What Is Technical Debt? A Complete Guide

You buy the latest iPhone on credit. Turn to fast car loan services to get yourself those wheels you’ve been eyeing for a while. Take out a mortgage to realise your dream of being a homeowner. Regardless of the motive, the common denominator is going into financial debt to achieve something today, and pay it off in future, with interest. The final cost will be higher than the loan value that you took out in the first place. However, debt is not limited to the financial world.

Technical Debt Definition

Technical debt – which is also referred to as code debt, design debt or tech debt – is the result of the development team taking shortcuts in the code to release a product today, which will need to be fixed later on. The quality of the code takes a backseat to issues like market forces, such as when there’s pressure to get a product out there to beat a deadline, front-run the competition, or even calm jittery consumers. Creating perfect code would take time, so the team opts for a compromised version, which they will come back later to resolve. It’s basically using a speedy temporary fix instead of waiting for a more comprehensive solution whose development would be slower.

How rampant is it? 25% of the development time in large software organisations is actually spent dealing with tech debt, according to a multiple case study of 15 organizations. “Large” here means organizations with over 250 employees. It is estimated that global technical debt will cost companies $4 trillion by 2024.

Is there interest on technical debt?

When you take out a mortgage or service a car loan, the longer that it takes to clear it the higher the interest will be. A similar case applies to technical debt. In the rush to release the software, it comes with problems like bugs in the code, incompatibility with some applications that would need it, absent documentation, and other issues that pop up over time. This will affect the usability of the product, slow down operations – and even grind systems to a halt, costing your business. Here’s the catch: just like the financial loan, the longer that one takes before resolving the issues with rushed software, the greater the problems will pile up, and more it will take to rectify and implement changes. This additional rework that will be required in future is the interest on the technical debt.

Reasons For Getting Into Technical Debt

In the financial world, there are good and bad reasons for getting into debt. Taking a loan to boost your business cashflow or buy that piece of land where you will build your home – these are understandable. Buying an expensive umbrella on credit because ‘it will go with your outfit‘ won’t win you an award for prudent financial management. This also applies to technical debt.

There are situations where product delivery takes precedence over having completely clean code, such as for start-ups that need their operations to keep running for the brand to remain relevant, a fintech app that consumers rely on daily, or situations where user feedback is needed for modifications to be made to the software early. On the other hand, incurring technical debt because the design team chooses to focus on other products that are more interesting, thus neglecting the software and only releasing a “just-usable” version will be a bad reason.

Some of the common reasons for technical debt include:

  • Inadequate project definition at the start – Where failing to accurately define product requirements up-front leads to software development that will need to be reworked later
  • Business pressure – Here the business is under pressure to release a product, such as an app or upgrade quickly before the required changes to the code are completed.
  • Lacking a test suite – Without the environment to exhaustively check for bugs and apply fixes before the public release of a product, more resources will be required later to resolve them as they arise.
  • Poor collaboration – From inadequate communication amongst the different product development teams and across the business hierarchy, to junior developers not being mentored properly, these will contribute to technical debt with the products that are released.
  • Lack of documentation – Have you launched code without its supporting documentation? This is a debt that will need to be fulfilled.
  • Parallel development – This is seen when working on different sections of a product in isolation which will, later on, need to be merged into a single source. The greater the extent of modification on an individual branch – especially when it affects its compatibility with the rest of the code, the higher the technical debt.
  • Skipping industrial standards – If you fail to adhere to industry-standard features and technologies when developing the product, there will be technical debt because you will eventually need to rework the product to align with them for it to continue being relevant.
  • Last-minute product changes – Incorporating changes that hadn’t been planned for just before its release will affect the future development of the product due to the checks, documentation and modifications that will be required later on

Types of Technical Debt

There are various types of technical debt, and this will largely depend on how you look at it.

  • Intentional technical debt – which is the debt that is consciously taken on as a strategy in the business operations.
  • Unintentional technical debt – where the debt is non-strategic, usually the consequences of a poor job being done.

This is further expounded in the Technical Debt Quadrant” put forth by Martin Fowler, which attempts to categorise it based on the context and intent:

Technical Debt Quadrant

Source: MartinFowler.com

Final thoughts

Technical debt is common, and not inherently bad. Just like financial debt, it will depend on the purpose that it has been taken up, and plans to clear it. Start-ups battling with pressure to launch their products and get ahead, software companies that have cut-throat competition to deliver fast – development teams usually find themselves having to take on technical debt instead of waiting to launch the products later. In fact, nearly all of the software products in use today have some sort of technical debt.

But no one likes being in debt. Actually, technical staff often find themselves clashing with business executives as they try to emphasise the implications involved when pushing for product launch before the code is completely ready. From a business perspective, it’s all about weighing the trade-offs, when factoring in aspects such as the aspects market situation, competition and consumer needs. So, is technical debt good or bad? It will depend on the context. Look at it this way: just like financial debt, it is not a problem as long as it is manageable. When you exceed your limits and allow the debt to spiral out of control, it can grind your operations to a halt, with the ripple effects cascading through your business.

 

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Spreadsheet Woes – Ill-Equipped for an Agile Business Environment

These days, crucial business decisions have to be made in a split second. However, the quality of these decisions hinges quite often on timely, insightful information and relevant business reporting.

How effective is your business reporting solution in providing you with the information you need at the time you need it?

Chances are, like 75% of small and medium businesses, your company is using spreadsheets. True, spreadsheets are the most common go-to solutions for on-the-fly forecasting, but they may not be your best option for presenting information that require consolidation and in-depth analysis and involve a lot of number crunching, especially with critical data at stake.

Furthermore, spreadsheet-based reports are rarely produced in a timely manner. In today?s fast evolving business environment where flexibility, mobility, and timeliness are the order of the day, this simply won’t do.

Let’s take a look at the particular areas where spreadsheets fall short when it comes to providing dynamic and sound financial reports:

Collaboration

With rapidly changing market conditions, organisations have to conduct budgeting, forecasting, and planning more often. Hectic schedules and geographical distances aren’t a hindrance though, because technologies like the Internet, advanced telecommunications and mobile devices can put instantaneous collaboration at everyone?s fingertips.

But collaborative activities in a dynamic setting can only succeed if all participating individuals are given secure, real time and simultaneous access to the same relevant information. This way, every change made is automatically consolidated and projected unto the bigger picture for everyone to digest.

Alas, spreadsheets aren’t built for this.

Cost Efficiency

Whether we’re in a recession or not, cost efficiency has to be taken into consideration. Are spreadsheets really the cost-effective solution?

Think ?time is money?. With the length of time needed to prepare data, establish controls, consolidate reports and distribute copies, you’ll realise how expensive spreadsheets actually are.

The ability to innovate in a changing economic environment and limited resources – a valuable derivative of agile practices – can give your company a very significant advantage. But dedicating so much time on spreadsheet management can strip your organisation of room for innovation.

Quality of Reports

Business empires rise and fall on the power of relevant information. At the end of the day, top management should assess their sources of key performance reports, planning tools and budgeting applications using these parameters:

  • Does your financial reporting system give you the right information right when you need it?
  • Do the reports allow you to look beyond the numbers to spot trends or forecast changes in the market?
  • Do they furnish enough significant data for you to make informed decisions in good time?

Spreadsheets weren’t designed to analyse data on the enterprise level. As a result, spreadsheet reports often take far too long to prepare and more importantly, may lack the dimension and depth that are crucial in decision making.

Data Reliability

We’re all familiar with the risks associated with spreadsheets. This error-prone UDA can provide inaccurate information simply because of a broken link, an incomplete range, a deleted number, or an incorrect formula. In an active business scenario where data manipulation has to be done under constant time pressure, the risk probabilities escalate.

As they always say, ?If anything can go wrong, it will?. With spreadsheets, a lot of things could go wrong. Is this the kind of tool you?d like to work with when making fast, crucial decisions? If you’re still using spreadsheets, then you?d best forget about dynamic reports and rolling forecasts.

Inability to adapt to personnel turnover

A key challenge in maintaining the spreadsheet system is picking up where another left off. A user would find it difficult to debug, revise, or analyse a spreadsheet system he developed himself and the process becomes doubly complicated if or when another person takes over.

Starting from scratch is painfully counterproductive, so that a newcomer has to spend hours figuring out the original entries in the spreadsheet and the reports it yields.

While no one is indispensable in any organisation, it’s pretty much accurate to say that if a spreadsheet ?developer? leaves, it could momentarily halt the production of key finance reports. In a fast changing business landscape, such failure to monitor performance at critical times could sound the death knell for your company.

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How Volvo Dublin achieved Zero Landfill Status

The sprawling New River Valley Volvo plant in Dublin, Virginia slashed its electricity bill by 25% in a single year when it set its mind to this in 2009. It went on to become the first carbon-neutral factory in 2012 after replacing fossil energy with renewable power. Further efforts rewarded it with zero-landfill status in 2013. ecoVaro decided to investigate how it achieved this latest success.

Volvo Dublin?s anti-landfill project began when it identified, measured and evaluated all liquid and solid waste sources within the plant (i.e. before these left the works). This quantified data provided its environmental project team with a base from which to explore options for reusing, recycling and composting the discards.

Several decisions followed immediately. Volvo instructed its component suppliers to stop using cardboard boxes and foam rubber / Styrofoam as packaging, in favour of reusable shipping containers. This represented a collaborative saving that benefited both parties although this was just a forerunner of what followed.

Next, Volvo?s New River Valley truck assembly plant turned its attention to the paint shop. It developed methods to trap, reconstitute and reuse solvents that flushed paint lines, and recycle paint sludge to fire a cement kiln. The plant cafeteria did not escape attention either. The environment team made sure that all utensils, cups, containers and food waste generated were compostable at a facility on site.

The results of these simple, and in hindsight obvious decisions were remarkable. Every year since then Volvo has generated energy savings equivalent to 9,348 oil barrels or if you prefer 14,509 megawatts of electricity. Just imagine the benefits if every manufacturing facility did something similar everywhere around the world.

By 2012, the New River Valley Volvo Plant became the first U.S. facility to receive ISO 50001 energy-management status under a government-administered process. Further technology enhancements followed. These included solar hot water boilers and infrared heating throughout the 1.6 million square foot (148,644 square meter) plant, building automation systems that kept energy costs down, and listening to employees who were brim-full with good ideas.

The Volvo experience is by no means unique although it may have been ahead of the curve. General Motors has more than 106 landfill-free installations and Ford plans to reduce waste per vehicle by 40% between 2010 and 2016. These projects all began by measuring energy footprints throughout the process. ecoVaro provides a facility for you to do this too.

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Migrating from CRM to Big Data

Big data moved to centre stage from being just another fad, and is being punted as the latest cure-all for information woes. It may well be, although like all transitions there are pitfalls. Denizon decided to highlight the major ones in the hope of fostering better understanding of what is involved.

Accurate data and interpretation of it have become increasingly critical. Ideas Laboratory reports that 84% of managers regard understanding their clients and predicting market trends essential, with accelerating demand for data savvy people the inevitable result. However Inc 5000 thinks many of them may have little idea of where to start. We should apply the lessons learned from when we implemented CRM because the dynamics are similar.

Be More Results Oriented

Denizon believes the key is focusing on the results we expect from Big Data first. Only then is it appropriate to apply our minds to the technology. By working the other way round we may end up with less than optimum solutions. We should understand the differences between options before committing to a choice, because it is expensive to switch software platforms in midstream. data lakes, hadoop, nosql, and graph databases all have their places, provided the solution you buy is scalable.

Clean Up Data First

The golden rule is not to automate anything before you understand it. Know the origin of your data, and if this is not reliable clean it up before you automate it. Big Data projects fail when executives become so enthused by results that they forget to ask themselves, ?Does this make sense in terms of what I expected??

Beware First Impressions

Big Data is just that. Many bits of information aggregated into averages and summaries. It does not make recommendations. It only prompts questions and what-if?s. Overlooking the need for the analytics that must follow can have you blindly relying on algorithms while setting your business sense aside.

Hire the Best Brains

Big Data?s competitive advantage depends on what human minds make with the processed information it spits out. This means tracing and affording creative talent able to make the shift from reactive analytics to proactive interaction with the data, and the customer decisions behind it.

If this provides a d?j? vu moment then you are not alone. Every iteration of the software revolution has seen vendors selling while the fish were running, and buyers clamouring for the opportunity. Decide what you want out first, use clean data, beware first impressions and get your analytics right. Then you are on the way to migrating successfully from CRM to Big Data.

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