• Solutions
        • STRATEGY & CONSULTING

        • Data & AI Strategy
        • Data Governance
        • Data Risk Management
        • Data Architecture
        • MODERNIZATION

        • Platform Modernization
        • Data Integration
        • Data Migration
        • Snowflake Implementation
        • DATA & AI

        • Data & AI Engineering
        • AI Enablement
        • Fractional Data Team
        • BI & Reporting
  • Technologies
        • Snowflake Elite Partner

          Delivering scalable data platforms, governed data pipelines, and AI-ready architectures for data-driven organizations.

  • Experience
        • MARKET EXPERIENCE

        • SUCCESS STORIES

        • Banking & Financial
        • Healthcare
        • Retail
        • Energy & Utilities
        • Construction and Manufacturing
        • Enterprise
        • Mid-Market
        • Private Equity
        • Building a Scalable Data Strategy from the Ground Up

          The client gained a clear path from fragmented systems to a modern data architecture, with a fully documented roadmap and business case.

        • Using AI to Streamline Home Construction Planning

          The client automated forecasting and prediction processes, saving time, reducing costs, and enabling smarter planning.

        • Explore More Success Stories
  • Company
        • Trusted experts in data, analytics, and AI enablement.

          Whether you’re modernizing your data platform, scaling AI, or building a governance foundation, we bring the right strategy, the right people, and the right pace to get it done right.

        • About
        • Leadership
        • Careers
  • Resources
        • Building A Right-Sized Data Strategy

          A practical framework for aligning data, governance, technology, and AI to business outcomes.


          View Data Strategy Guide
        • Realizing Business Value from Data & AI With Snowflake

          Whether your evaluating or scaling Snowflake, our guide can help you.


          View Our Snowflake Guide
        • Data & AI Governance for the Bank You’re Becoming

          Our interactive guide clarifies what you need as your bank scales.


          View Banking Governance Guide
  • Insights
        • ARTICLES & INSIGHTS

        • Data Strategy
        • Data Management
        • Data Science
        • Data Governance
        • Master Data Management
        • RESOURCES

        • Modern Data Architecture Guide
        • AI Readiness Assessment
        • Data & Analytics Infographics
        • C-Suite Data Answers
        • BUSINESS OBJECTIVES

          Adopt AI Improve Data Quality Eliminate Data Silos Ensure Security & Compliance Deliver Improved Analytics Enable Internal Teams
  • Talk To An Expert

Table of Contents

    Don’t Let Technical Debt Bankrupt Your Data Foundation

    Don’t Let Technical Debt Bankrupt Your Data Foundation

    Technical debt sounds like a term straight out of a show like Shark Tank or Mad Money.

    The truth of the matter is that the Information Technology sector borrowed the idea from Finance. Technical debt is commonly described as efforts deferred for the purpose of meeting deadlines or goals. Eventually, those deferred efforts will need to be revisited and reworked for the final state product. It is a tradeoff between gaining short-term benefits, mainly expediting work, while sacrificing long-term value and overall efficiency.

    According to recent research, IT departments are spending about 40% out of every $10 to address this issues. One of the more interesting aspects of technical debt is that it will most likely go unnoticed by the end user until it comes due. However, if the debt continues to mount and isn’t reconciled, the lingering damage could cause uncompromising harm to an organization’s data fabric.

    How Does Technical Debt Cause Harm?

    Almost every data & technology initiative has incurred technical debt at some point. With the pressures of on-time delivery, most teams find it easy to justify a shortcut or two. This is especially true if the debt appears light or easy to fix later on. Often, these initial estimates do not or cannot factor in the downstream impact of future refactoring. To avoid regrets, consider the harm that too much technical debt can cause:

    • Duplication of effort: Utilizing technical debt as an inexpensive way to accelerate growth can lead to eventual duplication of effort. Whole projects or models may need to be partially or completely reworked to satisfy future state needs. In the end, the business might end up with tools that appear to solve immediate problems but cause more critical issues later.
    • Hindered long-term productivity: Technical debt might speed up immediate deliverables but slow down future projects, make business performance issues tougher to solve (through technology), and compromise the ability of the business to scale. When everything is a priority, and multiple projects suffer from technical debt, enterprises may never make it back to the future state vision.
    • Reliance on legacy software or hardware: Extending reliance on outdated tools and hardware that have trouble keeping up with today’s data influx, can lead to larger problems in the long term. Furthermore, reliance on legacy products can also leave your work force stuck with a skill set that has been rendered obsolete by industry standards.
    • Lost time: Accruing technical debt eventually results in necessary fixes, which never appear at convenient times. Forced fixes for urgent problems carry a high likelihood of stealing time from important work of other projects and in some cases result in missed deadlines.

    How to Avoid Technical Debt

    Most businesses cannot and probably should not entirely avoid technical debt. People and companies incur monetary debt to expand businesses, buy property, and deal with urgent expenses. Sometimes, debt offers a better solution than doing without the monetary boost. As mentioned on the logz.io blog, “…sometimes, technical debt represents the least bad option.” Credit bureaus give their highest scores to entities that manage debt well and not those that avoid it entirely.

    As with financial debt, consider these suggestions to reduce reliance on and improve management of technical debt:

    • Teams must weigh the pros and cons of their data platform performance in order to make informed, logical decisions. Especially in consideration of the business’ growth, data insights, and data integrity.
    • If the team decides to assume technical debt, they should also develop a plan to monitor it and develop a roadmap to address it as the project continues to move forward.
    • Scrum masters can help by coaching, defining goals, assigning responsibilities, developing a project plan, and work to execute it.

    How Data Ideology Helps Manage Technical Debt

    Almost every data & technology adoption encounters technical debt. Consciously or not, it gets added to nearly every project. At Data Ideology, experience has taught us to acknowledge this debt and develop a quick payoff strategy for the best, long-term results.

    For some examples:

    • We manage technical debt with a Proof-of-Concept (POC) approach to data solutions. A POC approach establishes the best criterion to acknowledge, measure, and pay the debt before releasing any project into full-scale production.
    • We also enjoy success with Snowflake, a maintenance-free data management SaaS platform that prevents accruing technical debt and relieves IT from future burdens.

    Regardless of the project’s scope, these initiatives can maximize benefits and minimize costs. If Technical Debt is keeping your organization from fully realizing your data’s power and value, drop us a line. Tell us about your project. We’ll show you how we can help you wipe out your debt without going technically or financially bankrupt.

    Talk To A Data Expert

    Transform your data into business growth, innovation, and differentiation.

    Get Started Contact

    Explore More Insights

    1. Why do you need a Data Strategy now more than ever?
    2. Scrum Master Best Practices to Accelerate Data Projects
    3. Data Strategy Assessment: Current and Future State Analysis
    4. How to Build a Data Strategy Roadmap
    5. What is a Data Strategy & Why is it Important?

    Stay up to date on everything Data, Analytics & AI.

    Authors

    • Ian Coyle

      Ian Coyle

      Director of Business Development

    Browse Topics

    • AI Adoption
    • Data Architecture
    • Data Governance
    • Data Management
    • Data Science
    • Data Strategy
    • Master Data Management

    AI Use Cases

    • AI Data Strategy for Automated Regulatory Compliance in Banking & Financial Services
    • Hospital Resource Optimization
    • AI Data Strategy for Supply Chain Management in Healthcare
    • Patient Flow Optimization
    • AI Data Strategy for Portfolio Risk Assessment in Banking & Financial Services
    Data Ideology LinkedIn Data Ideology Twitter Data Ideology Youtube Data Ideology Facebook

    Women-Owned Business

    Privacy Policy

    Cookie Policy

    Website by Insivia, Tech Marketing Agency
    Snowflake Certified Partner
    Solutions
    • Data Strategy
    • AI Data Consulting
    • Data Risk Management
    • Data Governance
    • Data Engineering & Integration
    • Data Team-As-A-Service
    • Data Architecture
    • BI & Reporting
    Markets
    • Banking & Financial
    • Healthcare
    • Retail
    • Energy & Utilities
    • Construction & Manufacturing