How To Align Data Integration and Data Quality

Imagine a beautiful piece of furniture crafted from rotten wood or a high-fashion shirt made with poor-quality fabric. The quality of the material affects the final product. So why would data insights, the main product of your company’s vast data management efforts, be any different? 

It doesn’t matter how powerful your data management ecosystem is or how advanced your data integration, analytics, and visualization tools are. The ultimate quality of your business insights is rooted in the quality of the raw data used to generate them.

The term "quality" alludes not just to accuracy but also to consistency, completeness, conformity, and integrity. When a dataset is high quality, you can more easily process and analyze it to create business value. High-quality data creates a virtuous cycle. When users trust your data, they use it more and get better results. Subsequently, it creates a stronger data culture in your organization. 

On the flip side is low or unknown data quality, which is far from benign. Bad data can result in a vicious cycle that includes inaccurate analytics, ill-informed decisions, significant financial or reputational damage, and an eroded data culture.

Who Is Responsible for Data Quality?

Good data is on everyone’s wishlist. But where does the responsibility lie regarding ensuring high-quality data across the data management ecosystem? There are three key stakeholders in the journey from raw data to finished business insights: data producers, data integrators, and data consumers. However, because the journey gets complex and often lacks transparency, these stakeholders tend to focus only on their own puzzle pieces. This means data quality, which concerns everyone, often becomes the responsibility of no one. 

Even specially-appointed data stewards would not make headway without the active participation of the following three stakeholder groups that work hands-on with the data.

Data Producers

At most enterprises, data flows in petabytes from the everyday business operations of sales, marketing, finance, manufacturing, and customer service. IoT devices, edge computing, and third-party sources also contribute data in an ever-expanding range of formats. 

Data producers, who have a deep understanding of the data they collect, should mindfully collect data with real business value rather than dumping all the data they generate into analytics. The bottom line is that data collection, storage, and processing carry security and cost implications. Clearly defined data fields and qualifiers help keep your data relevant and timely for use downstream.

Data Integrators

Data engineers play a significant role in transforming raw data into business insights. In many organizations, the responsibility for data quality lands with you as the creators and owners of the pipelines that move and transform data.

While you are adept at handling data, you may lack a deep understanding of the data itself. That can lead to challenges in data quality management. For example, while a data consumer may know that a particular field can never be a negative value, you may not. Documentation of data quality rules that define how and when they apply at each step of the data journey would help you drive more consistent outcomes.

Data Consumers

Business users — like sales, marketing operations teams, and data analysts — want trusted, business-ready data and insights. When they can observe where data is being combined, changed, or transformed for quality purposes along with the formats, sources, and workflows that impact data, they feel more confident in the analytics and insights. 

However, they are not as technically sound as data engineers — which means self-serve options need to be user-friendly and intuitive for them to readily implement.

3 Ground Rules to Fix Data Quality for Good

For most companies, data tool sprawl is already a challenge. Add to that poor-quality data, and you have the recipe to keep expensive engineering resources in constant fire-fighting mode instead of focusing on strategic work. In fact, 41% of CDOs say they must improve the quality of their data to support data strategy priorities.

With most modern organizations operating in a hybrid, multi-cloud environment and moving towards an AI-powered data stack, there is an urgent need for clean, high-quality data in the data management ecosystem. Without this, generative AI and large language model (LLM)-managed services cannot improve outcomes. 

Here are three ground rules to move permanently from the ‘garbage-in, garbage-out’ (GI-GO) mode to the ‘quality in-quality-out’ (QI-QO) mode.

1. Build a Strong Data Quality Foundation

Data quality is not something you can make up or improve as you go along. The mandate for high-quality data needs to be baked into the data management foundations of your business. This includes:

• Clear definitions, rules and user-defined metrics that can be applied consistently to profile, cleanse, standardize, verify and de-duplicate data. This ensures the data you’re processing is fit for purpose and in compliance with data processing regulations.
• Data discovery and observability workflows to better understand the health of your data and identify the data fields critical to the success of each operation. 
• Alignment with established data governance practices to help allocate resources, define workflows and implement data quality improvement initiatives through the data life cycle.

2. Take the Long-Term, Enterprise-Wide Approach to Data Quality

Data quality is not a tactical solution that surfaces only when big problems arise. You can’t afford to wait until a problem is traced back to data quality or inconsistent data quality across functions. After all, the real business advantage today comes from enterprise-wide connected data insights. 

Just like data itself cannot be fragmented and siloed, nor can your data quality framework, which keeps your data clean and fit for purpose. One-off quick fixes may temporarily address a problem in a single application or for a specific business process. But, they will generally not achieve long-term data quality improvements for your business.

An end-to-end, enterprise-wide approach to data quality will:

• Ensure collaboration between data consumers, integrators and producers to:
  • Drive clarity and consensus on data quality definitions, rules and workflows.
  • Contextualize the data for various use cases.
  • Assess its true value to business outcomes. 
• Remain agnostic to applications, use cases and deployment models, applying standard rules across:
  • New tools and technologies in the data management ecosystem.
  • New data formats and structures that keep evolving.
  • Emerging data domains, including new areas (data lakes, AI, IoT) and new data sources.
  • Cloud-based data integration workflows in hybrid, multi-cloud environments.
• Regulate ongoing impact monitoring and measurement to analyze declines or improvements in data quality.

3. Leverage the Power of AI for Next-Level Data Quality

AI-powered data quality management tools act as your intelligent co-pilot to automate critical tasks, cut costs and boost productivity. AI can:

• Learn from metadata to identify patterns and anomalies. Recommend, create and execute rules to fix them.
• Automate repetitive tasks. Profile, cleanse, standardize and enrich data at scale with a key set of pre-built rules.
• Reuse data quality rules to help reconcile new applications or data sources with existing data.
• Support and enrich related data quality processes, such as master data management, data cataloging and data governance.
• Power a self-serve data culture, giving business users — who know the data best — the freedom to access the data they need on-demand and resolve problems without relying on IT. 
  • Natural language interfaces help business users rapidly build, test and run data quality plans with intuitive drag and configure capabilities.