What are data structures?

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Data is a core component of virtually every computer programme and software system – and data structures are what store, organise, and manage that data. Data structures ensure that different data types can be efficiently maintained and accessed, and effectively processed and used, in order to perform both basic operations and advanced tasks.

There are different data structure types – some basic and some more complex – that have been designed to meet different requirements, but all of them typically ensure that data can be understood by both machines and humans, and then used in specific ways for specific purposes.

But to understand the different types of data structures, it’s important to first understand the different types of data.

What are the different data types?

Data types are the foundation of data structures. They are what tell the computer compiler or interpreter – which translates programming languages such as Java, JavaScript and Python into machine code – how the programmer intends to use the data. They typically fall into one of three categories.

Primitive data types

Primitive data types are the most basic building blocks of data, and include:

  • Boolean, which has two possible values – true or false
  • characters, such as letters and numerals
  • integers and integer values, which are whole numbers that do not contain a fraction
  • references (also called a pointer or handle), which allow a computer programme to refer to data stored elsewhere, such as in the computer’s memory
  • floating-point numbers, which are numbers that include a decimal
  • fixed-point numbers, which are numbers that include a decimal up to a fixed number of digits

Composite data types

Also known as compound data types, composite data types combine different primitive data types. They include:

  • arrays, which represent a collection of elements, such as values or variables
  • records, which group several different pieces of data together as one unit, such as names and email addresses housed within a table
  • strings, which order data in structured sequences

What is an associative array?

An associative array – also called maps, symbol tables, or dictionaries – is an array that holds data in pairs. These pairs contain a key and a value associated with that key.

Abstract data types

Abstract data types are defined by their behaviour, and include:

  • queues, which order and update data using a first-in-first-out (FIFO) mechanism
  • stacks, which order and update data using a last-in-first-out (LIFO) mechanism
  • sets, which can store unique values without a particular order or sequence

What are the different types of data structures?

There are several different structures that store data items and process them, but they typically fall into one of two categories: linear data structures or non-linear data structures.

The data structure required for any given project will depend upon the operation of the programme or software, or the kinds of sorting algorithms that will be used. 

Examples of linear data structures

Array data structures

Like array data types, array data structures are made up of a collection of elements, and are among the most important and commonly used data structures. Data with an array structure is stored in adjoining memory locations, and each element is accessed with an index key.

Linked list data structures

Linked list data structures are similar to arrays in that they are a collection of data elements, however, the order of these elements is not determined by their place within the machine’s memory allocation. Instead, each element – or node – contains a data item and a pointer to the next item. 

Doubly linked list data structures

Doubly linked lists are more complex than singly linked lists – a node within the list contains a pointer to both the previous node and the next node. 

Stack data structures

Stacks structure data in a linear format, and elements can be inserted or removed from just one side of the list – the top – following the LIFO principle.

Queue data structures

Queues are similar to stacks, but elements can only be inserted or removed from the rear of the list, following the FIFO principle. There are also priority queues, where values are removed on the basis of priority.

Examples of non-linear data structures

Tree data structures

Trees store elements hierarchically and in a more abstract fashion than linear structures. Each node within the structure has a key value, and a parent node will link to child nodes – like branches on a family tree. There are a number of different types of tree structures, including red-black tree, AVL tree, and b-trees.

What is a binary tree?

Binary trees are tree data structures where each node has a maximum of two child nodes – a left child and a right child.

They are not to be confused with binary search trees, which are trees that are structured to be increasingly complex – a node is always more complex than the node that came before it, and the structure’s time complexity to operate will be directly proportional to the height of the tree.

Graph data structures

Graph structures are made up of a set of nodes – known as vertices – that can be visualised like points on a graph, and are connected by lines known as edges. Graph data structures follow the mathematical principles of graph theory. 

Hash data structures

Hash data structures include hash lists, hash tables, hash trees, and so on. The most commonly known is the hash table, also referred to as a hash map or dictionary, which can store large amounts of data, and maps keys to values through a hash function. They also employ a technique called chaining to avoid collisions, which can occur when two keys are hashed to the same index within the hash table.

Dig deeper into data structures

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What is a business strategy?

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A business strategy outlines the specific ways in which an organisation plans to position itself, achieve its short-term and long-term goals, and grow over a period of time. It draws on other important business resources, such as the organisation’s mission, its vision, and its values, to help chart its direction forward and deliver on its objectives.

A business strategy helps underpin an organisation’s other important strategies – for example, functional operational strategies – and helps staff, customers, investors, and other stakeholders better understand how it plans to achieve its goals.

A successful business strategy also ensures that an organisation – regardless of whether it’s a startup, a small business, or a global corporation– maintains a competitive advantage in its market. It’s there to help guide decision-making, particularly on matters such as business priorities, and resource allocation.

Where business strategies sit within organisations

A business strategy typically comes second to an organisation’s corporate strategy. The corporate strategy looks at the bigger picture – the market a business is operating in, new markets that may be profitable to enter, and how best to ensure company growth. 

The business strategy helps feed into the corporate strategy, and acts as a roadmap to help deliver on the corporate strategy. Sometimes, it may also feed into a competitive strategy, which outlines methods for strengthening an organisation’s market position, attracting clients away from competitors, and defining the unique selling points within the brand as well as its products and services.

At the same time, the functional and operating strategies feed into the business strategy. These two frameworks are responsible for effectively and efficiently delivering on the organisation’s objectives as outlined in the corporate, competitive, and business strategies.

It’s also worth noting that there are a number of other strategies that typically sit under an organisation’s business strategy. These can include the:

  • marketing strategy
  • pricing strategy
  • brand strategy
  • sales strategy
  • communications strategy
  • advertising strategy
  • PR and media strategy
  • social media strategy
  • promotion strategy

What is the difference between a business strategy and a business plan?

While a business strategy acts as an overarching strategic framework within an organisation, a business plan is more granular, outlining day-to-day activities and work. Effectively, a business plan is one of the tools that helps deliver the business strategy.

Business strategies are also separate to business models, which are the methods used to generate sales and growth within a business. Examples of business models include direct sales, subscriptions, and franchises.

Five questions a business strategy should answer

Global employment network Indeed suggests that there are five main questions a business strategy needs to answer in order to be useful to its business.

  1. Why is the organisation in business?
  2. What are its key selling points or core strengths?
  3. Who are its ideal customers?
  4. Which offers provide the best results for its customers and its company bottom-line?
  5. Will this strategic framework help the business achieve its goals and objectives?

Adequately answering these questions can help a business create or improve its market value, establish or enhance its reputation, and acquire customers in greater numbers.

What makes a good business strategy?

A lot of work goes on behind the scenes during business strategy formulation. 

Strategic management, including CEOs and business leaders, need to be on board and help drive the new strategy forward. 

This requires extensive analysis – in everything from the supply chain to market research –, as well as a thorough understanding of the organisation’s competitive position and competencies. This can be better understood through simple tools, such as a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis or template.

Stakeholders – such as staff groups – should also be brought into the development process to help ensure its success.

The Chartered Management Institute (CMI) recommends that a good strategy is developed to be:

  • flexible
  • responsive
  • creative
  • challenging
  • realistic
  • focused
  • engaging

And in order to be effective, strategic planning also needs to be implemented appropriately. The CMI recommends implementation techniques such as: 

  • ensuring that plans are aligned with the organisation’s mission, vision and values
  • building an effective leadership team
  • creating a dedicated implementation plan
  • allocating sufficient budgetary resources
  • assigning objectives and responsibilities to the appropriate people
  • aligning structures, processes, and people throughout the organisation
  • communicating the strategy
  • reviewing and reporting on progress
  • making strategic adjustments as necessary
  • developing an organisational culture that supports the strategy

Types of business strategies

There are a number of different business strategies used within the business world.

Common business strategy examples include:

  • Cost leadership. Cost leadership business strategies focus on beating competitors’ prices. For example, businesses such as Amazon look for ways to increase efficiencies or reduce production costs so that their prices are lower than their competitors’ prices. They often rely on economies of scale.
  • Differentiation. Differentiation business strategies highlight the unique features of products and services to stand out from competitors.
  • Focused differentiation. Focused differentiation is typically used by businesses that target niche markets. For example, an eco-friendly child toy company might market itself specifically to environmentally conscious parents. 
  • Focused low-cost. Focused low-cost business strategies are similar to focused differentiation strategies, but their point of differentiation is specifically lower-cost products and services. 
  • Integrated low-cost/differentiation. An integrated low-cost/differentiation strategy is the middle ground between focused differentiation and focused low-cost business strategies. It’s effectively a hybrid model where differentiated products are sold at a lower-than-average price point.
  • Structuralist. A structuralist business strategy is one that is built around current market and industry norms. Everything from products to processes is structured around current market conditions and industry standards, and the business strategy is developed around this structure.
  • Growth. Growth strategies are suitable for businesses that want to actively expand into new markets and introduce new products or services. Their focus is on continual growth, increased revenue, and new business.
  • Price-skimming. Price-skimming strategies aim to quickly recoup initial expenses for things like production, manufacturing, and marketing. Businesses charge a higher-than-average price for their product or service to swiftly recover costs, and is commonly employed for organisations launching unique and innovative products to market for the first time.
  • Acquisition. An acquisition strategy relies on business purchases and mergers to grow an organisation. Benefits can include gaining valuable skills and staff – in addition to new funding pools and assets – as well as increasing market share, reducing competition, and diversification of products and services.

What is the main difference between a defensive and offensive strategy?

An offensive business strategy targets an organisation’s competitors. Whether it’s through lower, more competitive prices, new enhancements to products or services, or even marketing plans that directly attack the competition, an offensive strategy aims to take on a bigger share of the market.

A defensive business strategy, meanwhile, protects an organisation from its competitors. It aims to inspire customer loyalty and safeguard its market share, and will use tactics such as incentives, exclusive products – or exclusive arrangements with suppliers or partners – as well as above-average customer service, to entice and retain customers and maintain its competitive position.

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What is advanced programming?

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Advanced programming is shorthand for the advanced-level programming techniques and concepts found in computer science.

Computer programmers typically move through three stages of competency – beginner, intermediate, and advanced – with advanced programmers working on more complex projects and typically earning higher salaries than their more junior colleagues.

Advanced programming concepts

Object-oriented programming

Object-oriented programming, or OOP, is a programming model that all advanced programmers should understand. It’s more advanced than basic procedural programming, which is taught to beginner programmers.

There are four principles of object-oriented programming:

  1. Encapsulation. Encapsulation is effectively the first step of object-oriented programming. It groups related data variables (called properties) and functions (called methods) into single units (called objects) to reduce source code complexity and increase its reusability.
  2. Abstraction. Abstraction essentially contains and conceals the inner-workings of object-oriented programming code to create simpler interfaces. 
  3. Inheritance. Inheritance is object-oriented programming’s mechanism for eliminating redundant code. It means that relevant properties and methods can be grouped into a single object that can then be reused repeatedly – without repeating the code again and again. 
  4. Polymorphism. Polymorphism, meaning many forms, is the technique used in object-oriented programming to render variables, methods, and objects in multiple forms.  

Event-driven programming

Event-driven programming is the programming model that allows for events – like a mouse-click from a user – to determine a programme’s actions. 

Commonly used in graphical user interface (GUI) application or software development, event-driven programming typically relies on user-generated events, such as pressing a key – or series of keys – on a keyboard, clicking a mouse, or touching the screen of a touchscreen device. However, events can also include messages that are passed from one programme to another.

Multithreaded programming

Multithreaded programming is an important component within computer architecture. It’s what allows central processing units (CPUs) to execute multiple sets of instructions – called threads – concurrently as part of a single process.

Operating systems that feature multithreading can perform more quickly and efficiently, switching between the threads within their queues and only loading the new or relevant components. 

Programming for data analysis

Businesses and governments at virtually every level are dependent on data analysis to operate and make informed decisions – and the tools they use for this work require advanced programming techniques and skills.

Through advanced programming, data analysts can:

  • search through large datasets and data types
  • find patterns and spot trends within data
  • build statistical models
  • create dashboards
  • produce useful visualisations to help illustrate data results and learning outcomes
  • efficiently extract data
  • carry out problem-solving tasks

Programming languages

A thorough understanding of programming language fundamentals, as well as expertise in some of the more challenging languages, are prerequisites to moving into advanced programming. It also helps to have knowledge about more complex concepts, such as arrays and recursion, imperative versus functional programming, application programming interfaces (APIs), and programming language specifications.

What are the different levels of programming languages?

Programming languages are typically split into two groups:

  1. High-level languages. These are the languages that people are most familiar with, and are written to be user-centric. High-level languages are typically written in English so that they are accessible to many people for writing and debugging, and include languages such as Python, Java, C, C++, SQL, and so on.
  2. Low-level languages. These languages are machine-oriented – represented in 0 or 1 forms – and include machine-level language and assembly language.

What is the best programming language for beginners?

According to CodeAcademy, the best programming language to learn first will depend on what an individual wants to achieve. The most popular programming languages, however, include:

  • C++, an all-purpose language used to build applications. 
  • C#, Microsoft’s programming language that has been adopted by Windows, Linux (derived from Unix), iOS, and Android, as well as huge numbers of game and mobile app developers.
  • JavaScript, a dynamic programming language that’s typically used for designing interactive websites.
  • Ruby, a general-purpose, dynamic programming language that’s one of the easiest scripting languages to learn.
  • Python, a general-purpose programming language commonly used in data science, machine learning, and web development. It can also support command-line interfaces.
  • SQL, a data-driven programming language commonly used for data analysis.

What are the top 10 programming languages?

TechnoJobs, a job site for IT and technical professionals, states that the top 10 programming languages for 2022 – based on requests from employers and average salaries – are:

  1. Python.
  2. JavaScript.
  3. C.
  4. PHP.
  5. Ruby.
  6. C++.
  7. C#.
  8. Java.
  9. TypeScript.
  10. Perl.

However, it’s worth noting that there are hundreds of programming languages, and the best one will vary depending on the advanced programming assignment, project, or purpose in question.

What’s the most advanced programming language?

Opinions vary on which programming language is the most advanced, challenging, or difficult, but Springboard, a mentoring platform for the tech industry, states the five hardest programming languages are:

  1. C++, because it has complex syntax, permissive language, and ideally requires existing knowledge in C programming before learning the C++ programming language.
  2. Prolog, because of its unconventional language, uncommon data structures, and because it requires a significantly competent compiler.
  3. LISP, because it is a fragmented language with domain-specific solutions, and uses extensive parentheses.
  4. Haskell, because of its jargon.
  5. Malbolge, because it is a self-modifying language that can result in erratic behaviour.

Alternatively, Springboard states that the easiest programming languages to learn are HTML, JavaScript, C, Python, and Java.

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Your coursework will include modules in advanced programming as well as algorithms, artificial intelligence and machine learning, software engineering, and cyber security. As part of this advanced programming course, you will also have the opportunity to explore the social context of computing, such as the social impact of the internet, software piracy, and codes of ethics and conduct.

What is social and public policy?

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Social and public policy is an interdisciplinary and applied social science that aims to critically analyse societal approaches to real-world issues. 

Through a combined application of political science, sociology, economics, law and philosophy, it investigates government action in response to social, demographic and economic development. The purpose of social and public policy is to understand the impacts of policy-making on welfare states and their communities.

There are notable disparities between the two fields. Public policy refers to the actual system of laws and regulatory measures underpinning government action. It comprises resource allocation within the areas of civilian life that impact society at large (such as crime, defence and education). 

Social policy, on the other hand, is more specifically concerned with the administration of social services and welfare. It draws on sociology to address the social context of policy-making, highlighting issues such as growing health disparities, class division, economic inequality and racial discrimination.

In terms of their interrelation, experts differ in opinion. Some believe social policy to be a subset of public policy, while other professionals consider them to be two separate, competing approaches for the same public interest. Overall, social policy is deemed more holistic than public policy.

What are the different types of public policy?

There are three primary types of public policy: regulatory, distributive and redistributive.

Regulatory public policy

This type of public policy ultimately provides the framework for ministerial rulemaking, setting the standards of what is lawful and what isn’t allowed in a bid to protect economic and social welfare. Regulatory public policy establishes the guidelines for developing, implementing and enforcing a system of public protections impacting the economy and civil society, and places restrictions on business practices in aims of keeping the market efficient and fair. Examples include minimum wage legislation and consumer safety law.

Distributive public policy

This type of public policy concerns legislation surrounding government funding into public goods or services that provide for the common good. Examples of this include funding of educational facilities and access to healthcare (such as the free distribution of the Covid-19 vaccines).

Redistributive public policy

These policies involve redistributing government funds from one group of people to a different group of people, to aid the more disadvantaged within society. Examples include progressive taxation systems, welfare distribution programs (such as Universal Credit) and student loans.

What is the purpose of social policy?

Social policy is concerned with how a government meets human needs. As a field of study, it considers the initiatives impacting quality of life (spanning health services, social care, housing, education and financial aid) and critically examines the policies, regulations and financial distribution that shapes the provision of welfare.

In doing so, social policy addresses the social and economic conditions that define barriers to access – such as poverty, age, health, disability and disadvantage. It notes the ways in which policies can be divisive and reinforce privilege and social inequality, with race, gender, class, sexual orientation and economic status acting as contributing factors to social cohesion and division.

Examples of social policy include an examination of antidiscrimination law, equal opportunity employment law, unemployment benefits, pensions, welfare initiatives (such as food stamps) and affordable housing initiatives. 

As British society has become more diverse, divided and disparate, social policy continues to expand fields of interest, including: 

  • regional inequalities;
  • the impact of climate change on vulnerable communities (such as those living in urban developments); 
  • government approaches to immigration and citizen’s rights; 
  • minimum living wages and modern slavery; 
  • education and social mobility; 
  • the policing of the poor.

At a top level, social policy also addresses how governments respond to global challenges (such as migration, pandemics and globalisation). As the divisions between the most privileged and vulnerable members of the community continue to grow, the case for social policy becomes ever more imperative to the survival of a fair and functioning society.

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Fulfil your social purpose as you become equipped with the knowledge and expertise needed to shape, influence and improve policy-making on a local, national and global level. 

Develop a range of transferable skills, such as critical thinking, and evaluating and commissioning research, in order to make a positive impact on improving public life.

 

What are the toughest challenges of leadership?

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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

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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.

Get ahead with AI

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Business across borders: The importance of international business law

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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. 

Become an international business leader

Whether you’re a budding entrepreneur looking to launch their own business, an aspiring global brand ambassador or fancy yourself a future Fortune 500 business leader, you can brush up on key learnings as part of University of York’s 100% online MSc in International Business Leadership and Management.

This postgraduate programme places particular emphasis on the challenges associated with business management and global trade, marketing and sales, and provides an excellent overview of relevant management disciplines. 

Obtain skills vital for professional adaptability and employability across industries, functions and roles, preparing you for a potential career within a world-leading economy.

How do algorithms work?

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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.

Unmask the secrets of data science 

Data is being collected at unprecedented speed and scale, becoming an ever-increasing part of modern life. While ‘big data’ is big business, it is of little use without big insight. The skills required to develop such insight are in short supply, and the expertise needed to extract information and value from today’s data couldn’t be more in demand. 

Study the University of York’s 100% online MSc in Computer Science with Data Analytics and enhance your skills in computational thinking, problem-solving and software development, while advancing your knowledge of machine learning, data analytics, data mining and text analysis. Armed with this sought-after specialist knowledge, you’ll graduate the course with an abundance of career prospects in this lucrative field.

 

Tech basics: An introduction to text editors

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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?

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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.

Want to build an ethical business?

Business ethics are one of many modules built into the University of York’s 100% online MSc in International Business Leadership and Management. 

The course places particular emphasis on the challenges associated with business management and global trade, marketing and sales, and provides an excellent overview of relevant management disciplines. Enrol now to obtain vital skills for professional adaptability and employability across industries, functions and roles.

Data architecture: the digital backbone of a business

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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.

Take a dive into big data

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Get to grips with data mining, big data, text analysis, software development and programming, arming you with robust theoretical knowledge to step into the data sector.

The real world impact of facial detection and recognition

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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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