Building a reliable data quality strategy in the age of AI

Effective business decision-making is at the core of any successful business. Good decisions are based on facts and data. When confidence in the quality of the data supporting important decisions is shaken, forward momentum and growth can break down. Ensuring that decision-makers trust each data point requires a definitive strategy. Simply implementing the latest tools and technology will not deliver optimal outcomes. Data quality needs to be part of your organization’s culture.

Importance of data quality

While data quality is vitally important, managing it is a significant challenge. In the age of AI, data quality will only grow in importance as AI can amplify the effects of low-quality data. Poor data that feed AI models lead to bad customer experiences and potential catastrophes that can damage reputations. High-profile mistakes by AI models can put a brand on the front page of the New York Times, causing irrefutable damage to an organization’s competitiveness

In a recent study by Vanson Bourn, 68% of respondents indicated that they struggle to cleanse data so it is in a usable format for AI programs. The study also pointed out that underperforming AI models built on low-quality data result in an average of $460 million in lost revenues.

The more an organization leans into its data and AI, the more important a solid data quality strategy is.

Data Quality Strategy - What Do You Need?

For a successful data quality strategy, you need four distinct components: metrics, culture, governance, and tools.

To understand the quality of your data, you must be able to measure it. Tracking the right metrics will help you determine where to improve and if your strategy is succeeding. Metrics also help you set goals and define tolerances.

A perfectly defined strategy is useless if you do not get the buy-in from the people who must implement it. Employees across the organization need to embrace a data quality culture that must emanate from the top management.

Data governance policies are where the rubber meets the road. Metrics and a data culture directly impact data governance and ensure the correct policies are in place to support top-quality data.

Having the best tools and platforms to track and manage data quality is also a key component of your data quality strategy.

Measuring data quality

To ensure data quality, you need to measure it. Data quality is graded by six metrics: completeness, consistency, timeliness, uniqueness, validity, and accuracy,

Completeness

This metric measures the number of incomplete records. When records are incomplete, it can lead to distorted data sets that can throw off your analysis. Data sets with many incomplete records cannot provide the same value as a dataset with most of the data present. Data sets with many missing values lead the analyst to place too much weight on the available data, distorting and skewing results.

Consistency

This metric measures data uniformity and accuracy across different systems. When two separate systems have two different values for the same data point, they are inconsistent. This conflict reduces the confidence managers have in the data. They know that when data conflicts, at least one is inaccurate, but without knowing which one and why, the value either data set can provide for decision-making is reduced.

Timeliness

This metric measures the age of the data in the database or how long ago it was refreshed. The world is constantly changing, and data that measures this change needs to be continuously updated. Decisions based on data that measure conditions that have since changed will not be optimal.

Uniqueness

This metric tracks duplicate data. The amount of data collected and stored in multiple databases is high. As data comes together, the same data from multiple sources may be duplicates. Or data can be inputted twice into a single database. If data is double counted, it can skew analysis.

Validity

This metric measures whether data conforms to a specific format. If a data point does not conform to the expected format, it may not reflect what you think it does. For example, if a data point can not be a negative number, yet you have negative numbers in your data set, the validity is questionable.

Accuracy

This metric measures how close the values in your data set are to their true values. Making decisions based on data that is simply wrong will lead to bad decisions. When accuracy is low, decision-makers cannot be confident that the data they are analyzing represents reality.

Tracking these metrics will provide insight into the quality of your data and where errors are occurring. However, to have superior data quality metrics, you need a culture and strategy to maintain high data quality measures. A data quality-focused culture provides the foundation for this objective.

Data Quality Culture

Data is collected, flows, and is consumed across all facets of any organization. Everyone in the typical organization touches data in some part of their job. Instilling a sense of responsibility for data quality in each individual is central to your data quality strategy. This fact means every employee practices good data hygiene by cleaning dirty data, validating data, and updating data. You need adequate training, leadership, and teamwork to install a data quality culture.

Training

Not everyone has the same level of skills working with data. Not everyone understands what data means and why it is crucial. Teaching employees how to work with data to help them in their jobs will lead them to appreciate its value. As they gain more skills and learn to become more data literate, they will have a greater appreciation for the nuances of data quality.

Training on interoperating data quality metrics, data capture and validation techniques, and data cleansing tools and processes is also essential for a solid data quality strategy.

Access to data also helps drive greater data literacy within your organization, which drives a greater appreciation for data quality. When individuals can access data without technical data engineering skills, they can practice existing data analysis skills and develop new ones to improve their proficiency in working with data and their appreciation for data quality. Check out our recent blog to learn more about driving greater data literacy. link to blog on data literacy

Collaboration and Teamwork

With data quality, the responsibility of every team member working together to deliver the most trustworthy data should be a fundamental component of your data culture. Roles and responsibilities must be defined so each team member understands how to contribute to data quality and what they are in charge of. This structure also helps workers understand who to collaborate with to manage and improve data quality. Greater collaboration enhances the process of addressing data quality issues and avoiding future problems.

Leadership

As with any cultural initiative, leadership needs to come from the C-suite. Leaders must constantly highlight the importance of data quality and how it is core to success. The ability to drive change starts with senior management. Middle management, data stewards, and domain managers also drive a data quality culture. These professionals must help educate their colleagues on best practices and emphasize the importance of data quality.

Data Governance Framework & Policies

In a constantly evolving data ecosystem that must adapt to the requirements of AI while still maintaining order, privacy, and security, traditional approaches to data governance must adapt

Agile Data Governance

Defining data governance and policies becomes much easier with rich data metrics and a data-driven culture. Responsibility for managing and stewarding data can be pushed down to domain managers instead of consolidating control in the IT department. This shift enables much more secure and effective access to data. Domain managers have a much better understanding of the data their group collects and who should have access to it. This knowledge enables more agile and dynamic governance policies, including attribute access controls or column-level access authority.

With an agile governance structure, ensuring that changes are working, a feedback loop needs to be enabled that can quickly iterate new policies and flag quality issues to ensure bad data does not taint critical decision-making or models. Communication channels must be open to gain quick authority to access data or report quality issues to data owners.

Agile governance and a data quality culture enable each other. To make quick governance decisions, a team effort and shared responsibility drives quick change. Without a culture that is mutually supportive and knowledgeable, authority remains centralized. In a symbiotic relationship, an agile data governance that enables access to teamwork fuels a data quality culture.

Standardization & consistency

Policies and frameworks that drive data standardization reduce confusion and the potential for errors. Data management policies should aim to standardize naming conventions and aspire to a single source of truth.

Conflicts are reduced by consolidating different data sets into a single data source, and analysts know they are working with the most accurate and timely data set. Master data management strategies support managing data sets to establish standardized data and consolidate management and monitoring.

Data monitoring

With established metrics, a strong data quality culture, and governance policies, the last step of your governance strategy is data monitoring. Monitoring ensures that policies are adhered to and data remains trustworthy. Processes include data profiling, data observability, and data lineage.

Data profiling identifies the content structure and formatting of each data set to identify data quality issues. This profiling would include calculating means and percentiles and collecting minimums and maximums. When these characteristics are compared to what we would expect values and formats to be, they can help identify data quality issues.

To ensure systems are running effectively and data errors are not being created,

data observability monitors the real-time performance of data systems

Data lineage maps the history of data as it is transformed and travels through a data pipeline. Monitoring this data helps analysts find the root source of data errors and gauge the trustworthiness of data sets based on their origin. We go deeper into data lineage in our recent blog -Know More

The Avrio Platform enables your data quality strategy in many ways.

The platform is designed to be used by professionals with various levels of expertise, from data scientists to analysts and domain managers. This makes Avrio an ideal platform to support collaboration between data practitioners and management in developing frameworks for agile governance.

Also, the platform provides more access to more people, regardless of their technical expertise. This helps drive greater data culture and literacy. When professionals have more access to data, they also take more responsibility for its quality.

Avrio supports a robust data quality module. This module performs over 15 data quality tests across six broad categories. The data catalogue tracks data lineage to provide more info on data sources.

Finally, the Avrio marketplace makes data products available to data consumers. It includes a feedback mechanism that allows users to alert data product producers, stewards, and engineers about issues with data quality. The trustworthiness of the data can also be rated by users within the data product. This feature helps expose data products with the highest data quality to more users.

Successful AI strategies rely on good data, making data quality one of the most critical and challenging issues in the foreseeable future. Getting on the right trajectory for data quality as soon as possible will pay dividends.