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CFO 4.0: How to make your CFO passionate about marketing

Source: Finance Derivative

By Damien Bennett, Global Director of Product, Strategy and Growth, Incubeta

Can you keep something secret for me? I’ve been ‘helping out’ both the marketing and finance departments, telling one department one thing and the other something different. Ultimately the goal is the same, but how we get there is a different strategy entirely.

In the modern world of business, no two departments are closer, and yet further apart, than marketing and finance. Both are ultimately working towards the same goal, which is growing their business, yet neither fully trusts the other to achieve this. Marketing thinks that finance doesn’t understand the complexities of what they do, whilst finance thinks marketing is wasting money by not putting enough pressure on ROI.

I might be generalizing, and I’m sure this isn’t always the case, but I also feel like many of the readers of this article who work in finance or marketing will recognize this sentiment to be familiar.

As with many arguments, neither side is right or wrong. It is true that finance teams can be guilty of taking reported numbers as truth, rather than indicators of truth, and in their haste to support new campaigns, marketers can be guilty of not putting enough pressure on understanding why something worked, or why it didn’t.

But the two departments need to find a way to work together as friends, rather than foes. Marketing teams can provide a huge amount of insight to finance that supports reporting, budgeting, and forecasting, whilst finance teams can help marketers to better understand the impact their campaigns have on business performance.

Why sharing the load can simplify everything

Never has this been more true than it is today. Marketing teams have more tools to align their campaigns to business drivers than ever before. For example, at Incubeta we can run campaigns that account for the profit margin of a product, whether an item is in or out of stock, and automatically respond to price competitiveness.

Yet finance teams don’t know we can do all these smart things. They don’t understand how deep we can go with regard to understanding performance, or how we can use advanced algorithms, machine learning, and automation to make intelligent, data-led decisions about where marketing investment is spent. And we can’t blame them for not knowing, because we haven’t told them.

I have found, in my personal experiences, that as soon as you explain the science behind digital marketing to finance teams their ears prick up. They are genuinely blown away by how much effort goes into ensuring that every dollar of the budget is spent as effectively as possible, and they are usually keen to learn more.

The opportunities for these meetings can’t exist, however, unless we double down on our efforts to collaborate. One of my first requests for any new client is that we hold a session that brings stakeholders from a variety of our client’s departments together in the same room. In that session, we explain what we’re trying to achieve, the challenges that we’re looking to overcome and the specifics of what we are doing, when we’re doing them and the impact we expect them to have. This means there is no ambiguity or mis-alignment – everyone is on the same page.

Open lines of communication is the key to success

As digital marketing becomes increasingly sophisticated, amid the prevalence of data and the shifting sands brought about by increased regulation, it’s crucial to ensure all business departments are kept up-to-date and aware of what’s changing, and what it will mean for their business. How an organization reports performance from marketing channels is a critical component of this. Most businesses started reporting performance by accrediting the value of a sale to the final touchpoint a customer had with marketing before they buy. Nowadays, we know there are much more accurate ways of measuring performance, using models that ensure we consider all touchpoints, the customer-type that was buying and how they made their purchase.

Yet even though we know there are better ways of measuring performance, many organizations still struggle to move away from the less accurate models of the past. This often brings them to a disappointing acceptance that things can’t change. However, if marketing teams were to guide finance teams through this process, we can leave the models of the past behind us. Consider implementing a solution that allows finance to update their internal reports and include the new measurement model side-by-side with the old. This will stop the business from losing visibility of the year-on-year before the old model is phased out for the new.

Promoting a strong inter-departmental relationship 

Running regular workshops with both the marketing team and the finance team present have been a great victory for us as an organization. We use these workshops to discuss challenges, explain industry developments and to outline new initiatives that we are looking to launch. Ironically, the outcome of these meetings is that the finance team will often thank us for helping the marketing team to be more focused on business performance, whilst the marketing team will thank us for helping the finance team to see how smart they are in their planning and activation. Some might say I have become something of an expert in playing along with both sides.

Ultimately, the most important factor is the results. Once a marketing team and finance team have joined forces towards achieving a goal, they are unstoppable. A strong inter-departmental relationship between marketing and finance teams will allow both to help reach a collective end goal. The teams can collaborate together to discuss what they need to do to achieve this goal and if their strategy is working or not.

To achieve this, if I need to occasionally tell both teams something different, then so be it.

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Business

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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Business

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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