Data Quality Management: The Key to Digital Transformation Success

To successfully embrace digital transformation, businesses must fundamentally rethink their use of technology across all aspects of operations. This journey involves significant changes to hardware, software, operational practices, and company culture. At the heart of this transformation is the need for high-quality data, which is crucial for driving innovation, efficiency, and strategic decision-making. As companies increasingly adopt advanced technologies like artificial intelligence (AI), machine learning (ML), and cloud computing, the reliability of their data becomes essential for leveraging these tools effectively.

Data Quality Management (DQM) is crucial for ensuring that the data driving these technologies is accurate and reliable. Effective Data quality management supports detailed data analysis, which provides the insights necessary for strategic decision-making and successful digital transformation. This blog explores how robust data quality management can help you successfully digitally transform your business.

Quick Takeaways

• Digital transformation is the use of digital technology to improve business operations.
• Digital transformation depends on accurate data, which is possible only via data quality management solutions.
• Data quality management helps strategize and optimize digital transformation.
• Data quality management is also important when moving from data silos to an integrated data repository.
• Data quality management helps management make more informed decisions and drives business growth.

The Foundation of Digital Transformation Success – High-Quality Data

Digital transformation is a fundamental transformation for businesses, leveraging digital technologies to unlock new opportunities and increase operational efficiency. This process often involves converting physical data to digital form, automating manual tasks, and integrating advanced technologies such as artificial intelligence (AI) to modernize business functions.

The key to this transformation will be the integration of new technologies, business continuity and the creation of a data-driven culture. However, poor data quality can severely hinder these efforts, resulting in inefficiencies, faulty procedures, and operational challenges. Real-world examples show how poor data quality has led to catastrophic failures in digital transformation, highlighting the critical role that high-quality data plays in driving successful outcomes.

Key Reasons Companies are Adopting Digital Transformation

According to TEKsystems’ 2020 State of Digital Transformation report, the main reasons include:

• Improve customer engagement (72%)
• Increase operational efficiency (48%)
• Upgrade or replace legacy systems (47%)
• Increase speed to market (29%) 

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This explains why Deloitte found that two-thirds of business executives believe that organizations that don’t embrace digital transformation are “doomed” to marketplace failure. These executives plan on spending, on average, 0.6% of their annual revenue on digital transformation efforts – 15% more than in the prior year.

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How Data Quality Management Enables Digital Transformation?

There are several key ways in that effective data quality management leads to digital transformation success. It’s all about providing the high-quality data your business and your digital transformation efforts need. 

Helps Strategize the Digital Transformation

High-quality data helps management make more informed decisions – including decisions regarding digital transformation strategy. Informed data analysis helps identify where an organization is in its transformation cycle, where it wants to be, and how it needs to get there. 

The first step in any digital transformation is assessing the current state of an organization’s digital efforts. You need to know what data you have, the quality of that data, where it’s stored, and who needs to access it. Robust data quality management supplies that information and ensures that the data you have isn’t infested with incomplete or low-quality information. 

Enables the Move from Data Silos to Integrated Data Usage

Most organizations find that digital transformation is hampered by their legacy data quality management systems. In particular, it’s common for essential data to be spread across an entire organization in departmental data silos. You might have separate silos for data in your marketing, sales, and finance departments, or in branches in different locations across the country. The situation becomes more challenging when you try to integrate data from merged or acquired companies, each of which will have their own way of doing things. 

All of this siloed data makes it difficult to know what data your organization owns and almost impossible to share that data across departments and locations. It also makes it difficult to ensure the integrity of that data.

State-of-the-art data quality management helps organizations make the move from data silos to integrated data systems. This transition requires not only cleaning the data in each silo but also standardizing it so it can be consolidated into a single data repository. Unstructured data needs to be converted into the proper structure and individual structures need to be standardized into a single structure. And, in the process, incomplete data needs to be fixed or deleted and bad outright data needs to be purged.

With proper data quality management, data can be integrated into a trusted single source that is accurate and reliable. This single source of knowledge makes it easier to share essential data across the entire organization and to make more informed decisions about the ongoing digital transformation. 

Reinvents Essential Operations

Businesses of all types depend on data to manage their essential operations, especially as those operations are digitally transformed. Data quality management is key to providing this data quickly and accurately. 

Digitally transformed operations are data-driven operations. Data helps inform the analysis that can revolutionize and automate formerly manual processes, and keep those processes running smoothly with little or no human intervention. 

Optimizes Digital Transformation Efforts

Few digital transformations go 100% smoothly. There are always bumps in the road and unexpected obstacles that need to be dealt with. 

These challenges are best met through a continuing inflow of high-quality data. Real-time data analysis during the transformation can detect gaps in strategy and missteps in execution, enabling management to recalibrate towards a more successful conclusion. 

Enables Faster and More Informed Decision Making

Robust data quality management provides the accurate and up-to-date data that management needs to make better decisions. These decisions drive digital transformation, long-term strategic planning, and decisions regarding day-to-day operations. Without accurate data, decisions can be ill-informed and off-target. High-quality data results in higher-quality decisions by all involved.

Supercharges Business Growth

Finally, data quality management is essential for the continued growth of your business. Complete, accurate, and timely data is a competitive advantage in every industry. It enables management to react faster to market changes, better respond to customer needs, and fine-tune ongoing operations – all of which help to drive business growth. It’s ultimately what digital transformation is all about – driven by data quality management.

Common Challenges in Data Quality During Digital Transformation

Breaking Down Data Silos: Disparate datasets across different departments create inconsistencies, making it difficult to consolidate and maintain a single source of truth.

Overcoming the Challenges of Legacy Systems: Older systems often lack the capability to meet modern data quality standards, complicating data integration and hindering transformation efforts.

Lack of Data Governance: Without a strong data governance framework, inconsistent practices and unclear data ownership can derail the success of digital transformation initiatives.

Let DataBuck Help Drive Your Organization’s Digital Transformation

When you need high-quality data to drive your digital transformation efforts, turn to DataBuck from FirstEigen. DataBuck is an autonomous data quality management solution that uses AI and ML technology to automate more than 70% of the data monitoring process. Our data management system ensures that the data that drives your digital transformation is as complete, accurate, and timely as possible.

Contact FirstEigen today to learn how DataBuck can tie into your company’s digital transformation. 

Check out these articles on Data Trustability, Observability & Data Quality Management-

• 6 Key Data Quality Metrics You Should Be Tracking
• How to Scale Your Data Quality Operations with AI and ML
• 12 Things You Can Do to Improve Data Quality
• How to Ensure Data Integrity During Cloud Migrations