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Building a Greener Web: Six Ways to Put Your Website on an Emissions Diet

By Roberta Haseleu, Practice Lead Green Technology at Reply, Fiorenza Oppici, Live Reply, and Lars Trebing, Vanilla Reply

Most people are unaware or underestimate the impact of the IT sector on the environment. According to the BBC: “If we were to rather crudely divide the 1.7 billion tonnes of greenhouse gas emissions estimated to be produced in the manufacture and running of digital technologies between all internet users around the world, it would mean each of us is responsible for 414kg of carbon dioxide a year.” That’s equivalent to 4.7bn people charging their smartphone 50,000 times.

Every web page produces a carbon footprint that varies depending on its design and development. This must be more closely considered as building an energy efficient website also increases loading speeds which leads to better performance and user experience.

Following are six practical steps developers can take to reduce the environmental impact of their websites.

  • Implement modularisation

With traditional websites that don’t rely on single page apps, each page and view of the site is saved in individual html files. The code only runs, and the data is only downloaded, for the page that the user is visiting, avoiding unnecessary requests. This reduces transmitted data volume and saves energy.

However, this principle is no longer the standard in modern web design which is dominated by single page apps which dynamically display all content to the user at runtime. This approach is easier and faster to code and more user-friendly but, without any precautions, it creates unnecessary overheads. In the worst case, accessing the homepage of a website may trigger the transmission of the entire code of the application, including parts that may not be needed.

Modularisation can help. By dividing the code of a website into different modules, i.e. coherent code sections, only the relevant code is referenced. Using modules offers distinct benefits: they keep the scope of the app clean and prevent ‘scope creeps’; they are loaded automatically after the page has been parsed but before the Document Object Model (DOM) is rendered; and, most importantly for green design, they facilitate ‘lazy loading’.

  • Adopt lazy loading

The term lazy loading describes a strategy of only loading resources at the moment they are needed. This way, a large image at the bottom of the page will not be loaded unless the user scrolls down to that section.

If a website only consists of a routing module and an app module which contain all views, the site will become very heavy and slow at first load. Smart modularisation, breaking down the site into smaller parts, in combination with lazy loading can help to load only the relevant content when the user is viewing that part of the page.

However, this should not be exaggerated either as, in some instances, loading each resource only in the last moment while scrolling can annihilate performance gains and result in higher server and network loads. It’s important to find the right balance based on a good understanding of how the app will be used in real life (e.g. whether users will generally rather continue to the next page after a quick first glance, or scroll all the way down before moving on).

  • Monitor build size

Slimming website builds is possible not only at runtime but also at a static level. Typically, a web app consists of a collection of different typescript files. To build a site and compile the code from typescript to JavaScript, a web pre-processor is used.

Pre-processors come with the possibility to prevent a build to complete if its files are bigger than a variable threshold. Limits can be set both for the main boot script as well as the single chunks of CSS to be no bigger than a specific byte size after compilation. Any build surpassing those thresholds fails with a warning.

If a build is suspiciously big, a web designer can inspect it and identify which module contributes the most, as well as all its interdependencies. This information allows the programmer to optimise the parts of the websites in question.

  • Eliminate unused code

One potential reason for excessive build sizes can be dozens of configuration files and code meant for scenarios that are never needed. Despite never being executed, this code still takes up bandwidth, thereby consuming extra energy.

Unused parts can be found in own source code but also (and often to a greater extent) in external libraries used as dependencies. Luckily, a technique called ‘tree shaking’ can be used to analyse the code and mark which parts are not referenced by other portions of the code.

Modern pre-processors perform ‘tree shaking’ to identify unused code but also to exclude it automatically from the build. This allows them to package only those parts of the code that are needed at runtime – but only if the code is modularised.

  • Choose external libraries wisely

One common approach to speed up the development process is by using external libraries. They provide ready-to-use utilities written and tested by other people. However, some of these libraries can be unexpectedly heavy and weigh your code down.

One popular example is Moment.js, a very versatile legacy library for handling international date formats and time zones. Unfortunately, it is also quite big in size. Most of all, it is neither very compatible with the typical TypeScript world nor is it modular. This way, also the best pre-processors cannot reduce the weight that it adds to the code by means of ‘tree shaking’.

  • Optimise content

Designs can also be optimised by avoiding excessive use of images and video material. Massive use of animation gimmicks such as parallax scrolling also has a negative effect. Depending on the implementation, such animations can massively increase the CPU and GPU load on the client. To test this, consider running the website on a 5 to 10-year-old computer. If scrolling is not smooth and/or the fans jump to maximum speed, this is a very good indication of optimisation potential.

The amount of energy that a website consumes — and thus its carbon footprint — depends, among other factors, on the amount of data that needs to be transmitted to display the requested content to users. By leveraging the six outlined techniques above, web designers can ‘slim’ their websites and contribute to the creation of a more sustainable web whilst boosting performance and user experience in the process.

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Driving business success in today’s data-driven world through data governance

Source: Finance derivative

Andrew Abraham, Global Managing Director, Data Quality, Experian

It’s a well-known fact that we are living through a period of digital transformation, where new technology is revolutionising how we live, learn, and work. However, what this has also led to is a significant increase in data. This data holds immense value, yet many businesses across all sectors struggle to manage it effectively. They often face challenges such as fragmented data silos or lack the expertise and resources to leverage their datasets to the fullest.

As a result, data governance has become an essential topic for executives and industry leaders. In a data-driven world, its importance cannot be overstated. Combine that with governments and regulatory bodies rightly stepping up oversight of the digital world to protect citizens’ private and personal data. This has resulted in businesses also having to comply e with several statutes more accurately and frequently.

We recently conducted some research to gauge businesses’ attitudes toward data governance in today’s economy. The findings are not surprising: 83% of those surveyed acknowledged that data governance should no longer be an afterthought and could give them a strategic advantage. This is especially true for gaining a competitive edge, improving service delivery, and ensuring robust compliance and security measures.

However, the research also showed that businesses face inherent obstacles, including difficulties in integration and scalability and poor data quality, when it comes to managing data effectively and responsibly throughout its lifecycle.

So, what are the three fundamental steps to ensure effective data governance?

Regularly reviewing Data Governance approaches and policies

Understanding your whole data estate, having clarity about who owns the data, and implementing rules to govern its use means being able to assess whether you can operate efficiently and identify where to drive operational improvements. To do that effectively, you need the right data governance framework. Implementing a robust data governance framework will allow businesses to ensure their data is fit for purpose, improves accuracy, and mitigates the detrimental impact of data silos.

The research also found that data governance approaches are typically reviewed annually (46%), with another 47% reviewing it more frequently. Whilst the specific timeframe differs for each business, they should review policies more frequently than annually. Interestingly, 6% of companies surveyed in our research have it under continual review.

Assembling the right team

A strong team is crucial for effective cross-departmental data governance.  

The research identified that almost three-quarters of organisations, particularly in the healthcare industry, are managing data governance in-house. Nearly half of the businesses surveyed had already established dedicated data governance teams to oversee daily operations and mitigate potential security risks.

This strategic investment highlights the proactive approach to enhancing data practices to achieve a competitive edge and improve their financial performance. The emphasis on organisational focus highlights the pivotal role of dedicated teams in upholding data integrity and compliance standards.

Choose data governance investments wisely

With AI changing how businesses are run and being seen as a critical differentiator, nearly three-quarters of our research said data governance is the cornerstone to better AI. Why? Effective data governance is essential for optimising AI capabilities, improving data quality, automated access control, metadata management, data security, and integration.

In addition, almost every business surveyed said it will invest in its data governance approaches in the next two years. This includes investing in high-quality technologies and tools and improving data literacy and skills internally.  

Regarding automation, the research showed that under half currently use automated tools or technologies for data governance; 48% are exploring options, and 15% said they have no plans.

This shows us a clear appetite for data governance investment, particularly in automated tools and new technologies. These investments also reflect a proactive stance in adapting to technological changes and ensuring robust data management practices that support innovation and sustainable growth.

Looking ahead

Ultimately, the research showed that 86% of businesses recognised the growing importance of data governance over the next five years. This indicates that effective data governance will only increase its importance in navigating digital transformation and regulatory demands.

This means businesses must address challenges like integrating governance into operations, improving data quality, ensuring scalability, and keeping pace with evolving technology to mitigate risks such as compliance failures, security breaches, and data integrity issues.

Embracing automation will also streamline data governance processes, allowing organisations to enhance compliance, strengthen security measures, and boost operational efficiency. By investing strategically in these areas, businesses can gain a competitive advantage, thrive in a data-driven landscape, and effectively manage emerging risks.

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The Benefits of EV Salary Sacrifice: A Guide for Employers and Employees

As the UK government continues to push for greener initiatives, electric cars have become increasingly popular. The main attraction for both employers and employees is the EV salary sacrifice scheme.

By participating in an EV salary sacrifice scheme, both employers and employees can enjoy cost savings and contribute to environmental sustainability along the way! This article will delve into the specifics of how these schemes operate, the financial advantages they offer, and the broader positive impacts on sustainability.

We will provide a comprehensive overview of the mechanics behind EV salary sacrifice schemes and discuss the various ways in which they benefit both employees and employers, ultimately supporting the transition to a greener future in the UK.

What is an EV Salary Sacrifice Scheme?

An EV salary sacrifice scheme is a flexible financial arrangement that permits employees to lease an EV through their employer. The key feature of this scheme is that the leasing cost is deducted directly from the employee’s gross salary before tax and National Insurance contributions are applied. By reducing the taxable income, employees can benefit from substantial savings on both tax and National Insurance payments. This arrangement not only makes EVs more affordable for employees but also aligns with governmental incentives to reduce carbon emissions.

For employers, implementing an EV salary sacrifice scheme can lead to cost efficiencies as well. The reduction in National Insurance contributions on the employee’s reduced gross salary can offset some of the costs associated with administering the scheme. Additionally, such programmes can enhance the overall benefits package offered by the employer, making the company more attractive to prospective and current employees.

Benefits for Employees

1. Tax and National Insurance Savings

By opting for an EV salary sacrifice scheme, employees can benefit from reduced tax and National Insurance contributions. Since the lease payments are made from the gross salary, the taxable income decreases, resulting in substantial savings.

2. Access to Premium EVs

Leading salary sacrifice car schemes often provide access to high-end electric vehicles that might be otherwise unaffordable. Employees can enjoy the latest EV models with advanced features, contributing to a more enjoyable and environmentally friendly driving experience.

3. Lower Running Costs

Electric vehicles typically have lower running costs compared to traditional petrol or diesel cars. With savings on fuel, reduced maintenance costs, and exemptions from certain charges (such as London’s Congestion Charge), employees can enjoy significant long-term financial benefits.

4. Environmental Impact

Driving an electric vehicle reduces the carbon footprint and supports the UK’s goal of achieving net-zero emissions by 2050. Employees can take pride in contributing to a cleaner environment.

Benefits for Employers

1. Attract and Retain Talent

Offering an EV salary sacrifice scheme can enhance an employer’s benefits package, making it more attractive to potential recruits. It also helps in retaining current employees by providing them with valuable and cost-effective benefits.

2. Cost Neutrality

For employers, EV salary sacrifice schemes are often cost-neutral. The savings on National Insurance contributions can offset the administrative costs of running the scheme, making it an economically viable option.

3. Corporate Social Responsibility (CSR)

Implementing an EV salary sacrifice scheme demonstrates a commitment to sustainability and corporate social responsibility. This can improve the company’s public image and align with broader environmental goals.

4. Employee Well-being

Providing employees with a cost-effective means to drive electric vehicles can contribute to their overall well-being. With lower running costs and the convenience of driving a new EV, employees may experience reduced financial stress and increased job satisfaction.

How to Implement an EV Salary Sacrifice Scheme

1. Assess Feasibility

Evaluate whether an EV salary sacrifice scheme is feasible for your organisation. Consider the number of interested employees, potential cost savings, and administrative requirements.

2. Choose a Provider

Select a reputable provider that offers a range of electric vehicles and comprehensive support services. Ensure they can handle the administrative tasks and provide a seamless experience for both the employer and employees.

3. Communicate the Benefits

Educate your employees about the advantages of the scheme. Highlight the financial savings, environmental impact, and access to premium EV models. Provide clear guidance on how they can participate in the programme.

4. Monitor and Review

Regularly review the scheme’s performance to ensure it continues to meet the needs of your employees and the organisation. Gather feedback and make adjustments as necessary to enhance the programme’s effectiveness.

Conclusion

The EV salary sacrifice scheme offers a win-win situation for both employers and employees in the UK. With significant financial savings, access to premium vehicles, and a positive environmental impact, it’s an attractive option for forward-thinking organisations. By implementing such a scheme, employers can demonstrate their commitment to sustainability and employee well-being, while employees can enjoy the benefits of driving an electric vehicle at a reduced cost.

Adopting an EV salary sacrifice scheme is a step towards a greener, more sustainable future for everyone.

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Machine Learning Interpretability for Enhanced Cyber-Threat Attribution

Source: Finance Derivative

By: Dr. Farshad Badie,  Dean of the Faculty of Computer Science and Informatics, Berlin School of Business and Innovation

This editorial explores the crucial role of machine learning (ML) in cyber-threat attribution (CTA) and emphasises the importance of interpretable models for effective attribution.

The Challenge of Cyber-Threat Attribution

Identifying the source of cyberattacks is a complex task due to the tactics employed by threat actors, including:

  • Routing attacks through proxies: Attackers hide their identities by using intermediary servers.
  • Planting false flags: Misleading information is used to divert investigators towards the wrong culprit.
  • Adapting tactics: Threat actors constantly modify their methods to evade detection.

These challenges necessitate accurate and actionable attribution for:

  • Enhanced cybersecurity defences: Understanding attacker strategies enables proactive defence mechanisms.
  • Effective incident response: Swift attribution facilitates containment, damage minimisation, and speedy recovery.
  • Establishing accountability: Identifying attackers deters malicious activities and upholds international norms.

Machine Learning to the Rescue

Traditional machine learning models have laid the foundation, but the evolving cyber threat landscape demands more sophisticated approaches. Deep learning and artificial neural networks hold promise for uncovering hidden patterns and anomalies. However, a key consideration is interpretability.

The Power of Interpretability

Effective attribution requires models that not only deliver precise results but also make them understandable to cybersecurity experts. Interpretability ensures:

  • Transparency: Attribution decisions are not shrouded in complexity but are clear and actionable.
  • Actionable intelligence: Experts can not only detect threats but also understand the “why” behind them.
  • Improved defences: Insights gained from interpretable models inform future defence strategies.

Finding the Right Balance

The ideal model balances accuracy and interpretability. A highly accurate but opaque model hinders understanding, while a readily interpretable but less accurate model provides limited value. Selecting the appropriate model depends on the specific needs of each attribution case.

Interpretability Techniques

Several techniques enhance the interpretability of ML models for cyber-threat attribution:

  • Feature Importance Analysis: Identifies the input data aspects most influential in the model’s decisions, allowing experts to prioritise investigations.
  • Local Interpretability: Explains the model’s predictions for individual instances, revealing why a specific attribution was made.
  • Rule-based Models: Provide clear guidelines for determining the source of cyber threats, promoting transparency and easy understanding.

Challenges and the Path Forward

The lack of transparency in complex ML models hinders their practical application. Explainable AI, a field dedicated to making models more transparent, holds the key to fostering trust and collaboration between human and machine learning. Researchers are continuously refining interpretability techniques, with the ultimate goal being a balance between model power and decision-making transparency.

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