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Hunting The Longtail: Five Steps To Solve Unplanned Downtime

Joseph Kenny is Vice President, Global Customer Transformation at ServiceMax.

How do organisations get to the root of annoying maintenance problems and tackle the troublesome reality of a loss in productivity?

There are some unsubstantiated stats doing the rounds at the moment, on the cost of unplanned downtime for manufacturers. These stats are putting average losses at over £1 trillion but while this is difficult to really measure, all organisations will have their own pain points where downtime is costing the business. What the actual cost is will vary but what is clear is that in this age of the “Digital Thread” – IoT sensors, advanced cloud-based infrastructures, AI/ML-driven automation and analytics, and a feedback loop to product engineering, we have the tools to try and solve problems that lead to downtime.

Below are five steps to help track down persistent maintenance issues:

Measure Those Assets

To really get to grips with machine performance and to try and understand error rates, it is essential that asset data is captured and analysed. One way is to leverage the concept of a “Digital Twin” (the expected performance of an asset in the field) to its actual performance via IoT devices and sensors installed on the asset itself – this year IoT is expected to see significant growth, according to Verdict research. The global IoT market will be worth a whopping $650bn by the end of this year, as organisations recognise the value of machine and device intelligence in real time.

“Advances to the industrial internet will be accelerated through increased network agility, integrated AI and the capacity to deploy, automate, orchestrate and secure diverse use cases at hyperscale,” says an Ericsson report The Future of IoT.  “The potential is not just in enabling billions of devices simultaneously but leveraging the huge volumes of actionable data which can automate diverse business processes.”  By comparing how an asset should be performing to how it is actually performing, we gain insights into what that assets maintenance needs are and provide critical data to product engineering to continuously improve the asset design.

This is key to optimising not just service capabilities but to extend the asset uptime, service life, and in the process increase customer productivity.

Unify The Data

Gartner suggests that poor data quality costs organisations $12.9 million every year adding that, over the long term, it “increases the complexity of data ecosystems and leads to poor decision making.”  A big driver of poor data quality is errors in the transcription of data and having multiple different procedures for data collection. Automated IOT data collection reduces the opportunity for manually inserting errors and standardizes the process for data collection simplifying data aggregation and utilization.

Another issue is data silos. Organisations need to unify and standardize their data to provide a comprehensive picture of the business, assets and customers.  This will help leaders make informed decisions on end-of-life products or consistently under-performing products and then deliver ideas to customers on how to improve uptime and productivity.

It also enables organisations to plan, ensuring optimisation and profitability through a complete and accurate picture of customer contracts, renewals and upgrades. As Deloitte suggested in its report Next Generation Customer Service: The Future of Field Service, to transform to next generation field service, businesses need a 360-degree view of both customers and their assets.

Predict Failures

AI/ML-enabled analytics, leveraging a Digital Twin and the Digital Thread (from product design, through engineering, manufacture, installation, and maintenance) can deliver predictive maintenance capabilities, to identify potential problems with machines and devices before there is a failure. As organisations move towards more outcome-based arrangements with customers, having SLAs that guarantee uptime, for example, will demand real-time analytics capabilities and rapid execution of maintenance delivery when a problem is indicated.

Asset intelligence is central to prediction. Comparing actual asset performance data to the Digital Twin for that asset, leveraging AI automation and analytics to identify potential issues, finding and ordering components or parts, and despatching maintenance teams to deliver services before a failure occurs. As well as extending the life of existing assets, this removes the fear of unplanned downtime and identifies those potential long tail service issues before they become long tails.

All of this drives increased customer satisfaction as well as the opportunity to grow contract revenue for manufacturers and value for customers.

Optimise Maintenance Teams

According to a report on transforming field service with emerging technologies, customer-centricity and personalization (68%); face-to-face or in-person field service appointments (55%); and leveraging service technicians as brand representatives or salespeople (51%) will become more important over the next three years. The point is that the service team is changing, and access to accurate and timely data is making it all possible.

Using the Digital Thread, accessing asset data, predictive analytics and optimised supply chain deliveries and inventory, organisations can reduce costs, and wasted journey times for engineers, through optimised service work. By reduce truck rolls, organisations can save time and money as well as improve service efficiency for customers.

Engage Product Design Teams

With any longtail service problem – traditionally this has been down to poor product or machine design leading to persistent maintenance issues – organisations now have the intelligence to understand the condition of the asset in the field, find the areas that require reengineering, and react proactively rather than reactively.

Assets can deliver qualitative data to organisations which should be fed back to engineering, design, and development teams to solve recurring product issues. With unified data, real-time asset data and AI automation and analytics, this should close the loop between product lifecycle management and service lifecycle management.

As a PwC report on The importance of the circular economy in manufacturing claimed, bringing the concept of the circular economy to life within the manufacturing value chain “involves substantial changes in core production and supply chain processes,” which would also require a “reverse logistics process to get the used products back into the cycle.”

This makes sense. By focusing on the entire lifecycle of a product (design, engineering, manufacture, and service) and not just its recyclability, manufacturers can start to shift their thinking towards longer product life and efficiencies in service provision, putting an end to the longtail service issues and getting to the root of unplanned downtime problems.

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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.


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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