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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The Future is Smarter with a Smart Meter

Traditionally, electricity and water meter consumption was measured via analogue meters. Utility billing was based on actual consumption units obtained from the meter by meter readers. This entailed physical visits to the metering point. Lots of challenges came with meter reading; talk of customers feeling their privacy is intruded, meter readers encountering hostile customers, dogs, closed gates. The result was estimated bills that were most often than not very high.

Smart meters can be dubbed as the ?next generation? type of meters. Smart meters send wireless electronic meter readings to one?s energy supplier automatically. There are both gas smart meters and electricity smart meters. Smart meters come with in-home displays, which give someone real-time feedback on their energy usage and the associated cost.

Smart meters communicate meter readings directly to utility companies therefore no one has to come to your home to read your meter; and neither are you required to submit meter readings yourself. This not only reduces costs, but leads to more accurate electricity bills practically eliminating estimated bills. Smart meters signal the end of estimated bills, and the end of overpaying or underpaying for energy.

Whereas a smart meter in itself does not save you money, the add-ons (in-home displays) that come with the smart meters and which give someone real-time feedback on their energy usage helps them to reduce the unnecessary energy use and this ultimately leads to better oversight into how to lower utility bills hence better management of one?s energy use.

In summary, a smart meter is a technology that enables energy consumers to see their energy as they use it, a technology where energy is displayed as it is being used and wireless ratings sent. Adoption of smart meters would mean the end of estimated energy bills.

Smart meters are also promising a smart future where all energy consuming devices can be connected to the internet and centrally controlled using computers or smartphones. This means one is able to switch off lights and other energy consuming devices from a central point, hence make savings and this will enable them to have greater control of their energy use, hence more comfort, convenience and life will be cheaper for all. This is the smarter future we are all looking forward to.

Spreadsheet Woes – Burden in SOX Compliance and Other Regulations

End User Computing (EUC) or end User Developed Application (UDA) systems like spreadsheets used to be ideal ad-hoc solutions for data processing and financial reporting. But those days are long gone.

Today, due to regulations like the:

  • Sarbanes-Oxley (SOX) Act,
  • Dodd-Frank Act,
  • IFRS (International Financial Reporting Standards),
  • E.U. Data Protection Directive,
  • Basel II,
  • NAIC Model Audit Rules,
  • FAS 157,
  • yes, there?s more ? and counting

a company can be bogged down when it tries to comply with such regulations while maintaining spreadsheet-reliant financial and information systems.

In an age where regulatory compliance have become part of the norm, companies need to enforce more stringent control measures like version control, access control, testing, reconciliation, and many others, in order to pass audits and to ensure that their spreadsheets are giving them only accurate and reliable information.

Now, the problem is, these control measures aren’t exactly tailor-made for a spreadsheet environment. While yes, it is possible to set up a spreadsheet and EUC control environment that utilises best practices, this is a potentially expensive, laborious, and time-consuming exercise, and even then, the system will still not be as foolproof or efficient as the regulations call for.

Testing and reconciliation alone can cost a significant amount of time and money to be effective:

  1. It requires multiple testers who need to test spreadsheets down to the cell level.
  2. Testers will have to deal with terribly disorganized and complicated spreadsheet systems that typically involve single cells being fed information by other cells in other sheets, which in turn may be found in other workbooks, or in another folder.
  3. Each month, an organisation may have new spreadsheets with new links, new macros, new formulas, new locations, and hence new objects to test.
  4. Spreadsheets rarely come with any kind of supporting documentation and version control, further hampering the verification process.
  5. Because Windows won’t allow you to open two Excel files with the same name simultaneously and because a succession of monthly-revised spreadsheets separated by mere folders but still bearing the same name is common in spreadsheet systems, it would be difficult to compare one spreadsheet with any of its older versions.

But testing and reconciliation are just two of the many activities that make regulatory compliance terribly tedious for a spreadsheet-reliant organisation. Therefore, the sheer intricacy of spreadsheet systems make examining and maintaining them next to impossible.

On the other hand, you can’t afford not to take these regulations seriously. Non-compliance with regulatory mandates can have dire consequences, not the least of which is the loss of investor confidence. And when investors start to doubt the management’s capability, customers will start to walk away too. Now that is a loss your competitors will only be too happy to gain.

Learn more about our server application solutions and discover a better way to comply with regulations.

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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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