What are the toughest challenges of leadership?

Business leaders around the globe face an array of internal and external burdens. The toughest challenges in leadership today include mounting pressure, strengthening communication and shaping corporate culture – and 57% of UK executives are facing a crisis in confidence.

With leadership spending nearly half of their time deliberating the needs of the business, critical decision-making is key. However, a quick-solutions approach often results in a misidentified problem, wasted resources and a back-pedalling effect which puts more stress on the internal infrastructure of the organisation. 

For any practising (or aspiring) business leader, it’s imperative to understand how to diagnose and dissolve the most pressing leadership problems.

What are the top five problems in leadership?

In 2020, the Center for Creative Leadership conducted a global study with 763 corporate leaders and found that businesses worldwide are facing the same top leadership challenges – regardless of industry, sector or organisational culture.

The report uncovered that the top five problems impacting leadership are:

  1. managerial effectiveness
  2. driving inspiration
  3. developing others
  4. guiding change
  5. managing relationships and politics

The report outlined a universal focus for managerial development across these areas.

Effectiveness

For aspiring leaders, developing the relevant skills for optimum managerial effectiveness is essential. These skills range from top-level strategic thinking to time management – staying up-to-speed with the ever-changing demands of a leadership role. However, this was the most frequently reported challenge for executives across China, India, and the United States.

Business heads steer the ship. They oversee the most critical elements of a business, from finances through to operations, and will regularly face challenges ranging from redundancies to misconduct. However, poor decision-making is one of the key contributing factors to ineffective leadership, and studies have shown a spike in decision paralysis post-pandemic. In fact, a percentage of UK business leaders now believe their decisions to be less effective. 

Poor decisions lead to slack execution. CEO Coaching International attributes a failure of due diligence to spending time and resources in the wrong place. Many business leaders struggle to comprehensively understand their staff roles, making delegation difficult – while an inability or aversion to interpreting data effectively can derail meaningful goal-setting.

Creating a disciplined plan, conducting thorough research and assembling the right team are the key components to successful execution. Gaining role clarity allows leaders to delegate more, deploy the right training initiatives and work on the tasks that maximise their own unique skill set. Effective annual planning and reporting also helps maintain company-wide precision and accountability, while clear and actionable goals create a more cohesive vision.

Driving inspiration

Without a clear plan or projection for the future, it’s difficult to motivate a team. Today’s executives are tasked with onboarding all stakeholders and creating a shared vision – but when you’re leading a team of varying experience levels and (sometimes conflicting) viewpoints, this can be challenging. 

With today’s teams comprising colleagues from numerous academic, educational, cultural and political backgrounds, the pressure is on for leaders to inspire on a collective and individual level. You’re only as strong as your workforce – and company growth, productivity, morale, and attendance suffer when staff feel disconnected and overlooked. But many business leaders struggle when it comes to effective communication.

This lack of coherence extends to company vision – and it’s difficult for any staff member to stay motivated without a clearly defined purpose to their role. With more millennials seeking employers that are both exciting and share their values and ethics, a lack of narrative could derail engagement, motivation and focus within the workforce.

Leaders need to lean in and commit to the process. Clarity and consistency are key, and it’s important to reinforce words with tangible actions. This breeds trust, while a transparent approach to conversation engenders respect. 

It’s important for employees to have a comprehensive understanding of their purpose and performance. Having an understanding of where their careers are headed will keep staff driving forward. Communication works both ways too, and leaders who are approachable and receptive to feedback are well placed to get the best out of their teams.

Equally, it’s important for leaders to communicate wins as much as areas to improve. Recognising achievements, developing quality incentives and offering staff flexibility boosts morale and keeps employees feeling valued.

Development

To stay effective and relevant, businesses need to constantly level-up. 

Companies that lack the finance or resource, or simply fail to prioritise training and development, risk losing staff. Offering professional development boosts employee engagement and attracts top talent. 

Leaders should take an active role in mentoring, coaching and developing others. Promoting employees to upper management, or creating new roles to further professional development have significant impact – reaching as far as increasing profitability. Enabling and encouraging good internal communication fosters skills sharing between junior and senior-level stakeholders too.

Guiding change

Managing and mobilising change isn’t easy – and it’s one of the biggest challenges facing UK leaders. Companies need to be agile and adaptable to succeed, but mitigating the consequences of change is a balancing act.

A huge hurdle to change is team resistance – and often this comes from a lack of communication from leadership. Staff need clearly defined strategies to navigate change – but also an understanding of why these changes are happening in the first place. A closed-door approach builds resentment; employees want to be consulted about what directly impacts their role or environment. 

Change-capable leadership clearly communicates the purpose and value of change, in alignment with organisational goals. It fosters collaborative decision-making, directly involves stakeholders in the execution of plans and embraces emotional reactions to change.

Managing stakeholder relationships and politics

Executive alignment is essential, and can make or break modern businesses. Creating a unified force is a key part of leadership success. It’s important to establish an environment where staff support one another – and this extends to leadership. 

When a decision is made, executives need their senior team to be behind it. Business leaders that struggle to account for this may run into roadblocks. Navigating workplace conflict, establishing team norms and remaining politically savvy are some of the key demands in this area.

What are some emerging issues in leadership?

Redefining how people work in a post-pandemic world has been the biggest corporate challenge of recent times. Business leaders are confronting new approaches to crisis management and strategic resilience, while accommodating flexible working schedules and developing new staff wellbeing initiatives. 

Ultimately, it would appear that agility, adaptability and cohesion could be some of the biggest emerging battles that businesses will face.

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AI search and recommendation algorithms

Powered by artificial intelligence (AI), search and recommendation algorithms shape our interaction (and satisfaction) with online platforms. Developed to predict user choices, preferences and behaviours, its purpose is to improve the overall user experience of websites, apps, smart assistants and other types of computer programmes.

From Google to Amazon and Netflix, today’s biggest online retailers and service providers are making use of this class of machine learning to improve business conversion and retention rates: pushing products, boosting repeat sales and keeping customers happy and engaged.

How do search and recommendation algorithms work?

Where search algorithms work to retrieve relevant data, information and services in reaction to a user query, recommendation algorithms suggest similar alternate results based upon the user’s search (or purchase) history. 

Put simply, search algorithms assist users in finding exactly what they want, while recommendation algorithms help users find more of what they like.

Search algorithms

A search algorithm locates specific data within a larger collection of data. According to Internet Live Stats, on average, Google processes over 100,000 search queries per second. That’s an immense demand on a system, but while 98% of all internet users frequent a search engine monthly, it’s imperative that they be built to produce accurate results quickly and efficiently. 

Basic site-search has quickly become an essential feature of almost any website, while the search function is considered a fundamental procedure in computing overall (extending to coding, development and data science). Intended to keep all types of users happy and informed, search algorithms step in to get the right resources in front of the right people.

All search algorithms operate via a search key (or bar) and work by returning a success or failure status based on the entered information. They break a query down into separate words, and using text-matching, link those words to matching titles and descriptions in the data sets. 

Different search algorithms vary in terms of performance and efficiency, depending on how they are used and the available data. Some of the more commonly used search algorithms include:

  • linear search algorithm
  • binary search algorithm
  • depth-first search algorithm
  • breadth-first search algorithm

More complex algorithms can identify typing and auto-correct mistakes, as well as offering synonym recognition. Advanced algorithms can produce more refined results, factoring in popular answers, product rankings and other key metrics.

Google’s search algorithm

Google attributes its success to meticulous testing and complex search experiments. A combination of its infamous crawling “spider” bots, data-driven indexing and rigorous ranking system enables the search engine to meet its exemplary standards of relevance and quality. 

Analysing everything from sitemap to content, images and URLs used, Google is able to identify the best pages to signpost your query in a fraction of a second. The search engine even boasts a freshness algorithm that, in response to trending topics and keywords, shows users the most up-to-date online articles available in realtime. 

Good search engines boast another important feature: related results. This can make the difference between a bounce and purchase as customers are encouraged to keep browsing the site. This is where recommendation algorithms become useful.

Recommendation algorithms

Recommendation algorithms rely on data science to filter and recommend personalised suggestions (whether that be related search results or product recommendations) based on a user’s previous actions. Recommendation algorithms can generally be separated into two types: content-based filtering and collaborative filtering.

Content-based filtering

These algorithms factor in information (such as keywords and attributes) of both the user and the chosen item or product profile to generate recommendations. By utilising the customer’s personal data (such as gender, occupation and more), content-based filtering algorithms are able to assess popular products by age-group or locale, for example. Similarly, by analysing the product characteristics, the system can recommend other items with similar attributes. The more people that use the platform, the more data can be mined and improve the specificity of the suggestions.

The Netflix recommendation engine

Netflix states that recommendation algorithms are at the core of its product. In fact, 80% of viewer activity is driven by personalised recommendations from its engine. Netflix began experimenting with data as early as 2006 to improve the accuracy of its preference algorithms. As a result, the Netflix recommendation engine tracks numerous data points, from browsing behaviours to binge-watching habits, and filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. The platform has taken data beyond rating prediction and into personalised ranking, page generation, search, image selection, messaging, marketing, and more.

Collaborative filtering

These algorithms accumulate data from all users on a platform and work like a word-of-mouth recommendation. By comparing datasets, such as purchase or rating information, the algorithms help the platform identify kindred customer profiles and recommend other products or services favoured by these ‘similar users’.  

InData Labs notes the greatest merits of collaborative filtering systems as:

  • capability of accurately recommending complex items (such as films, books or clothing) without requiring an “understanding” of the item itself
  • basing recommendations on more personalised ‘similar’ users, without needing a more comprehensive knowledge of all products or all users of the platform
  • ability to be applied to any domain and provide more versatile cross-domain recommendations

Why are search and recommendation algorithms so essential?

The number of digital purchases continues to climb each year, cementing e-commerce as an indispensable function of the global retail framework. And, in the world of online shopping, customers want accuracy, ease of use and appropriate suggestions.

For online businesses and service providers, some of the key benefits to using a search or recommendation algorithm include:

  • improve the relevance of search results and reduce the time it takes to find specific products and services
  • boost key metrics, including web visits and purchase rate, plus improve overall user loyalty and customer satisfaction
  • aid in the selection process for an undecided customer, encourage them to interact with more products and and enter other potential purchases into their field of vision
  • obtain data to target the right people with personalised ads and other digital marketing strategies to encourage users to frequent the website or platform

The quality of search and recommendation systems can significantly impact key business conversions, such as lead generations, customer sentiment scores and closing sales.

Amazon’s AI algorithms

As the leader of the global e-commerce market, Amazon is an almost-unrivalled product discovery and purchase platform, thanks to its optimal machine learning model. Built upon comprehensive ranking systems, the company’s A9 search algorithm analyses sales data, observes historical traffic patterns and indexes all product description text before a customer search query even begins, ensuring the best products are placed in front of the most likely buyers. 

The platform’s combination of intelligent recommendation tools forms the personalised shopper experience that has become so popular with consumers.

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Business across borders: The importance of international business law

International business is an essential part of a growing global economy. The integration of national economies into a global economic system – otherwise known as globalisation – has been one of the most important developments over the last century, prompting an extraordinary swell in international trade, commerce and production. 

This connectedness of markets and peoples has produced global value chains that account for a sizable share of trade growth, global gross domestic product and employment in both developed and developing countries. 

As such, international business has become a vital condition for economic and social development – especially for low-income countries. However, the ways in which this business is conducted can have a significant impact on the fortunes and futures of a nation.

Why do we need international business laws?

International business law comprises the various legal aspects of conducting business across borders, including business transactions, entity formation and funding, intellectual property protection, regulatory compliance, dispute resolution and international trade policy. They are put in place to regulate the business operations of a company and their supply chain across different nations. 

Upholding international laws is meant to protect against exploitation of a thriving economy or the oppression of a more vulnerable nation. Consequently, the impact of law-making must be carefully considered; the recent political crisis sparked by the Prime Minister’s proposed changes to the negotiated Northern Ireland Protocol is a prime example of this.

Trade or commerce is often at the centrepoint of these considerations, as the economic impact of a certain policy or transaction can be widespread. Multiple jurisdictions must be consulted. Trade agreements provide rules that assimilate and support fair and lawful trade between respective countries, and – ultimately – make business transactions easier.

International commercial law consists of a body of legal rules, conventions, treaties, domestic legislation and commercial customs that governs international business transactions. These laws facilitate mutually beneficial cooperation between respective countries, spanning economics, licensing, tariffs and taxes, and many other elements of business.

Why is international trade so important?

On a business scale, international trade is essential for increasing revenue, broadening a customer base and ensuring a longer product lifespan. Companies can also benefit from currency exchange fluctuations and gain access to a wider pool of potential employees. The majority of Fortune 500 corporations operate locations overseas, while all boast an international client list. 

The impact of the Covid-19 crisis has highlighted the importance of globalisation. Following the pandemic, businesses (both big and small) are increasingly relying on international trade to improve commercial viability, with 34% citing a desire to expand internationally and 51% of business leaders influenced to change their view on the value of exports.

Going global: Things to consider

For any company contemplating global expansion, the following are legal questions it will need to consider.

  • Labour and employment law: If a business hires or subcontracts overseas, it is subject to the respective country’s labour and employment laws. Consulting legal counsel is essential in helping companies with compliance and risk mitigation.
  • International trade compliance: Whenever a business transaction crosses borders, it invokes the national security and economic interests of the respective countries. This area of business law spans the navigation of imports, exports and sanctions. It’s also of great importance to have an understanding of corrupt nations and which countries are off limits (such as the trade sanctions taken against Russia during the Ukrainian crisis).
  • Corporate structure: If a business is setting up a branch or subsidiary overseas, where and how it chooses to establish a new business carries costs, capital requirements and tax consequences.
  • Taxes: Before going global, a corporation will want to carefully examine whether the foreign country has a tax treaty with their domestic nation, and the particular tax consequences of conducting business there.
  • Intellectual property: Spanning patents, copyrights, trademarks or trade secrets, intellectual property is a valuable asset. Securing and enforcing these rights can be costly. However, contractual arrangements including licences and employment agreements can be established before venturing overseas to mitigate risks and lower the expense.
  • Finances: The movement of money carries risk and complexity. An organisation must adhere to any applicable foreign currency exchange controls. The employment of a legal advisor can assist in keeping payments secure.
  • Termination of a business: Before setting up shop overseas, it’s best to consider an exit strategy if all goes wrong. It can be a complicated and expensive process to close an international venture. Government approval may be needed and there can be significant tax consequences as well as employee rights compliance.

The role of international business lawyers

International business lawyers advise, advocate for, or represent a client’s business interests regarding global transactions. They typically have a specialised education. 

These legal advisors can offer cross-border counsel on compliance with international trade rules. For example, they can assist corporations in obtaining the correct exporting licensing and advise on customs classifications. They will also conduct internal investigations and represent organisations through international disputes or when action is taken against any violations. 

To be a successful international business lawyer, you must have a strong grasp of economics and well-developed negotiation skills. The demand for international business lawyers and advisors is certain to spike as UK companies navigate the consequences of Brexit and aim to boost commercial viability following the coronavirus pandemic.

Why should I study international business?

Business success requires a global perspective. As companies continue to increase conduct on a global scale, anyone looking to enter an area of business management should have a good understanding of global governance, international agreements, foreign policy, various international business practices and the strategic decision-making of multinational enterprises. 

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How do algorithms work?

Much of what we do in our day-to-day lives comprises an algorithm: a sequence of step-by-step instructions geared to garner results. In the digital sphere, algorithms are everywhere. They’re the key component of any computer program, built into operating systems to ensure our devices adhere to the correct commands and deliver the right results on request.

An algorithm is a coded formula written into software that, when triggered, prompts the tech to take relevant action to solve a problem. Computer algorithms work via input and output. When data is entered, the system analyses the information given and executes the correct commands to produce the desired result. For example, a search algorithm responds to our search query by working to retrieve the relevant information stored within the data structure. 

There are three constructs to an algorithm.

  • Linear sequence: The algorithm progresses through tasks or statements, one after the other.
  • Conditional: The algorithm makes a decision between two courses of action, based on the conditions set, i.e. if X is equal to 10 then do Y.
  • Loop: The algorithm is made up of a sequence of statements that are repeated a number of times.

The purpose of any algorithm is to eliminate human error and to arrive at the best solution, time and time again, as quickly and efficiently as possible. Useful for tech users, but essential for data scientists, developers, analysts and statisticians, whose work relies on the extraction, organisation and application of complex data sets.

Types of algorithm 

Brute force algorithm

Direct and straight to the point, the brute force algorithm is the simplest but the most applicable, eliminating incorrect solutions based on trial and error.

Recursive algorithm

Recursive algorithms repeat the same steps until the problem is solved.

Backtracking algorithm

Using a combination of the brute force and recursive approach, a backtracking algorithm builds a data set of all possible solutions incrementally. As the name suggests, when a roadblock is reached, the algorithm retraces or ‘undoes’ its last step and pursues other pathways until a satisfactory result is reached.

Greedy algorithm

All about getting more juice for the squeeze, greedy algorithms are employed to source and select the optimal solution to a problem. They typically extract the most obvious and immediate information in minimum time, enabling devices to sort through data quickly and efficiently. This algorithm is great for organising complex workflows, schedules or events programmes, for example.

Dynamic programming algorithm

A dynamic programming algorithm remembers the outcome of a previous run, and uses this information to arrive at new results. Applicable to more complex problems, the algorithm solves multiple smaller subproblems first, storing the solutions for future reference.

Divide and conquer algorithm

Similar to dynamic programming, this algorithm divides the problem into smaller parts. When the subproblems are solved, their solutions are considered together and combined to produce a final result.

Are algorithms artificial intelligence?

Algorithms define the process of decision-making, whereas artificial intelligence uses data to actually make a decision.

If a computer algorithm is simply a strand of coded instructions for completing a task or solving a problem, artificial intelligence is more of a complex web, comprising groups of algorithms and advancing this automation even more. Continuously learning from the accumulated data, artificial intelligence is able to improve, modify and create further algorithms to produce other unique solutions and strengthen the result. The output is not defined, as with algorithms, but designated. In this way, artificial intelligence enables machines to mimic the complex problem-solving abilities of the human mind.

Artificial intelligence algorithms are what determine your Netflix recommendations and recognise your friends in Facebook photos. They are also called learning algorithms, and typically fall into three types: supervised learning, unsupervised learning and reinforcement learning.

Supervised learning algorithms

In this instance, programmers feed training data (or ‘structured’ data sets) into the computer, complete with input and predictors, and show the machine the correct answers. The system learns to recognise the relational patterns and deduce the right results automatically, based on previous outcomes.

Unsupervised learning algorithms

This is where machine learning starts to speak for itself. A computer is trained with unlabeled (or ‘raw’) input data, and learns to mine for rules, detect patterns and summarise and group data points to help better describe the data to users. The algorithm is used to derive meaningful insights from the data, even if the human expert doesn’t know what they’re looking for.

Reinforcement learning algorithms

This branch of algorithm learns from interactions with the environment, utilising these observations to take actions that either maximise the reward or minimise the risk. Reinforcement learning algorithms allow machines to automatically determine the ideal behaviour within a specific context, in order to maximise its performance.

Artificial intelligence algorithms in action

From artificial intelligence powered smartphone apps to autonomous vehicles, artificial intelligence is embedded into our digital reality in a multitude of big and small ways. 

Facial recognition software is what enables you to log in to your device in the first place, while apps such as Google Maps and Uber analyse location-based data to map routes, calculate journey times and fares and predict traffic incidents.

From targeted ads to personalised shopping, artificial intelligence algorithms are working to optimise our online experiences, while future applications will see the installation of self-driving cars and artificial intelligence autopilots.

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Tech basics: An introduction to text editors

Autocorrect: the maker or breaker of an awkward situation. As smart device users, we’re certainly au fait with the ways in which software like spell checkers can protect against common (and costly) linguistic mistakes. In our technological age, most of our digital practice involves using platforms built on text editors – but, if a conversation on coding still leaves you in a cold sweat, read on.

What is a text editor?

A text editor refers to any form of computer program that enables users to create, change, edit, open and view plain text files. They come already installed on most operating systems but their dominant application has evolved from notetaking and creating documents to crafting complex code. Today, text editors are a core part of a developer’s toolbox and are most commonly used to create computer programmes, edit hypertext markup language (HTML), and build and design web pages.

Examples of commonly used text editors include:

  • Android Studio
  • Atom
  • Notepad++
  • Sublime Text
  • VS Code

Text editors typically fall into two distinct categories: line editors and screen oriented editors. The latter allows more advanced flexibility for making modifications.

What’s the difference between a text editor and a word processor?

Text editors deal in plain text, which exclusively consists of character representation. Each character is represented by a fixed-length sequence of one, two, or four bytes, in accordance with specific character encoding conventions (such as ASCII, ISO/IEC 2022, UTF-8, and Unicode). These conventions define many printable characters, as well as non-printing characters that control the flow of the text, such as space, line break, and page break. 

Text editors should not be confused with word processors – such as Microsoft Office – which enable users to edit rich plain text too. Rich plain text is more complex, consisting of metadata such as character formatting data (typeface, size and style), paragraph formatting data (indentation and alignment commands) and page specification data (margins). Word processors are what we use to produce streamlined, formatted documents such as letters, essays or articles.

Features and functions of a text editor

Basic features of a text editor include the ability to cut, paste and copy text, find and replace words or characters, create bulleted lists, line-wrap text, and undo or redo a last command. They’re also equipped to open very large files (too big for a computer’s main memory to process) and read them at speed. Whether you’re coding with Linux or text editing with a Windows PC or a Mac device, the software should be functional, reliable and easy to use.

Other platforms (preferred by software developers) offer advanced features for more complex source code editing, including:

Syntax highlighting

Reading through endless reams of code can be overwhelming and time-consuming not to mention messy. This feature allows users to colour code text based on the programming or markup language it is written in (such as HTML and Javascript) for ease of reference.

Intelligent code completion

A context-aware software that speeds up the coding process by reducing typos, correcting common mistakes and offering auto-completion suggestions for syntax errors.

Snippets

An essential feature that enables users to quickly substitute longer pieces of content or code with a shortcut phrase which is great for creating forms, formatting articles or replicating chunks of information that you’re likely to repeat in your day-to-day workload.

Code folding

Also called expand and collapse, the code folding feature hides or displays certain sections of code or text, allowing for a streamlined and decluttered display – great for if you’re working on a long document.

Vertical selection editing

A useful tool that enables users to select, edit or add to multiple lines of code simultaneously, which is great for making repeat small changes (such as adding the same character to the end of every line, or deleting recurring errors).

Where and how are text editors used?

Most of us use text editors unconsciously. Almost everyone has a text and code editor built into their workflow, as they’re the engine that drive businesses all over the world. 

Developers and user experience (UX) designers use text editors to customise and enhance company web pages, ensuring they meet the needs of customers and clients. IT departments and other site administrations utilise this form of tech to keep internal systems fluent and functioning, while editors and creators use these applications to produce programs and content to funnel out to their global audience.

Going mobile: text editors and smartphones

So, where does autocorrect come in? Text editors appeal to the needs of the average tech user too, with forms of the software built into our iPhone and Android devices. 

The autocorrect feature (a checker and suggestion tool for misspelt words) is a prime example, combining machine-learning algorithms and a built-in dynamic dictionary to correct typos and offer replacement words in texts and Google searches.

A sophisticated mode of artificial intelligence, the autocorrect algorithm computes a considerable number of factors every time you type a singular character from the placement of your fingers on the keyboard to the grammar of other words in the sentence, while also accounting for phrases you currently use. The machine-learning algorithms absorb and relay back to what is documented on the internet.

Or perhaps not. To side-step the well-cited irritations of predictive text, you may have found yourself scrabbling with your settings, creating your own shortcuts and abbreviations for words commonly used in your communications. If that’s the case, congratulations. You may be more familiar with text editors than you first thought as you’ve accidentally tapped into an intelligent code completion tool!

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What are business ethics, and why do they matter?

It’s no exaggeration that the Covid-19 pandemic transformed the business world. With millions of workers furloughed and redundancies rife, companies – both big and small – faced extraordinary challenges.

For those who remained in business, mass adaptations had to be made – from remote working to social distancing. Fostering a collaborative, communicative and sensitive company culture became essential.

In our post-pandemic reality, corporate responsibility continues to be tested. Our societal lens has shifted, with staff welfare a centre point of discussion, labour demand being questioned and misconduct reports on the rise. The very nature of the coronavirus has forced companies to consider wider health and safety implications, while other businesses have had to adapt, modify and change to meet ever-evolving consumer needs too.

Meanwhile, wider societal fears are on the increase. Amongst other telling statistics, the 2022 Edelman Trust Barometer reports a 6% global increase in public fear of experiencing prejudice or racism, a 3% increase in concern over climate change, and a notable anxiety regarding job security. And, with government distrust at a disarming high point, in the wake of the pandemic, the public have turned to NGOs and businesses to solve these escalating ethical concerns.

This is nothing new. In the 1960s, rising consumer-awareness and discourse on increased corporate responsibility underpinned the decade – and markedly, the concept of business ethics was first conceived. In times of global crisis, it’s been proven that they matter more than ever.

What are business ethics?

Business ethics refer to an essential system of policies and practices that uphold a corporation’s legal and moral responsibilities. At their core, they determine what is ‘right and wrong’ for a company and its employees and inform a wider code of conduct.

These ethical standards are reflective of various contributing factors to a safe and functioning workforce. Many are embedded in law, others are influenced by social and ethical dilemmas, while additional business practices may be adopted as part of a more ‘individualised’ company culture.

While organisations vary in nature, business ethics should typically address the following principles:

Personal responsibility
Workers strive to be reliable employees and complete the duties assigned to them to their best ability.

Corporate responsibility
Businesses uphold contractual and legal obligations to employees, stakeholders and clients – such as determining safe working conditions, meeting minimum wage requirements and upholding manufacturing standards.

Loyalty and respect
Addresses the ways in which a company, their stakeholders, employees and clients should interact with integrity to maintain positive business relations. 

Trust
Businesses should cultivate trust, with employees trusting that terms of their employment will be kept, while clients can trust the business with their money and confidential information, for example.

Fairness
A company commits to holding all employees to the same standard, regardless of rank, and employs an equal treatment of customers.  

Community and environmental responsibility
Businesses will consider their impact on wider society and adhere to environmental regulations.

Examples of ethical standards in action

General expressions of ethical behaviour within the workplace include maintaining data protection, prioritising workplace diversity, putting customer needs first, and operating fairly and transparently as a business. Other ethical practices are more sector-specific, such as food and cosmetic producers adhering to lawful product labelling; and, financiers protecting against bribery and insider trading.

Alternatively, cultivating a hostile workplace, ignoring conflicts of interest, favouritism or discrimination of employees and misusing company time would be examples of unethical behaviours.

Business ethics: the bigger picture

Business ethics also bleed into a wider framework of corporate social responsibility, which refers to the way in which a company works to achieve or support larger societal goals. Not governed by law, corporate social responsibility is largely a self-regulated practice, where a business independently and voluntarily decides how it can contribute positive action of a philanthropic, activist or charitable nature.

This could include a commitment to the reduction of a company’s carbon footprint, improving their labour policies, making charitable donations, strengthening diversity, equality and inclusion, and making socially conscious investments.

Some key real-world examples include Coca Cola’s commitment to sustainability and Ford Motor Company’s investment in electric vehicles. Starbucks, meanwhile, in a move to tackle racial and social equity, aims to represent black, indigenous, and people of colour (BIPOC) at 30% in corporate roles and 40% in retail and manufacturing by 2025. 

Why are business ethics important?

Business ethics are important for a number of reasons. They ensure that a company operates lawfully, safeguarding both employees and the general public. They keep trade honest and fair, uphold manufacturing standards, and prevent false or bogus product claims. Plus, a strong ethical corporate culture fosters, amongst other things, improved performance and prevents employee burnout.

It works both ways, too. Any successful relationship is built on trust, and adhering to an evolved code of ethics can really benefit a business in terms of brand awareness and customer loyalty.

As Edelman states in its 2022 report: “Lasting trust is the strongest insurance against competitive disruption, the antidote to consumer indifference, and the best path to continued growth. Without trust, credibility is lost and reputation can be threatened.”

With regard to social responsibility, a values statement that addresses, challenges and attempts to solve both social and environmental issues paves the way to a business having real-world impact. With both millennials and Gen Z taking an amplified interest in brand activism and positive action, socially conscious companies are more likely to capitalise on reach, engagement and public investment.

Do business ethics make economic sense?

As we’ve seen, ethical decision-making breeds trust – and, in business, trust is currency. 

A company that upholds ethical standards that reflect real-world concerns and plays to a rising consumer consciousness is more likely to attract monetary investment, loyal staff (reducing recruitment costs) and consistent clientele. A good reputation is valuable and ultimately results in stronger financial health, from share price to increased sales.

Getting caught for unethical behaviours, on the other hand, could cost a company custom and fines, lead to less competitive hires and drive down its share price. For example, when Reuters reported a Johnson & Johnson company cover-up involving asbestos-contaminated talcum powder, the accusation triggered a 10% drop in the company’s stock price. 

Ultimately, leveraging business ethics wisely can result in increased brand equity overall.

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Data architecture: the digital backbone of a business

We are each the sum of our parts, and, in our modern technological age, that includes data. Our search queries, clicking compulsions, subscription patterns and online shopping habits – even the evidence collected from wearable fitness tech – feeds into our digital footprint. And, wherever we choose to venture on our online quests, we are constantly being tracked.

Experts claim that we create 2.5 quintillion bytes of data per day with our shared use of digital devices. With the big data analytics market slated to reach a value of $103 billion by 2027, there are no signs of data storage slowing down.

But it’s less about acquisition than application and integration, with poor data quality accounting for a cost of $3.1 trillion per year against the US economy according to market research firm IDC. While device-driven data may be fairly easy to organise and catalogue, human-driven data is more complex, existing in various formats and reliant on much more developed tools for adequate processing. Around 95% of companies can attest that their inability to understand and manage unstructured data is holding them back.

Effective data collection should be conceptual, logical, intentional and secure, and with numerous facets of business intelligence relying on consumer marketplace information, the data processed needs to be refined, relative, meaningful, easily accessible and up-to-date. Evidently, an airtight infrastructure of many moving parts is needed. 

That’s where data architecture comes into the equation.

What is data architecture?

As the term would imply, data architecture is a framework or model of rules, policies and standards that dictate how data is collected, processed, arranged, secured and stored within a database or data system.

It’s an important data management tool that lays an essential foundation for an organisation’s data strategy, acting as a blueprint of how data assets are acquired, the systems this data flows through and how this data is being used.

Companies employ data architecture to dictate and facilitate the mining of key data sets that can help inform business needs, decisions and direction. Essentially, when collected, cleaned and analysed, the data catalogues acquired through the data architecture framework allow key stakeholders to better understand their users, clients or consumers and make data-driven decisions to capitalise on business.

For example, e-commerce companies such as Amazon might specifically monitor online marketing analytics (such as buyer personas and product purchases) to personalise customer journeys and boost sales. On the other hand, finance companies collect big data (such as voice recognition and facial detection) to enhance online security measures.

When data becomes the lifeblood of a company’s potential reach, engagement and impact, having functional and adaptable data architecture can mean the difference between an agile, informed and future-proofed organisation and one that is constantly playing catch-up.

Building blocks: key components of data architecture

We can better visualise data architecture by addressing some of the key components, which act like the building blocks of this infrastructure.

Artificial intelligence (AI) and machine learning models (ML)

Data architecture relies on strong IT solutions. AI and machine learning models are innovative technologies designed to make calculated decisions, including data collection and labeling.

Data pipelines

Data architecture is built upon data pipelines, which encompass the entire data moving process, from collection through to data storage, analysis and delivery. This component is essential to the smooth-running of any business. Data pipelines also establish how the data is processed (that is, through a data stream or batch-processing) and the end-point of where the data is moved to (such as a data lake or application).

Data streaming

In addition to data pipelines, the architecture may also employ data streaming. These are data flows that feed from a consistent source to a designated destination, to be processed and analysed in near real-time (such as media/video streaming and real-time analytics).

APIs (or Application Programming Interface)

A method of communication between a requester and a host (usually accessible through an IP address), which can increase the usability and exposure of a service.

Cloud storage

A networked computing model, which allows either public or private access to programs, apps and data via the internet.

Kubernetes

A container or microservice platform that orchestrates computing, networking, and storage infrastructure workloads.

Setting the standard: Key principles of effective data architecture

As we’ve learned, data architecture is a model that sets the standards and rules that pertain to data collection. According to simplilearn, effective data architecture, then, consists of the following core principles.

  • Validate all data at point of entry: data architecture should be designed to flag and correct errors as soon as possible.
  • Strive for consistency: shared data assets should use common vocabulary to help users collaborate and maintain control of data governance.
  • Everything should be documented: all parts of the data process should be documented, to keep data visible and standardised across an organisation.
  • Avoid data duplication and movement: this reduces cost, improves data freshness and optimises data agility. 
  • Users need adequate access to data. 
  • Security and access controls are essential.

The implementation and upkeep of data architecture is facilitated by the data architect, a data management professional who provides the critical link between business needs and wider technological requirements.

How is data architecture used?

Data architecture facilitates complex data collection that enables organisations to deepen their understanding of their sector marketplace and their own end-user experience. Companies also use these frameworks to translate their business needs into data and system requirements, which helps them prepare strategically for growth and transformation.

The more any business understands their audience’s behaviours, the more nimble they can become in adapting to ever-evolving client needs. Big data can be used to improve upon customer service, cultivate brand loyalty, and ensure companies are marketing to the right people.

And, it’s not all about pushing products. In terms of real-world impact, a shifting relationship to quality data could improve upon patient-centric healthcare, for example.

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The real world impact of facial detection and recognition

From visual confirmation of rare diseases to securing smartphones, facial detection and recognition technologies have become embedded in both the background of our daily lives and the forefront of solving real-world problems. 

But is the resulting impact an invasive appropriation of personal data, or a benchmark in life-saving security and surveillance? Wherever you stand on the deep-learning divide, there is no denying the ways in which this ground-breaking biometric development is influencing the landscape of artificial intelligence (AI) application.

What is facial detection and recognition technology?

Facial detection and recognition systems are forms of AI that use algorithms to identify the human face in digital images. Trained to capture more detail than the human eye, they fall under the category of ‘neural networks’; aptly-named computer softwares modelled on the human brain, built to recognise relationships and patterns in given datasets.

Key differences to note

Face detection is a broader term given to any system that can identify the presence of a human face in a visual image. Face detection has numerous applications, including people-counting, online marketing, and even the auto-focus of a camera lens. Its core purpose is to flag the presence of a face. Facial recognition, however, is more specialised, and relates specifically to softwares primed for individual authentication. Its job is to identify whose face is present.

How does it work?

Facial recognition software follows a three-part process. Here’s a more granular overview, according to Toolbox:

Detection

A face is detected and extracted from a digital image. Through marking a vast array of facial features (such as eye distance, nose shape, ethnicity and demographic data, and even facial expressions), a unique code called a ‘faceprint’ is created to identify the assigned individual.

Matching

This faceprint is then fed through a database, which utilises several layers of technology to match against other templates stored on the system. The algorithms are trained to capture nuance and consider differences in lighting, angle and human emotion.

Identification

This step depends on what the facial recognition software is used for — surveillance or authentication. The technology should ideally produce a one-to-one match for the subject, passing through various complex layers to narrow down options. (For example, some software providers even analyse skin texture along with facial recognition algorithms to increase accuracy.)

Biometrics in action

If you’re an iPhone X user, you’ll be familiar with Apple’s Face ID authentication system as an example of this process. The gadget’s camera captures a face map using specific data points, allowing the stored user to unlock their device with a simple glance.

Some other notable face recognition softwares include:

  • Amazon Rekognition: features include user verification, people counting and content moderation, often used by media houses, market analytics firms, ecommerce sites and credit solutions
  • BioID: GDPR-compliant solution used to prevent online fraud and identity theft
  • Cognitec: recognises faces in live video streams, with clients ranging from law enforcement to border control
  • FaceFirst: a security solution which aims to use DigitalID to replace cards and passwords
  • Trueface.ai: services span to weapon detection, utilised by numerous sectors including education and security

Real-world applications

As outlined in the list above, reliance on this mode of machine learning has permeated almost all areas of society, extending wider still to healthcare and law enforcement agencies. This illustrates a prominent reliance on harvesting biometric data to solve large-scale global problems, spanning – at the extreme – to the life-threatening and severe. 

Medical diagnoses

We are beginning to see documented cases of physicians using these AI algorithms to detect the presence of rare and compromising diseases in children. According to The UK Rare Diseases Framework, 75% of rare diseases affect children, while more than 30% of children with a rare disease die before their fifth birthday. With 6% of people slated to be impacted by a difficult to diagnose condition in their lifetime, this particular application of deep learning is imperative.

Criminal capture

It was recently reported that the Metropolitan Police deployed the use of facial recognition technology in Westminster, resulting in the arrests of four people. The force announced that this was part of a ‘wider operation to tackle serious and violent crime’ in the London borough. The software used was a vehicle-mounted LFR system, which enables police departments to identify passers-by in real-time by scanning their faces and matching them against a database of stored facial images. According to the Met Police website, other applications of face identification include locating individuals on their ‘watchlist’ and providing essential information when there is an unconscious, non-communicative or seriously injured party on the scene.

Surveillance and compliance

A less intensive example, but one that could prove essential to our pandemic reality. Surveillance cameras equipped with facial detection were used to filter face mask compliance at a school in Atlanta, while similar technology has been applied elsewhere to conduct gun control.

Implications of procuring biometric information

Of course, no form of emerging or evolving technology comes without pitfalls. According to Analytics Insight, the accuracy rates of facial recognition algorithms are notably low in the case of minorities, women and children, which is dangerously problematic. Controversy surrounding data protection, public monitoring and user privacy persists, while the generation of deepfake media (and softwares like it), used to replicate, transpose and project one individual’s face in replacement of another, gives rise to damaging – and potentially dangerous – authentication implications. Returning to the aforementioned Met Police arrests, even in this isolated sample, reports of false positives were made, sparking outcry within civil rights groups.

At the centre of this debate, however, one truth is abundantly clear; as a society, we are becoming rapidly reliant on artificial intelligence to function, and the inception of these recognition algorithms is certainly creating an all new norm for interacting with technology.

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What are flexible work arrangements?

Flexible work arrangements are working hours, work locations, or working patterns that are altered to suit an employee’s individual circumstances. They are an increasingly popular way for employees to balance their personal and professional lives, and for employers to attract and retain talented people.

Types of flexible working arrangements include:

Remote working

This is one of the most common and well-known forms of flexible working. With the coronavirus pandemic necessitating a work-from-home mandate for the majority of workplaces, businesses and employees had to quickly adapt to this flexible working arrangement. Telecommuting and telework – an employee completing their work from outside their traditional office using tools such as email, phones, and video apps like Zoom or Teams – swiftly became the norm. Now, even as the working world begins to return to offices and other workplaces, many are opting to work remotely, either on a full-time basis, or some of the time, which is known as hybrid working. Many remote workers have found that they save time and money on their commutes, have fewer distractions while working, and have increased their productivity.

Staggered hours

Staggered hours are when an employee has a start time, finish time, or break time that differs from their colleagues’ hours at the organisation. For example, someone may request to work from 12:00 until 20:00 every day, even though the typical working hours at the business are 09:00 until 17:00, to accommodate their personal circumstances.

Compressed hours

If a person works full-time hours over fewer days, this is known as compressed hours. For example, an employee might choose to work what’s called a nine-day fortnight – the employee works a little later than other employees every day in order to have every other Friday off work.

Job sharing

Job sharing is when one role is split between two people. For example, one employee may work Monday and Tuesday, while the second employee has a Wednesday-to-Friday work week, but both do the same job when at work.

Part-time hours

Part-time work is often requested when an employee wants to work reduced hours during the day, or work fewer days a week. For example, a parent or guardian may request a working day of 9am until 3pm every day so that they can be home for their children or dependants before and after school during term-time. This can also reduce the likelihood of a parent needing to take parental leave.

Flexitime hours

Not to be confused with staggered hours, flexitime, or flextime, allows an employee to choose their working start and finish times, but always works the organisation’s “core hours” – for example, between 10am and 2pm every day.

Annualised hours

Annualised hours mean that an employee works a certain number of hours during a year, but their schedule is a bit more flexible. For example, agency workers may work certain core hours, and then complete the rest of their hours when needed for projects or by clients, and so on.

The increasing popularity of flexible work arrangements has prompted the UK government to complete a consultation on ‘Making flexible working the default’. While the results of the consultation are still pending, the government has noted that flexible working can be particularly useful for people who need to balance their personal and working lives. For example, people with carer responsibilities may be better able to access the labour market, or stay in work, with flexible working options. The government has also noted that flexible working arrangements can help employers by attracting more applicants to new roles, as well as by increasing productivity and motivation levels within workplaces.

How does flexible working affect a business?

The impact of flexible working on businesses is overwhelmingly positive for both employers and employees. The UK government’s consultation document for changes to flexible working states that by “removing the invisible restrictions to jobs, flexible working fosters a more diverse workforce – and the evidence shows that this leads to improved financial returns for businesses.”

Meanwhile, the Chartered Institute of Personnel and Development (CIPD), the UK’s association for human resources professionals, says that quality flexible arrangements and flexible work schedules can also help businesses to:

  • improve employee work-life balance, job satisfaction, loyalty, and well-being
  • increase staff retention
  • reduce absenteeism
  • become more responsive to change

However, it’s worth noting that research conducted by the CIPD suggests that not all employers offer flexible working practices. In fact, 46% of employees say they do not have flexible working arrangements in their current roles.

The CIPD also notes that while working from home, or remote work, has increased during the COVID-19 pandemic, 44% of people did not work from home at all during the past two years. So while remote working is a popular flexible working arrangement, it’s just one of the options available – and 75% of employees say it’s important that people who can’t work from home have choices to work flexibly in other ways.

How to implement flexible work arrangements

All employees are entitled to request flexible working in the UK, as long as they have 26 weeks of service with their employer. Requests can be made once every 12 months and must be submitted in writing.

The Advisory, Conciliation and Arbitration Service (ACAS), which offers advice and support on flexible working arrangements, recommends that employers:

  • offer clear guidance about what information is needed when an employee submits their flexible working request
  • talk to the employee requesting flexible working as soon as possible after receiving the request. This conversation should be in a private place, and determine how the request might benefit the employee and the business
  • allow an employee to be accompanied by a work colleague for any discussions, and make sure the employee knows they have this option
  • let the employee know the decision about their request as soon as possible, in writing
  • allow the employee to appeal the decision if their request is denied

It’s also worth noting that a request for flexible working can only be rejected for one of the following reasons:

  • the burden of additional costs
  • an inability to reorganise work among existing staff
  • an inability to recruit additional staff
  • a detrimental impact on quality
  • a detrimental impact on performance
  • a detrimental effect on ability to meet customer demand
  • insufficient work for the periods the employee proposes to work
  • a planned structural change to the business

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Embracing the technological revolution: launching a career in computer programming

With our modern, globalised world so heavily reliant on data and technology, it is now almost impossible to comprehend the impact its absence would have on our lives. The prevalence of data and technology is advancing at an unprecedented speed and scale, fundamentally transforming the ways in which we live and work.

Supporting our increasingly automated lives and lifestyles through data collection, information analysis and knowledge sharing – in an effort to continuously advance and innovate upon existing processes and structures – is of strategic importance.

The UK digital skills shortage

The UK suffers from a critical digital skills shortage. Reports from a number of sources – including the latest report from the Department of Digital, Culture, Media and Sport (DCMS) – reveal that:

  • almost 20% of UK companies have a skills vacancy, with 14.1% reporting a lack of digital know-how
  • 66% of digital leaders in the UK are unable to keep up with changes due to a lack of talent
  • the UK tech industry is facing its greatest shortages in cybersecurity, data architecture, and big data and data analysis
  • only 11% of tech leaders believe the UK is currently capable of competing on a global scale
  • data analysis is the fastest-growing skills clustering in tech, set to expand by 33% over the next five years
  • 80% of digital leaders feel retention is more difficult post-pandemic due to shifting employee priorities

Evidently, there is a stark need for individuals with the skills and fundamentals necessary to harness technology’s potential, using it to guide, improve and provide insights into today’s global business environments. Millions are being invested to encourage more people to train for roles which require skills such as coding, data analytics, artificial intelligence (AI) and cybersecurity.

Digital skills are considered vital to post-pandemic economic recovery; in competitive, crowded marketplaces, evidence and data are key to guiding decision-making and business efforts. For those considering a career in computer science – whether in big data, web development, application development, programming, or any number of other fields – there has never been a better time to get involved.

Computer programming as a career

Depending on the role, industry and specialism, programmers can expect to undertake a wide-ranging array of tasks. For example:

  • designing, developing and testing software
  • debugging, to ensure that operating systems meet industry standards and are secure, reliable and perform as required
  • integrating systems and software
  • working alongside designers and other stakeholders to plan software engineering efforts
  • training end-users
  • analysing algorithms
  • scripting and writing code in different languages

The applications for this specialist skill set are vast – and the skills are required in almost every industry and sector. Individuals can work across, for example, websites and web applications, mobile and tablet applications, data structures and video games. Most of us will be familiar with the global, household names of Microsoft, Google and IBM – titans of the computing and technology industry. However, the technological skills and expertise gained from a computer science degree can open doors to careers in any number of businesses and sectors.

Potential career paths and roles could include:

  • computer programmer
  • software application developer
  • front-end/back-end web developer
  • computer systems engineer
  • database administrator
  • computer systems analyst
  • software quality assurance engineer
  • business intelligence analyst
  • network system administrator
  • data analyst

It’s a lucrative business. The current average salary for a programmer in the UK is £57,500 – a figure that can be well-exceeded with further experience and specialisation. It’s also a career with longevity; while computer programming is of paramount importance today, as the data and digital landscape continues to evolve, it’s only going to be even more important in the future.

What skills are needed as a computer programmer?

In the role of a programmer, it’s essential to combine creativity with the more technical and analytical elements of information systems. It’s a skilled discipline which requires artistry, science, mathematics and logic.

Indeed list a number of the more common skills required by computer programmers:

  • Proficiency with programming languages and syntax: While JavaScript is currently the most commonly used programming language, there are also many others, including Python, HTML/CSS, SQL, C++, Java, and PHP. Learning at least two computer programming languages will help to boost employability. Most programmers choose their area of computing specialism and then focus on the most appropriate language for that field.
  • Learning concepts and applying them to other problems: Take the example of CSS, where styles that are applied to a top-level webpage are then cascaded to other elements on this page. By understanding how programming concepts can be translated elsewhere, multiple issues can be resolved more efficiently.
  • Solid knowledge of mathematics: For the most part, programming relies on an understanding of mathematics that goes beyond the basics. Possessing solid working knowledge of arithmetic and algebra underpins many aspects of programming proficiency.
  • Problem-solving abilities: Code is often written and developed in order to create a solution to a problem. As such, having the capabilities to identify and solve problems in the most efficient way possible is a key skill for those working in programming.
  • Communication and written skills: Demonstrating how certain processes and results are produced – for example, stakeholders who may have limited or no programming and technical knowledge – is often a necessary part of the role. The ability to coherently communicate work is vital.

For those interested in developing their skill set, there exist a wealth of interactive, online courses and certifications to get started. Typical entry requirements include an undergraduate/bachelor’s degree.

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The Internet of Things in the age of interconnectivity

Global online interconnectivity has woven itself seamlessly into our lives. How many of us can conceive of a modern life without the internet?

Going about our daily lives both personally and professionally, we reach for our mobile phones and devices for news, information, entertainment, and to communicate with each other. The ease and expectation of accessing information online 24/7 is taken as a matter of course. What most people may not consider, however, is how all this information technology is delivered to us. Digital transformation, due to emerging technologies, continues to grow exponentially. The Internet of Things (IoT) is an essential, and integral, element in ensuring current and future business success.

What is the Internet of Things and how did it evolve?

Simply put, the IoT is the concept of networking connected devices so that they can collect and transmit data. Nowadays, it enables digital technology to be embedded in our physical world, such as in our homes, cars, and buildings, via vast networks connecting to computers.

Historically, the concept of IoT devices has an interesting timeline, and its early pioneers are names that remain well-known to many of us today:

  • 1832. Baron Schilling creates the electromagnetic telegraph.
  • 1833. Carl Friedrich Gauss and Wilhelm Weber invent a code-enabling telegraphic communication.
  • 1844. Samuel Morse transmits the first Morse code public message from Washington D.C. to Baltimore.
  • 1926. Nikola Tesla conceives of a time when what we know as a mobile phone will become a reality.
  • 1950. Alan Turing foresees the advent of artificial intelligence.
  • 1989. Tim Berners-Lee develops the concept of the World Wide Web.

Even ordinary physical objects became the subject of IoT applications:

  • 1982. Carnegie-Mellon University students install micro switches to check the inventory levels of Coca-Cola vending machines and to see whether they were cold enough to drink.
  • 1990. John Romkey and Simon Hackett connect a toaster to the internet.

As the technology and research grew exponentially from the 1960s onwards, the actual term ‘Internet of Things’ was coined in 1999 by Proctor & Gamble’s Kevin Ashton. By 2008, the first international conference on IoT was held in Switzerland. By 2021, it was reported that there were 35.82 billion IoT devices installed globally, with projections of 75.44 billion worldwide by 2025.

Real-world application

Given the huge potential of IoT technology, the scale of its cross-sector assimilation is unsurprising. For example, it impacts:

  • The consumer market. Designed to make life easier, consider the sheer number of internet-enabled smart devices – including wearables and other goods – that are in daily use. Common examples include smartphones and smartwatches, fitness trackers, home assistants, kitchen appliances, boilers, and home security cameras. We interact with internet connectivity every day; increasingly, many of us are already living in ‘smart’ homes. Optimising the customer experience is key to business success. Whether related to data-collecting thermostats which monitor energy consumption, or wifi providers which supply the best Bluetooth packages, all are driven by IoT systems.
  • Physical world infrastructure. On a grander scale, IoT technology is now focused on developing smart buildings and, in the long run, smart cities. In buildings, elements such as heating, lighting, lifts and security are already directed by automation. In the outside world, real-time, data-gathering traffic systems and networks rely on IoT to share data using machine learning and artificial intelligence.
  • Industrial and domestic sectors. Where, previously, many items and goods were manufactured and serviced off-grid, everything is now internet-connected. Items used domestically include washing machines, doorbells, thermostats, and gadgets and virtual assistant technology such as Alexa and Siri. Amazon distribution centres, factories and international mail delivery systems are all examples of environments that are reliant on IoT platforms.
  • Transportation. In an area of highly complex logistics, keeping the supply chain moving and reaching its destination is critical. The same can be applied to all other modes of transport, such as aeroplanes, ships, trains and vehicles. For the individual, connected cars are already a reality. Many vehicles have the ability to communicate with other systems and devices, sharing both internet access and data.
  • Healthcare. The impact of the global Covid pandemic has taken a huge toll on our lives. The stresses on worldwide healthcare and medical business models have become ever more pressing. The need for strategies and solutions to deliver optimal healthcare, as modelled on IoT, is being researched by many organisations including Microsoft. Devices such as pacemakers, cochlear implants, digital pills, and wearable tech such as diabetic control sensors, are making invaluable contributions to patients across the sector.

The technology behind the Internet of Things

IoT technology presents immeasurable benefits in our lives, and its scope is seemingly limitless. IoT platforms are interconnected networks of devices which constantly source, exchange, gather and share big data using cloud computing or physical databases. They consist of:

  • Devices. These connect to the internet and incorporate sensors and software which connect with other devices. For example, the Apple watch connects to the internet, uses cloud computing, and also connects with the Apple iPhone.
  • Communications. Examples include Bluetooth, MQTT, wifi and Zigbee.
  • Cloud computing. This refers to the internet-based network on which data from IoT devices and applications is stored.
  • Edge computing. The use of tools such as IBM’s edge computing uses artificial intelligence to help solve business problems, increase security, and enhance both capacity and resilience.
  • Maintenance and monitoring. Monitoring and troubleshooting these devices and communications is essential to ensure optimum functionality.

Inevitably, while the benefits to both international businesses and organisations are immense, IoT technology also attracts cybercrime and hackers. Cyber security threats target all areas of IoT – from businesses to individual users.

IoT has been hailed as the fourth Industrial Revolution. Future technology is already blending artificial intelligence with IoT, with the aim of enabling our personal and professional lives to become simpler, safer and more personalised. In fact, in terms of IoT security, artificial intelligence can be used for:

  • Evaluating information for optimisation
  • Learning previous routines
  • Decreasing down times in functionality
  • Increasing the efficiency and efficacy of procedures
  • Creating solutions to ward off potential threats, thus enhancing security

Career prospects

Career opportunities within the computer science and artificial intelligence fields may include, but are not limited to:

  • Natural language processing
  • Machine learning engineering
  • Semantic technology
  • Data science
  • Business intelligence development
  • Research science
  • Big data engineering/architecture

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Intellectual capital: driving business growth and innovation

How can a business maximise its growth and development? What can be done to increase competitive advantage? Are businesses making the best possible use of all their assets?

In an increasingly crowded global economy, all businesses must work hard to remain relevant, competitive and profitable. Innovation is key to maximising business growth and, for many businesses, they already possess the means to achieve it. Alongside this, developing customer-focused, personalised experiences – and adding value through the customer journey – is key. An organisation’s intellectual capital has the potential to achieve both aims, and add significant economic benefit – but what is it, and how is it best utilised?

What is intellectual capital?

Intellectual capital (IC) refers to the value of an organisation’s collective knowledge and resources that can provide it with some form of economic benefit. It encompasses employee knowledge, skill sets and professional training, as well as information and data.

In this way, IC identifies intangible assets, separating them into distinct, meaningful categories. Although not accounted for on a balance sheet, these non-monetary assets remain central to decision making and can have a profound impact on a company’s bottom line. More than ever, IC is recognised as one of most critical strategic assets for businesses.

Broadly speaking, there are three main categories:

  • Human capital: the ‘engine’ and ‘brain’ of a company is its workforce. Human capital is an umbrella term, referring to the skills, expertise, education and knowledge of an organisation’s staff – including how effectively such resources are used by those in management and leadership positions. A pool of talented employees, with a wealth of both professional and personal skills, adds significant value to a workplace. Companies who prioritise investing in the training, development and wellbeing of their teams are actively investing in their human capital. It can bring a host of benefits, including increased productivity and profitability.
  • Relational capital: this category refers to any useful relationships an organisation maintains – for example, with suppliers, customers, business partners and other stakeholders – as well as brand, reputation and trademarks. Customer capital is adjacent to this, and refers to current and future revenues from customer relationships.
  • Structural capital: structural capital relates to system functionality. It encompasses the processes, organisation and operations by which human and relational capital are supported. This may include intellectual property and innovation capital, data and databases, culture, hierarchy, non-physical infrastructure and more.

Each area offers the means for value creation – which is integral to increasing competitiveness. As such, business leaders should prioritise intellectual capital, and its role within operational strategy, in both short-term and long-term planning.

How is intellectual capital measured?

As stated, while IC is counted among a company’s assets, it is not included in its balance sheet. While there are various ways to measure intellectual capital, there isn’t one widely agreed, consistent method for doing so. Together, these aspects mean that quantifying it can be challenging.

Three main methods are generally used to measure IC:

  • The balanced scorecard method examines four key areas of a business to identify whether they are ‘balanced’. They are:
    1. customer perspective – how customers view the business; 
    2. internal perspective – how a company perceives its own strengths; 
    3. innovation and learning perspective – examining growth, development and shortfalls;
    4. financial perspective – whether shareholder commitments are being met. 

A visual tool which communicates organisational structure and strategic metrics, the scorecard provides a detailed overview without overwhelming leaders with information.

  • The Skandia Navigator method uses a series of markers to develop a well-rounded overview of organisational performance. It focuses on five key areas: 
    1. financial focus – referring to overall financial health; 
    2. customer focus – including aspects such as returning customers and satisfaction scores; 
    3. process focus – how efficient and fit-for-purpose businesses processes are; 
    4. renewal and development focus – which looks at long-term business strategy and sustainability;
    5. human focus – sitting at the centre of the others, human focus encompasses employee wellbeing, experience, expertise and skills.
  • Market value-to-book value ratio is calculated by comparing a company’s book value with its market value, and aims to identify both undervalued and overvalued assets. A ratio above one indicates that there may be undervalued assets which are not being utilised; a ratio below one indicates there may be overvalued assets which action could be taken to strengthen.

How can a business increase its intellectual capital?

Intellectual capital acts as a value-driver in our twenty-first-century economy. As such, it’s no surprise that many businesses are pivoting to focus on human, relational and structural assets over others. Given both its relative importance and the returns an organisation can expect, finding ways to increase IC could be key to achieving key business goals.

For Forbes, efforts to increase IC mean adopting either a solution-focused or perspective-focused approach. The first refers to the methods by which specific results can be achieved – the what, when, why and where. The second refers to how IC can utilise industry and marketplace trends, forecasts and insights to seize opportunities. Whichever approach a business opts for, there are a number of ways in which to boost intellectual capital efforts. These include:

  • Improving employee satisfaction to increase retention rates
  • Recruiting individuals with specific knowledge, competencies and skill sets that are currently lacking among the existing workforce
  • Auditing and enhancing systems and processes
  • Gathering research and data to inform decision making
  • Investing in training and development opportunities for employees
  • Improving employer branding to both attract and retain the best talent
  • Creating new products, services and initiatives through innovation

Influential contributors and further reading

Early and current proponents and authors of intellectual capital thinking include:

  • Patrick H Sullivan who wrote ‘A Brief History of the Intellectual Capital Movement’. He presented a concise overview of the beginnings of the discipline in which he traced it back to three origins. These were: Hiroyuki Hami, who studied invisible assets pertaining to Japanese operational management; the work of various economists (Penrose, Rumelt, Wernerfelt et al) which was included in Dr David J. Teece’s 1986 article relating to technical commercialisation; and Karl Erik-Sveiby, who focused on human capital in terms of employee competences and knowledge base. His model of intellectual capital, published in 1997, was a seminal contribution in the field.
  • Dr David J Teece published ‘Managing Intellectual Capital’ in 2002, and further publications by him are available on Google Scholar.
  • Leif Edvinsson’s 2002 book, ‘Corporate Longitude’, concerned itself with the measurement, valuation and economic impact of the knowledge economy.
  • Thomas A Stewart, a pioneer in the field, authored ‘The New Wealth of Organizations’ in 1997. He delved into areas such as unlocking potential hidden assets, spotting and mentoring talented employees, and investigating methods to identify and retain customer and brand loyalty.

The field of intellectual capital continues to expand and evolve globally. Many well-known international figures such as Johan Roos and Nick Bontis continue to explore both its ramifications and applications.

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