examine it.

Many companies do not fully realize the potential value of the data they hold.

To use this data, it must be extracted and its correctness, consistency, and completeness assessed. Data storage costs.

If data storage costs rise while the volume of data in use stays the same, some of the data is poor quality.

Time to value from data.

A high number of errors during data conversion or a need for manual cleansing points to low data quality.

The faster a team turns data into business value, the higher the data quality.

Email bounce rates.

Sales and marketing can only succeed with a high-quality email address list.

Data on current and potential customers can degrade quickly, reducing dataset quality and campaign effectiveness. Percentage of duplicate records.

Duplicate records appear due to data-entry errors, system issues or other causes.

Their number reflects the quality of data management. Data update delays.

Irregular data updates lead to decisions based on outdated information.

This metric helps keep data current and relevant. Data pipeline incidents.

Data pipelines are systems that collect, process, and transfer data from one place to another.

Monitoring the number of incidents in pipelines, such as failures or data loss, helps teams identify points where data integrity may be compromised. Table availability.

This is an aggregate indicator that measures the overall health of a database table.

It includes the number of missing values, data range, and record integrity in a table, metrics that provide a comprehensive assessment of data quality for individual datasets.

Data quality management: methods and processes

Data quality management is a set of methods for improving and maintaining an organization's data quality.
The key management method is data profiling: analyzing structure and content to assess quality and establish a baseline.
Remediation measures will be assessed against this baseline.
Data quality is assessed by parameters and metrics.
If data is poor quality, it can be cleansed: fixing errors and inconsistencies in the source datasets.
After cleaning, data can be transformed into a format suitable for analysis.

Building a data-driven strategy

Data analysis helps determine where an organization stands in the transformation cycle, what it wants to achieve and how to get there.

The first step in digital transformation is assessing the current state of an organization's digital processes.

A company needs to know what data it has, what its quality is, where it is stored, and who needs access to it.

Assessing the current state makes it possible to: gather information to develop a tailored transformation strategy that matches the organization's specific needs and goals; identify bottlenecks, inefficiencies, and areas where digital technologies can have the greatest impact; allocate resources effectively and make budget, staffing, and technology investments; identify areas where resistance to change may arise and apply proactive strategies to address it; set realistic and

achievable transformation goals; measure the success of transformation initiatives against a baseline and make the necessary adjustments.

Moving from data silos to integrated use

In 64% of organizations, digital transformation is hampered by outdated data quality management systems.
Important data is often scattered across separate repositories in different departments, such as marketing, sales, and finance, as well as in branch offices. This fragmentation makes it difficult to ensure access to data, verify integrity, and perform analytics.
Data quality management technologies help organizations move from fragmented silos to integrated data systems.
To make the transition, data in each repository must be cleaned and standardized: unstructured data should be converted into the required structure; separate structures should be standardized into one; incomplete data should be corrected or removed; incorrect data should be removed.
With proper data quality management, data can be integrated into a single, reliable source of knowledge.
It gives a holistic view of the business, a single context, and synchronized processes, simplifies the exchange of important data, and enables better-informed decisions.

Rethinking operations and optimizing transformation

Rethinking core operations: Companies need data to manage their core operations.

Data quality management

makes it possible to collect and process this data quickly and accurately. Digital operations are data-driven operations. Data helps perform analysis to change and automate manual processes. Automation keeps processes running smoothly with minimal human intervention or none at all.

Optimizing digital transformation efforts

: An Everest Group study found that 73% of companies gain no business value from their digital transformation efforts because they lack a clear strategy or goals. A steady flow of high-quality data helps solve this and other challenges that arise during transformation. Real-time data analysis exposes gaps in strategy and errors in its execution, bottlenecks and business-process inefficiencies.

It also reveals potential risks, such as fraud or security threats. This way, management can regularly adjust strategy to take preventive measures that reduce risk and improve transformation outcomes.

Faster, better-informed decision-making

: With reliable data quality management, company leadership gets accurate, current information and can make informed decisions. Such decisions underpin daily operations, long-term strategic planning, resource allocation and future investment. Real-time data monitoring lets companies track progress, identify areas for improvement and adapt to changing market conditions.

Analyzing historical data helps forecast future trends and optimize resource allocation.

Accelerating business growth

: Complete, accurate, and up-to-date data are a competitive advantage in any industry. They enable leadership to respond faster to market changes, better understand customer needs, and optimize current operations. As a result, the business grows steadily, develops sustainably, and is easy to scale.

The role of data quality management in digital transformation success

Digital data transformation is tightly linked to quality management. It:

Lays the foundation of trust within the organization.

Governance sets the rules and guidelines for accessing, using and managing data.

When everyone understands these parameters, there is less uncertainty.

This trust extends to confidence in high data quality and secure data management, supporting more informed and reliable decisions.

Designed around the organization's specific needs and goals.

Every company has its own unique culture and goals, which require a tailored data management plan.

This ensures that digital transformation efforts align with strategic goals. Minimizes complexity.

When team members have clear instructions, they are less likely to improvise or make arbitrary decisions about data.

Consistency improves the overall quality and reliability of the analytics that underpin transformative business decisions.

Expands the organization's ability to use data and analytics.

Established rules and processes ensure data is handled correctly from creation to archiving or destruction.

Comprehensive lifecycle management is critical for adapting to a constantly changing digital landscape.

Digital transformation is impossible without a data quality management system.

It is not a support function, but the core of change

Companies that build a data-driven culture gain not only a technological but also a strategic advantage.

For new platforms and solutions to deliver results, you need to start with the basics: clean, current, accessible, and useful data.

Digital transformation starts with data management: how information quality affects business results

Introduction: data as the foundation of transformation

Reading time: 11 min

Why Data Is the Foundation of Digital Transformation

Examples

Comparing high-quality and low-quality data

How data drives transformation failure

Data management

Focusing on technology rather than business needs

Data quality metrics: an overview

Data management

Assess where AI can deliver impact in your process

Data observability

It ensures their quality, availability, and reliability

Data quality management: methods and processes

Building a data-driven strategy

Moving from data silos to integrated use

Rethinking operations and optimizing transformation

Data quality management

Optimizing digital transformation efforts

Faster, better-informed decision-making

Accelerating business growth

The role of data quality management in digital transformation success

It is not a support function, but the core of change

Discuss the article: Digital transformation starts with...

Digital transformation starts with data management: how information quality affects business results

Introduction: data as the foundation of transformation

Reading time: 11 min

Why Data Is the Foundation of Digital Transformation

Examples

Comparing high-quality and low-quality data

How data drives transformation failure

Data management

Focusing on technology rather than business needs

Data quality metrics: an overview

Data management

Assess where AI can deliver impact in your process

Data observability

It ensures their quality, availability, and reliability

Data quality management: methods and processes

Building a data-driven strategy

Moving from data silos to integrated use

Rethinking operations and optimizing transformation

Data quality management

Optimizing digital transformation efforts

Faster, better-informed decision-making

Accelerating business growth

The role of data quality management in digital transformation success

It is not a support function, but the core of change

Discuss the article: Digital transformation starts with...

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