Data Technical Debt: Proven Strategies to Improve Data Quality
Data technical debt refers to data quality challenges associated with legacy data sources, including both mission-critical sources of record as well as “big data” sources of insight. Data technical debt, sometimes called data debt, impedes the ability of your organization to leverage information effectively for better decision making, increases operational costs, and impedes your ability to react to changes in your environment. Bad data is estimated to cost the United States $3 trillion annually alone, yet few organizations have a realistic strategy in place to address data technical debt.
This presentation defines data technical debt is and why it is often a greater issue than classic code-based technical debt. We describe the types of data technical debt, why each is important, and how to measure them. Most importantly, this presentation works through disciplined strategies for avoiding, removing, and accepting data technical debt. Data is the lifeblood of our organizations, we need to ensure that it is clean if we’re to remain healthy.
What You’ll Learn About Data Technical Debt
- Definition of technical debt, data technical debt
- Why data technical debt is important
- Types of data technical debt
- Strategies for avoiding data technical debt
- Strategies for removing data technical debt
- Strategies for accepting data technical debt
Audience For This Presentation
- Experienced developers who want to learn about the full range of technical debt
- Architects who want to avoid new data technical debt, and reduce existing debt within their organization
- Managers and leaders who want to understand and address data technical debt
Why You Want to Hear About Data Technical Debt From Me
I’m the thought leader behind the Agile Data method, a primary focus of which is on effective strategies to either avoid to to remedy data technical debt, a term which I coined. This presentation shares my experiences and both technical and management ways of working (WoW) pertaining to data technical debt.
I have given versions of this presentation at data and agile conferences, and to user groups, since 2017.