Imagine a beautiful piece of furniture made of rotten wood or a fashionable shirt made of poor quality fabric. The quality of the material affects the final product. So why should data insights, the main product of your company’s massive 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 superior quality of your business insights is based on the quality of the raw data used to generate them.
The term “quality” alludes not only to accuracy, but also to consistency, completeness, conformity, and integrity. When a dataset is of high quality, you can more easily process and analyze it to create business value. High quality data creates an efficient cycle. When users trust your data, they use it more and get better results. Consequently, it creates a stronger data culture in your organization.
On the other hand, there is low or unknown data quality, which is far from benign. Bad data can result in a vicious cycle involving inaccurate analytics, ill-informed decisions, significant financial or reputational damage, and a damaged data culture.
Who is responsible for data quality?
Good data is on everyone’s wish list. But where does the responsibility lie for ensuring high-quality data across the entire 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, as the journey becomes complex and often lacks transparency, these stakeholders tend to focus only on their own pieces of the puzzle. This means that data quality, which concerns everyone, often becomes nobody’s responsibility.
Even specifically appointed data stewards would not thrive without the active participation of the following three groups of stakeholders who work directly with the data.
Data producers
In most enterprises, petabytes of data flow from the daily 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 understand the data they collect, should consciously collect data with real business value instead of throwing all the data they generate into analytics. The bottom line is that collecting, storing and processing data has security and cost implications. Clearly defined data fields and qualifiers help keep your data relevant and timely for further use.
Data integrators
Data engineers play a significant role in turning raw data into business insights. In many organizations, the responsibility for data quality rests with you as the creator and owner of the pipelines that move and transform data.
Although you are adept at handling data, you may lack a deep understanding of the data itself. This can lead to challenges in data quality management. For example, while a data user may know that a particular field can never be a negative value, you may not. Documenting data quality rules that define how and when they apply at each step of the data journey would help you achieve more consistent results.
Data consumers
Business users — such as sales teams, marketing operations and data analysts — want reliable data and business-ready insights. When they can see where data is being combined, changed or transformed for quality purposes along with the formats, sources and workflows that affect the data, they feel more confident in their analytics and insights.
However, they are not as technically sound as data engineers — meaning that self-service options must be easy to use and intuitive to implement easily.
3 basic rules for permanently improving data quality
For most companies, the proliferation of data tools is already a challenge. Add to that poor quality data and you have a recipe for keeping expensive engineering resources in constant firefighting mode instead of focusing on strategic work. In fact, 41% of CDOs say they need to 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 dataset, there is an urgent need for clean, high-quality data in the data management ecosystem. Without it, generative artificial intelligence and services managed by the large language model (LLM) cannot improve results.
Here are three basic rules for a permanent transition from a ‘garbage in, garbage out’ (GI-GO) mode to a ‘quality in quality out’ (QI-QO) mode.
1. Build a strong foundation of data quality
Data quality is not something you can make up or improve on the fly. The mandate for high-quality data should be built into the data management foundation of your business. This includes:
- Clear definitions, rules, and user-defined metrics that can be consistently applied to profiling, cleaning, standardization, verification, and deduplication of data. This ensures that the data you process is fit for purpose and complies with data processing regulations.
- Data discovery and visibility workflows to better understand the health of your data and identify data fields critical to the success of any operation.
- Alignment with established data management practices to help allocate resources, define workflows, and implement initiatives to improve data quality throughout the data lifecycle.
2. Take a long-term approach to data quality at the enterprise level
Data quality is not a tactical solution that surfaces only when big problems arise. You can’t afford to wait until the problem is traced back to data quality or inconsistent data quality across functions. After all, real business advantage today comes from seeing connected data across the entire company.
Just as the data itself cannot be fragmented and isolated, neither can your data quality framework, which keeps your data clean and fit for purpose. One-time hotfixes can temporarily solve a problem in a single application or for a specific business process. But they generally won’t achieve long-term data quality improvements for your business.
A comprehensive enterprise-level approach to data quality will:
- Ensure cooperation between data users, integrators and producers in order to:
- Encourage clarity and consensus on data quality definitions, policies and workflows.
- Contextualize data for different use cases.
- Assess its true value to business results.
- Remain independent of applications, use cases and deployment models by applying standard rules to:
- New tools and technologies in the data management ecosystem.
- New data formats and structures that are constantly evolving.
- Emerging data domains, including new areas (data lakes, AI, IoT) and new data sources.
- Cloud-based data integration processes in multi-cloud hybrid environments.
- Regulate continuous monitoring and impact measurement to analyze declines or improvements in data quality.
3. Use the power of artificial intelligence for a higher level of data quality
AI-powered data quality management tools act as your intelligent co-pilot to automate critical tasks, reduce costs and increase productivity. AI can:
- Learn from metadata to identify patterns and anomalies. Recommend, create and execute policies to fix them.
- Automate repetitive tasks. Profile, clean, standardize and enrich data at the level with a key set of pre-built rules.
- Reuse data quality rules to help align new applications or data sources with existing data.
- Support and enrich related data quality processes, such as master data management, data cataloging and data management.
- Empower a self-service data culture, giving business users — who know data best — the freedom to access the data they need on demand and solve problems without relying on IT.
- Natural language interfaces help business users quickly build, test and launch data quality plans with intuitive pull-out and configuration capabilities.