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Five Tech Strategies In An Inflationary Environment

Simon Edwards is Chief Financial Officer for ServiceMax

UK Government turmoil, warnings from the IMF, rising inflation, the war in Ukraine and spiralling energy costs continue to have a big impact on the global economic outlook, compounding an already difficult overhang created by the COVID-19 pandemic. Meanwhile, central banks are scrambling to reverse the impact of over a decade of of fiscal stimulus, while talent shortages and logistics constraints are disrupting supply chains, resulting in multi-year inflation highs.

Gartner’s recent forecast shows CFOs believe technology is at the very heart of competitiveness and viability, with 78% planning to maintain or increase technology spend – even if inflation persists through 2023. Admittedly, it can be tempting to cut tech spend in the face of global retraction. Yet scaling back on digital transformation initiatives in the face of inflation is the wrong approach. Leading digital organisations have a lower cost of doing business and thus a significant competitive advantage in the current inflationary environment. There’s a strong argument that now is the time to invest in technology as it helps businesses to do more with less.

Technology is now so fundamental to enterprises that how CFOs and CIOs manage tech spend and implementation over the next year could significantly impact success or failure.

As the CFO remit expands, we can and should do more than simply keep the lights on in the face of current inflationary trends. I’ve outlined five key technology strategies below from the CFO perspective.

Workforce Empowerment and Enablement

More traditional CFOs may feel a little uneasy about the post-pandemic trend of hybrid working but the reality is that talent models are evolving quickly. Skills gaps in increasingly digital organisations can lead to inefficiencies and poor productivity. Understanding that a workforce, now attracting older, tech-savvy Gen Zers has a different view on how they want to work and how they use technology at work.

Every organisation will need to navigate working flexibility and empowerment, to address shortages but also to drive performance. Collaboration tools, remote access, cyber security and centralised customer and product data capabilities will be crucial to enabling employees regardless of location and role.

As McKinsey said recently in a blog; “successful organizations deliberately create a flexible culture by developing leaders who foster outcome-oriented performance, trust and togetherness, and team engagement and problem solving.” Insisting employees are physically in the office and working strict office hours runs counter to the realities of today’s reality.

Data Creation and Retention

Data is crucial to enterprise costs, value creation and staff retention. Whether it’s structured or unstructured, it is essential for understanding customers, managing supply chains and inventory and providing intelligence on the business and its key markets. Smart use of data models can lead to increased operational efficiencies, personalization of products and services and opportunities to unlock growth.

The challenge for CFOs is to understand where to focus. As futurist Bernard Marr suggests, every organisation needs to evaluate its data use through different lenses. Does your data help your firm improve evidence-based decision-making, understand your customers and markets better, enable smarter products and services, improve internal processes and add additional revenue by monetising data?

It’s also worth noting that for many CFOs, data problems lurk beneath the surface. Accuracy and compatibility Are paramount when it comes to measuring performance across different departments and sources. It’s a common problem and needs addressing before it does material harm to the business.

Scale the Cloud

As the cost of capital rises, increasingly CFOs want to be able to point to a near-term payback for net new investments. Organisations need to look to retire on-premises environments and scale their cloud operations based on demand to reduce capital expenditure burden and also to ensure the most bang for their buck.

The trend towards cloud infrastructure is well documented – research firm Forrester claims over half of European IT decision makers see cloud modernisation as a top priority – and the cost benefits, although occasionally criticised due to rising monthly costs, still outweigh the capital investment required for physical internal infrastructures. In short, scaling cloud investment makes a lot of sense for growing businesses or those looking to enable a more distributed workforce.

Software-as-a-Service is a Must

As McKinsey says in a recent article, “there’s a lot to like about SaaS. For one thing, the asset-light nature of SaaS means it has lower overhead and logistical costs than physical products. Second, it frees traditional companies from doing some of the most complicated technical work by themselves—they can partner with major cloud service providers. And third, since software at its core is a series of 1s and 0s, it’s easy to scale, especially when combined with the cloud’s extensibility and elasticity.”

Of course, it’s even better if that software is actually built for use in the cloud. Cloud-native applications can take full advantage of the scale, resiliency and flexibility the cloud provides. For CFOs looking to maximise impact for minimum outlay, that is surely a must.

Back-office Automation

It makes sense for any organisation, if not doing so already, to explore how automating some functions in the back office can help improve processes, cover skills gaps and increase efficiencies. Using technologies, such as Robotic Process Automation (RPA) and AI-enabled data analytics will provide enterprises with more intelligence for forecasting and planning, more efficient and transparent transactions and more effective use of employee skills and time. It should enable employees to focus on creating more business value.

Gartner’s Finance practice cites automation of back-office workflows as being key to achieving efficiency gains across finance areas, such as accounts payable, accounts receivable and internal IT services, such as helpdesk support. When margins are under pressure in a cash-constrained environment, the urgency to improve productivity in these areas is heightened even more.

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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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