All technology projects are data projects, yet data is still an afterthought in many organizations. With AI on the horizon, it's time to prioritize data quality management in the business.
Author of "Data Quality: The Field Guide," Tom Redman, emphasizes the importance of business involvement in the most critical moments of a piece of data's lifetime: the moment it is created and the moment it is used. These moments often occur outside of IT, as the business consumes vast amounts of data.
Those who have provisioned and consumed data know that bad data dies hard. It's time to get rid of it before it gets rid of you. Data quality management requires intentional collaboration to become a priority in organizations. If it happened organically, it would have.
DQ should not BE confined to IT departments
Any processes, technology, analytics, or AI initiatives must consider data quality by design. Data quality must be a key performance indicator (KPI) in a firm's risk profile and managed at the executive level. The best people, processes, IT solutions, analytics, and AI frameworks and approaches will not lead to a quality outcome if the data is poor. Tools alone will not solve this issue. Culture must assist this challenge and requires people to drive change enterprise-wide.
Assigning DQ tasks to data consumers improves outcomes
Data creation must focus on clear guidelines and training for those involved in data entry and management. This includes defining standards for data accuracy, completeness, and timeliness. For example, a stakeholder in organizational development could develop a comprehensive training program or set of resources emphasizing the importance of data quality and providing practical tips for maintaining it.
DQ informs business decisions & should be inclusive
Responsibility for data quality should be a shared endeavor across an organization, not solely on the IT department. It involves collaboration between those who create, manage, and consume the data. For example, the data project lead can partner to develop literacy initiatives to improve data skills across the enterprise and establish clear guidelines for data management within teams, leveraging your skills in organizational development and team leadership.
Improving Literacy helps everyone become an Advocate
Improving data literacy within teams can start with easy-to-read newsletters or structured learning--depending on how the data team is resourced and the organization's maturity. Data literacy training could help stakeholders develop strategies for finding and using various types of information effectively. The training would cover essential skills in evaluating the usefulness and trustworthiness of data sources. Users would learn to consider the source's credibility, the data's consistency with other information, and its peer-review status. For example, users might learn to apply these criteria to enhance the reliability of the data they work with, ensuring it supports accurate analysis and decision-making. The ideal outcome? Everyone involved learns to become an effective advocate of data and culture change in the organization, emphasizing the shared responsibility for data quality across all departments.
Buy Driving Data Projects on Amazon to learn:
the strategies and tools to partner more effectively, intentionally, and collaboratively with business stakeholders.
the importance of identifying, cultivating, and supporting executive sponsorship to aid data project and transformation success.
the culture shifts needed to manage ongoing change and how to best prepare for them to sustain success.