Data technical debt refers to 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 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. The annual cost of bad data is in the trillions of dollars, this problem is real and it won't go away on its own.

Target Audience: Developers, Data Professionals, Managers, Architects
Prerequisites: Understanding of basic data terms
Level: Basic

Extended Abstract

Data technical debt refers to 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 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 what 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 Agile (DA) 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.

Learning objectives:

• Discover what data technical debt is

• Understand the complexities of data technical debt and why they’re difficult to address

• Learn technical and management strategies to address data technical debt