Automation and integration are essential for streamlining operations, improving efficiency, and driving business growth. But poor data quality, whether inaccurate, incomplete, or inconsistent — can derail even the best-designed strategies, leading to inefficiencies, compliance risks, and missed opportunities.

Businesses must prioritize data quality to fully gain the benefits of automation and integration. This means taking a proactive approach that includes governance, cost-benefit analysis, operational best practices, and AI-driven technologies.

Previously, we covered the role of data quality in integration success. Here, we’ll break down key strategies for improving data quality, dispel common myths, and explore ways businesses achieve seamless, high-quality integrations.

1. Front-load data quality efforts in automation and operations

Data quality is a key factor in successful business automation. High-quality data enhances process efficiency, ensures accurate decisions, enables seamless integration, and reduces operational costs.
However, data quality issues often become apparent only during analysis, leading to reactive fixes in the “downstream.”

However, addressing these issues at this stage can be five to ten times more costly than preventing them “upstream” in the operational phase.

High-quality upstream data leads to smoother and more reliable downstream integration and analytics. To achieve this, organizations must invest time and resources upfront to establish robust governance, implement appropriate technologies, and adopt best practices.

Celigo provides automation and integration solutions that help maintain high data quality by:

• Synchronizing data across platforms to avoid duplication and inconsistency.
• Automating data validation and cleansing before data gets propagated into other systems.
• Providing real-time error handling to detect and correct issues early, reducing manual efforts.

Myth: View automation, integration, and data quality as siloed activities.

Many organizations pursue process automation and application integration to support their business objectives, only to discover that poor data quality becomes a significant obstacle to achieving their goals. Business teams, such as sales or finance, often bear the heavy burden of cleaning and reconciling data.

Consider the order-to-cash (O2C) process, which encompasses the entire lifecycle of receiving and fulfilling customer orders, invoicing, and payment collection. Data quality issues at any stage of this process can significantly hinder operational efficiency, reduce customer satisfaction, and impact revenue generation.

Read more about the disconnect between automation, integration, and data quality.

2. Assess the costs and benefits of data quality

The phrase, “You can’t manage what you don’t measure,” is especially applicable to data quality. Without assessing the impact of data quality, organizations risk falling into reactive data management, missing growth opportunities, and facing increased risks.

Forward-thinking organizations assess both the costs of poor-quality data and the benefits of high-quality data.

This proactive approach provides several advantages:

• Enhancing operational efficiency and accelerating automation.
• Increasing productivity and improving decision-making.
• Building a strong business case for initiating and sustaining long-term data quality and governance efforts.

To effectively measure the costs and benefits of data quality, IT and business leaders should collaborate on the following best practices:

• Identify a high-impact domain: Focus on a business area with significant data quality issues, e.g., sales, finance, or logistics.
• Select key business metrics: Choose a few key metrics for the identified business domains, e.g., sales pipeline value for finance and on-time order fulfillment rate for third-party logistics (3PL).
• Map to business metrics: Align data quality dimensions with the above business metrics to assess the positive and negative impacts of data quality on business. Avoid overly focusing on IT gains and costs.
• Implement actions and measure results: Business and IT teams take concrete steps to improve data quality and measure its benefits iteratively. Avoid the big-bang approach.

This proactive approach links data quality improvements to measurable financial and operational outcomes, securing executive buy-in and sustaining long-term efforts.

Myth: Higher data quality must always be better

Data quality initiatives require resources, but not all use cases require the highest level of quality assurance. For instance, a revenue prediction report may only need “good enough” data, while a financial report demands higher accuracy and reliability.

To avoid the trap of perfection, IT and business leaders can leverage data quality dimensions to assess, document, and measure the specific quality requirements of each use case. This ensures resources are used efficiently and effectively while meeting the needs of the use cases.

Myth: Out-of-box SaaS connectors always save money

Most SaaS platforms include embedded connectors, allowing for quick integration with other applications and data sources. While this approach may deliver quick wins in the short term, it often leads to unmanageable point-to-point integrations over time. These tightly coupled connections can result in higher long-term costs due to redundant work, production issues, suboptimal architectures, limited scalability, and challenges with governance and compliance.

To address these issues, IT and business leaders must define clear governance and architectural standards while assessing key requirements such as data privacy, business impact, and total cost of ownership.

3. Use data governance to support AI strategy and governance

Data governance is the overarching strategy and framework for managing an organization’s data assets. It defines the policies, standards, roles, and responsibilities needed to ensure effective data management. Data governance establishes guardrails and accountability for data quality. Learn more about the relationship between data quality and data governance.

Moreover, AI governance is key to an impactful AI strategy, as it mitigates risks, ensures data protection, promotes ethical use and maintains regulatory compliance. Data governance further supports AI by providing AI-ready data that continuously meets AI requirements, aligns with use cases, enables data qualification, and maintains governance standards.

Myth: Data quality is the responsibility of IT or business, but not both

IT teams often view data quality as the responsibility of their business counterparts, as they lack a deep understanding of the data’s content, context, and requirements. Conversely, business teams often perceive data quality as an IT responsibility since they typically do not own or manage complex technologies.

In reality, ensuring data quality requires collaboration between IT and business teams, as each has a partial view of the bigger picture. Strong teamwork in AI and data governance is essential for AI success.

4. Adopt AI to improve data quality and integration

Organizations often spend countless hours manually cleaning inaccurate customer data, inconsistent product information, and incorrect payment details. One organization even noted that they couldn’t hire enough data stewards to address data quality issues using traditional methodology, which heavily relies on data cleaning after the fact.

Celigo offers cutting-edge AI behind the scenes to proactively improve data quality and integration during operations with the following key features:

• Error classification: Categorizes data and system exceptions (e.g., value errors, rate limits) while supporting diverse APIs, including custom ones.
• Automatic recovery: Detects and resolves errors autonomously, minimizing downtime and repetitive manual work.
• Error management: Enables tagging, filtering, and sorting for efficient issue resolution and improved team coordination.
  Human-in-the-loop (HITL): Data stewards or owners can validate AI outputs when necessary, reducing risks and improving effectiveness.
• AI Knowledge Bot: Celigo’s AI Knowledge Bot combines the power of generative AI and auto-documentation of existing flows, which empowers business teams to build, manage, and monitor integrations with confidence.

By adopting Celigo’s AI, organizations can enjoy numerous benefits to improve data quality and integration:

• Improved accuracy and scalability: Celigo’s AI identifies data anomalies, inconsistencies, and errors with high precision, significantly reducing human errors. Additionally, it efficiently handles massive datasets, making it ideal for enterprises managing complex, large-scale data.
• Automation for cost efficiency: Celigo’s AI automates repetitive tasks such as error detection, validation, and correction, saving time and resources.
• Faster issue resolution and proactive monitoring: Celigo’s AI quickly categorizes and resolves errors (e.g., missing, duplicate, or invalid data) to ensure smoother operations.
• Support for complex integrations: Celigo’s AI recognizes and addresses errors across various APIs, systems, and custom workflows, ensuring end-to-end data integrity.

By combining AI’s capabilities with human oversight, organizations can validate critical outputs, minimize risks, and ensure compliance with governance policies.

Myth: specialized middleware alone can solve data quality problems

Many organizations turn to specialized data quality or master data management middleware in hopes of improving data quality. However, these middleware are often sold separately from integration tools, leading to a high total cost of ownership when factoring in expenses for procurement, implementation, maintenance, and the need for specialized skillsets. Worse yet, various types of middleware often require more integration.

A smarter approach to data quality and integration

For many organizations, specialized middleware can add unnecessary complexity without delivering proportional value. Celigo’s built-in data quality capabilities ensure both operational efficiency and analytical accuracy, allowing businesses to integrate applications and data seamlessly within their existing workflows.

Discover how Celigo simplifies data quality and governance.