If Data is Everywhere—Then Data Quality Needs to Be Everywhere as Well

May 01, 2020 |

Donal Dunne

And if data is being used by everyone, then everyone must have access to quality data

data quality

All organizations have data quality issues. These issues are usually exposed during digital transformation initiatives: when organizations move volumes and varieties of data between applications to an ever-increasing number of transition applications and data center workloads to the cloud and hybrid environments, the quality of the data becomes evident.

The costs of data quality problems are high. Confidence and trust in data—and in the systems that contain the data—erodes as business users get frustrated with incomplete, inconsistent, and inaccurate data. The business impact of poor data quality ranges from day-to-day failures—such as poor customer experience, inefficient marketing processes, and supply chain errors—to major delays or failures in digital transformation initiatives as organization integrate digital technology into every part of the business. As data requirements expand to other data domains (like sensors), is streamed at ever-increasing velocity, and is consumed by more users, the potential for data quality problems only increases.

The business can’t solve data quality problems on its own. Line-of-business managers, business analysts, data stewards, and IT need to collaborate and take ownership, but lack the appropriate tools and processes. IT is frequently scapegoated as the cause for data quality problems, but this is rarely the case. While IT can help to solve data quality problems, IT often cannot respond within the time frames the business requires.

So instead of working together, departments and business units often decide to implement their own separate data quality projects. And while these projects may solve an immediate problem or address an immediate need, this one-off approach has larger implications. Any one individual project won’t be part of an overall strategy to improve data quality across the enterprise. And any specific data quality rules or artifacts created for an individual project probably can’t be repurposed for other projects or applications.

Without a consistent, comprehensive way to manage data quality, bad data continues to propagate across the enterprise. Confidence in data continues to erode. Costs continue to rise. Business remains at risk for failing to comply with regulations.

Data Quality for Everyone, Everywhere

It is difficult to prevent bad data from entering the business—and it is equally challenging to prevent it from propagating as it moves between applications and organizations, and between providers and consumers. Clearly, with the move toward data democratization, it is no longer sufficient to have one or two tactical data quality initiatives. Data quality must be addressed at all levels across the organization. And data quality needs to be available to everyone, everywhere.

What Does “Data Quality for Everyone, Everywhere” Mean?

Data quality for everyone, everywhere means that organizations can trust all their data, for all their needs, all the time. This may sound obvious, but let me share some thoughts:

More people need to be involved in data quality processes. Data quality needs to be an enterprise-wide endeavor. Everyone needs to be empowered with the right tools they need to take responsibility for the data, including line of business managers, data stewards, analysts, and IT developers.
Data quality needs to extend beyond name and address to all data domains such as product, financial, asset, social, and sensor data.
Data quality needs to follow were the data and applications are as organizations transition to cloud and hybrid environments.
To proactively prevent the entry of low-quality data, the organization needs to either deploy common data quality rules or use data services to regularly cleanse the data.
Data quality scorecards need to be published and shared, allowing the entire organization to monitor and measure data quality across all project, processes, and applications.
There needs to be a clear understanding of the risk and impact of poor data quality, as well as the business value of high-quality data across the organization. This understanding will drive the need for people to become actively involved in, and accountable for, ensuring data quality.

Data will only get more important to your organization. So, data quality will be foundational to your organization’s success. Taking a data quality for everyone, everywhere approach in your organization might be the biggest impact you can ever make to the success of the company.

Why not join us for our Cloud Data Quality Virtual Summit to hear thought leaders and innovators on crucial topics, including a live keynote with Jitesh Ghai on why enabling employees to use data effectively is pivotal to a successful cloud strategy. After the keynote, check out our Solution Theater sessions, which offer access to an interview with a partner or customer and a deep-dive product demo:

Data Quality for Everyone, Everywhere: See how to democratize trusted data in a hybrid and multi-cloud environment and empower end users through self-service capabilities.
Cloud Data Quality for Cloud Data Warehouses: Learn how to adopt proven methods for validation, cleansing, and standardization of data to generate market-shaping insights from cloud data warehouses.
Accelerate SaaS Migration and Adoption: Discover how trusted data can speed up migrations, reduce risk and business disruption, increase the value of data when it arrives, and improve user adoption of SaaS applications.

How can I empower users with trusted data? webinar

You can also experience a no-code, visual environment for creating and managing data quality capabilities—all built on a leading next-generation iPaaS. Sign up for our free 30-Day Trial for Cloud Data Quality here.