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Data Quality Improvement Assessment

What steps can your organization take to improve its data quality?

 

The issue of data quality is quickly moving up the corporate agenda, and yet, many organizations are still not taking an optimized and governed approach towards managing the quality of their data assets. Data lives at the heart of all business operations and an organization's ability to unlock its value is critical to ongoing business performance.

The Data Quality Improvement Assessment can help organizations to gauge the maturity of their data quality methodology by analyzing the three core building blocks of a successful data improvement initiative;People, Process, and Technology.

By completing our short assessment, you will understand where your organization sits on our maturity scale and what steps you can take to improve the quality of your data.

 

The three core building blocks of effective data quality

Expand the sections below to find out more about the three key elements to a successful data quality strategy.

The driving force within your organization. Responsible for implementing business as usual activity, instigating change and setting strategy.

People are critical to the effective roll-out and implementation of a robust data quality strategy, ensuring the right personnel are in place to champion your data quality initiative, linking performance to key business success metrics and creating a culture of data quality as business as usual.

A structured set of activities that have a well-defined and documented outcome.

In data quality terms your processes are designed to give a clear identification of roles, responsibilities, and ownership. The volume and variety of data entering your organization on a daily basis is growing exponentially and without a clear understanding of the process flow of that information it will create issues for downstream users and ultimately minimise the impact of tactical investment in data improvement.

The tools employed by an organization to help perform a specific action or activities more efficiently.

Data quality technology has evolved over recent years and offers critical support for managing information across its full lifecycle, from data discovery through to monitoring and visualization.