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Planning for success: four tips for reaching business goals

Source: Finance Derivative

Patrick Ross, Relationships Manager at ActiveOps discusses the four points every frontline manager needs to master to ensure business needs are met for a safer, more resilient future.

Businesses know that agility is the key to manoeuvring through the whirlwind of ever-changing demands in industry today. Seismic shifts in technology and our communities have influenced not only our work ethics but our positions on working from home, work-life balance, and global economies. So, it is no wonder that the secret to industry survival is to create step-by-step plans ensuring your teams are on track to meet their goals.

Managers at all levels should be meticulous in setting targets, assessing progress, and mapping their future course of action. This naturally includes how well planning delivers agility in the real world. A good plan will always contain specific goals and fundamental touchstones: where you are now, where you want to be, and how you will get there. Inevitably there will always be a need to deal with unexpected glitches, and these days, you can count on those goals you set to shift, often, at a moment’s notice.

So how do you manage to keep your business plan alive in the face of constantly shifting priorities, needs, and circumstances? It comes down to four key points:

  • Having the facts to backup goals – ensuring that key data points in the right direction of an obtainable goal.
  • Prioritising end goals – identifying the difference between short term and long-term objectives.
  • Involving all stakeholders at every stage of the planning process – ensuring that everyone on board is in tune with the desired results.
  • Capitalising on past successes – what worked in the past and can it be repeated in the future?

1: Build your plan with essential facts, not guess work

Establishing a level of output from a team along with an outline of typical figures and achievable timelines, needs to be at the heart of any business goal.

For example, a good plan will include how many widgets an employee should produce per hour, the average handle time for a customer call, or the delivery time for a standard order. With a complete understanding of the data, managers can justify the goals they set and understand the challenges to achieving them.

A good repository of data and a suite of analytics tools will help tremendously in goal creation and all stages of the process, from implementing changes and troubleshooting glitches to assessing what has been achieved in the past and what is likely to be achievable in the future.

It is a considerable effort to amass all that amount of information, particularly up front, but it’s undoubtedly worth the time. Firstly, it provides valuable insight into the challenges your colleagues face. Yet, at the same time, your data may uncover things you hadn’t even considered which might influence the path you want to take to achieve your goal. Yet your actions should be fluid meaning that while the plan is in action, any bit of crucial data will help inform any decisions you need to make when the unexpected occurs.

2: Prioritise the need first

Managers often short-change themselves and their teams when they create plans. They start from the perspective of what they think their team is capable of when instead they should start with what the business needs.

Lots can be possible if you focus on the goal and are realistic about the required effort, as well as be open to ways to make the goal worth achieving. After working out what would be required to attain your goal, you can consider workable solutions. Check to see if you can get help from other business areas. Find out if any work can be moved out to another date. Are there obstacles that could be addressed, such as technology or training? Even if you do not get the help you need, you will at least have a handle on what your challenges are – and for that matter, so will others.

3: Share your plan with others

One of the significant reasons plans fail is because someone or something comes out of nowhere with a perceived higher priority. In many cases, there is nothing you can do about it. Business needs can change in an instant, and you must make sure all departments have in place contingency plans in place to weather the storm.

You can mitigate harsh weather and even avoid it by making yourself heard. Your plans should not be kept secret. Share them with everyone, particularly those who have the power to impact them. Having conversations early and incorporating everyone’s needs will help guarantee more remarkable success.

And it’s essential not to forget about your team members. They need to be included from the start. This means getting their agreement on your proposed solution and understanding their role in making it successful. Employees will be more invested in executing a plan they helped build rather than one they were told to go with – particularly if they don’t agree with it.

Lastly, don’t forget to share your incremental results, too. Let everyone know about the progress, including successes and failures. By welcoming ongoing feedback, your plan will become front of mind for others, increasing the chances of it being successful.

4: Don’t forget to cash the cheque!

If you have done the work of detailing your plan, plotting out your activities and resources, and ensuring that all relevant parties are on board, you are going to start to see results. You need to reap the rewards of that effort.

If your efforts to speed up the process on an assembly line have worked, what are you doing as a result? Whatever it is, document it and brag mercilessly because a well-executed plan does not just deliver operational benefits – it also demonstrates your and your team’s competence.

In most cases, your plan will not deliver one significant result in the end. Instead, it is going to produce small wins throughout. Use these to your advantage. Share reports, speak up at meetings, even host a celebration. These anecdotes will help keep the enthusiasm alive among your partners as the plan continues to roll out.

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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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Technology

‘Aligning AI expectations with AI reality’

By Nishant Kumar Behl, Director of Emerging Technologies at OneAdvanced

AI is transforming the way we work now and will continue to make great strides into the future. In many of its forms, it demonstrates exceptional accuracy and a high rate of correct responses. Some people worry that AI is too powerful, with the potential to cause havoc on our socio-political and economic systems. There is a converse narrative, too, that highlights some of the surprising and often comical mistakes that AI can produce, perhaps with the intention of undermining people’s faith in this emerging technology.

This tendency to scrutinise the occasional AI mishap despite its frequent correct responses overshadows the technology’s overall reliability, creating an unfairly high expectation for perfection. With a singular focus on failure, it is, therefore, no surprise that almost 80% of AI projects fail within a year. Considering all of the hype around AI and particularly GenAI over the past few years, it is understandable that users feel short-changed when their extravagant expectations are not met.

We shouldn’t forget that a lot of the most useful software we all rely on in our daily working lives contains bugs. They are an inevitable and completely normal byproduct of developing and writing code. Take a look at the internet, awash with comments, forums, and advice pages to help users deal with bugs in commonly used Apple and Microsoft word processing and spreadsheet apps.

If we can accept blips in our workhorse applications, why are we holding AI to such a high standard? Fear plays a part here. Some may fear AI can do our jobs to a much higher standard than we can, sidelining us. No technology is smarter than humans. As technology gets smarter, it pushes humans to become smarter. When we collaborate with AI, the inputs of humans and artificial intelligence work together, and that’s when magic happens.

AI frees up more human time and lets us be creative, focusing on more fulfilling tasks while the technology does the heavy lifting. But AI is built by humans and will continue to need people asking the right questions and making connections based on our unique human sensibility and perception if it is to become more accurate, useful, and better serve our purpose.

The fear of failing to master AI implementation might be quite overwhelming for organisations. In some cases, people are correct in being cautious. There is a tendency now to expect all technology solutions to have integrated AI functionality for the sake of it, which is misguided. Before deciding on any technology, users must first identify and understand the problem they are trying to solve and establish whether AI is indeed the best solution. Don’t be blinded by science and adopt the whistles and bells that aren’t going to deliver the best results.

Uncertainty and doubt will continue to revolve around the subject of AI, but people should be reassured that there are many reliable, ethical technology providers developing safe, responsible, compliant AI-powered products. These organisations recognise their responsibility to develop products that offer long-term value rather than generating temporary buzz. By directly engaging with customers to understand their needs and problems, a customer-focused approach helps identify whether AI can effectively address the issues at hand before proceeding down the AI route.

In any organisation, the leader’s job is to develop strategy, ask the right questions, provide direction, and often devise action plans. When it comes to AI, we will all need to adopt that leadership mindset in the future, ensuring we are developing the right strategy, asking insightful questions, and devising an effective action plan that enables the engineers to execute appropriate AI solutions for our needs.

Organisations should not be afraid to experiment with AI solutions and tools, remembering that in every successful innovation, there will be some failure and frustration. The light bulb moments rarely happen overnight, and we must all adjust our expectations so that AI can offer a perfect solution. There will be bugs and problems, but the journey towards improvement will result in achieving long-term and sustainable value from AI, where everyone can benefit.

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Nishant Kumar Behl is Director of Emerging Technologies at OneAdvanced, a leading provider of sector-focussed SaaS software, headquartered in the UK.

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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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