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Speakers:Scott Ambler

Data Technical Debt: Looking Beyond Code

Conference Theme: Customer Cases

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 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 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.

Agenda

• 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

Learning Objectives

• Technical debt
• Data technical debt
• Data quality

Speakers : Nols Ebersohn

Information is an asset, however every organisation faces a bleak and foreboding prospect of facing in business intelligence the equivalent of a GFC.

Why is it that most data warehouses on average only last for 3-5 years, at which time its either duplicated or replaced? How and where will the next major investment failure occur for Business Intelligence? How is this preventable and what are the key success factors in dealing with the organisational technical debt?

Somebody has to pay this debt - like the sub-prime crisis, someone will have to find a way to have the information assets based on real assets instead of fictitious assets. We will explore the ways to quantify, manage and remediate these challenges.

Speaker: Nols Ebersohn

Part 2:

Information is an asset, however every organisation faces a bleak and foreboding prospect of facing in business intelligence the equivalent of a GFC. Why is it that most data warehouses on average only last for 3-5 years, at which time its either duplicated or replaced? How and where will the next major investment failure occur for Business Intelligence?

How is this preventable and what are the key success factors in dealing with the organisational technical debt? Somebody has to pay this debt - like the sub-prime crisis, someone will have to find a way to have the information assets based on real assets instead of fictitious assets.