· Financial services. Financial institutions regularly use predictive analytics to drive algorithmic trading of stocks, assess business risks for loan approvals, detect fraud, and help manage credit and investment portfolios for clients.
· Customer service. Many organisations incorporate deep learning technology into their customer service processes. Chatbots—used in a variety of applications, services, and customer service portals—are a straightforward form of AI. Traditional chatbots use natural language and even visual recognition, commonly found in call centre-like menus. However, more sophisticated chatbot solutions attempt to determine, through learning, if there are multiple responses to ambiguous questions. Based on the responses it receives, the chatbot then tries to answer these questions directly or route the conversation to a human user. Virtual assistants like Apple's Siri, Amazon Alexa, or Google Assistant extends the idea of a chatbot by enabling speech recognition functionality. This creates a new method to engage users in a personalised way.
· Healthcare. The healthcare industry has benefited greatly from deep learning capabilities ever since the digitisation of hospital records and images. Image recognition applications can support medical imaging specialists and radiologists, helping them analyse and assess more images in less time.
Prescriptive analytics, an area of business analytics dedicated to finding the best course of action for a given situation, is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices given the known parameters. It can also suggest decision options for how to take advantage of a future opportunity or mitigate a future risk and illustrate the implications of each decision option. In practice, prescriptive analytics can continually and automatically process new data to improve the accuracy of predictions and provide better decision options.
Specific techniques used in prescriptive analytics include optimisation, simulation, game theory and decision-analysis methods. Advancements in the speed of computing and the development of complex mathematical algorithms applied to the data sets have boosted demand for prescriptive analysis skills.
Two fundamental concepts in computer and information security are the Security Model, which outlines how security is to be implemented—in other words, providing a “blueprint”—and the Security Architecture of a computer system, which fulfills this blueprint. Security architecture is a view of the overall system architecture from a security point and how the system is put together to satisfy the security requirements. It describes the components of the logical hardware, operating system, and software security components, and how to implement those components to architect, build and evaluate the security of computer systems. With cybersecurity related skills gaining prominence and the threat landscape continuing to be a core business issue, we expect security models and architecting skills to continue to be strong going forward.
- Cryptography (encryption, VPN, SSL/TLS, Hybrids)
Average pay premium: 18% of base salary equivalent
Market value increase: 20% (in the six months through July 1, 2022)
Cryptography (or cryptology) is the practice and study of techniques for secure communication in the presence of third parties called adversaries. More generally, cryptography is about constructing and analysing protocols that prevent third parties or the public from reading private messages. Various aspects in information security such as data confidentiality, data integrity, authentication, and non-repudiation are central to modern cryptography. Modern cryptography exists at the intersection of the disciplines of mathematics, computer science, electrical engineering, communication science, and physics. Applications of cryptography include electronic commerce, chip-based payment cards, digital currencies, computer passwords, and military communications.
Modern cryptography is heavily based on mathematical theory and computer science practice. Cryptographic algorithms are designed around computational hardness assumptions, making such algorithms hard to break in practice by any adversary. It is theoretically possible to break such a system, but it is infeasible to do so by any known practical means. These schemes are therefore termed computationally secure; theoretical advances, e.g., improvements in integer factorisation algorithms, and faster computing technology require these solutions to be continually adapted. There exist information-theoretically secure schemes that provably cannot be broken even with unlimited computing power—an example is the one-time pad—but these schemes are more difficult to use in practice than the best theoretically breakable but computationally secure mechanisms.
- [Tie] Data Engineering
Data Strategy
Functional Programming
Zachman Framework
Average pay premium: 18% of base salary equivalent
Market value increase: 12.5% (in the six months through July 1, 2022)
Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In order for that work to ultimately have any value, there also have to be mechanisms for applying it to real-world operations in some way. Those are both engineering tasks: the application of science to practical, functioning systems.
If you have a business, you have data. But data by itself won’t let you optimise and improve your business: you need a Data Strategy if you want to turn data into value. A business without a data strategy is poorly positioned to operate efficiently and profitably or to grow successfully. Data strategy refers to the tools, processes, and rules that define how to manage, analyse, and act upon business data. A data strategy helps you to make informed decisions based on your data. It also helps you keep your data safe and compliant. Virtually every business collects data in multiple forms, and a data strategy enables a business to manage and interpret all of that data.
It also puts a business in a strong position to solve challenges such as:
- Slow and inefficient business processes
- Data privacy, data integrity, and data quality issues that undercut your ability to analyse data
- Lack of deep understanding of critical parts of the business (customers, supply chain, competitive landscape, etc.) and the processes that make them tick
- A lack of clarity about current business needs (a problem that descriptive analytics can help solve) and goals (which predictive and prescriptive analytics can help identify)
- Inefficient movement of data between different parts of the business, or duplication of data by multiple business units
Often abbreviated FP, Functional Programming is a programming paradigm, meaning that it is a way of thinking about software construction based on some fundamental, defining principles (listed above). Other examples of programming paradigms include object-oriented programming and procedural programming. It promotes a coding style that helps developers write code that is short, concise, and maintainable. For example, pattern matching allows developers to easily destructure data and access its contents. When mixed with guards, pattern matching allows us to elegantly match and assert specific conditions for some code to execute. Functional code tends to be more concise, more predictable, and easier to test than imperative or object-oriented code — but if you’re unfamiliar with it and the common patterns associated with it, functional code can also seem a lot denser, and the related literature can be impenetrable to newcomers.
Functional programming has become a hot topic in the JavaScript world. Just a few years ago, few JavaScript programmers even knew what functional programming is, but every large application codebase in the past four years makes heavy use of functional programming ideas. It is the process of building software by composing pure functions, avoiding shared state, mutable data, and side-effects. Functional programming is declarative rather than imperative, and application state flows through pure functions. Contrast with object-oriented programming, where application state is usually shared and collocated with methods in objects.
The Zachman Framework is an enterprise ontology and a fundamental structure for enterprise architecture which provides a formal and structured way of viewing and defining an enterprise. It is not a methodology in that it does not imply any specific method or process for collecting, managing, or using the information that it describes. Rather, it is an ontology whereby a schema for organising architectural artifacts (design documents, specifications, and models) is used to take into account both who the artifact targets (for example, business owner and builder) and what particular issue (for example, data and functionality) is being addressed. The framework is a logical structure for classifying and organising the descriptive representations of an enterprise
The basic idea behind the Zachman Framework is that the same complex thing or item can be described for different purposes in different ways using different types of descriptions (e.g., textual, graphical). As such, it allows different people to look at the same thing from different perspectives, creating a holistic view of the environment. It is significant to both the management of the enterprise and the actors involved in the development of enterprise systems. This has made tools such as this extremely valuable in today’s complex environment of pandemics, recessions, and accelerating technology changes.
- [Tie] Complex Event Processing/Event Correlation
Data Architecture
Microservices
Natural language processing
Scaled Agile Framework (SAFe)
Average pay premium: 18% of base salary equivalent
Market value increase: 5.9% (in the six months through July 1, 2022)
Complex event processing, or CEP, consists of a set of concepts and techniques developed in the early 1990s for processing real-time events and extracting information from event streams as they arrive. The goal of complex event processing is to identify and analyse meaningful events (such as opportunities or threats) in real-time situations and respond to them as quickly as possible. At the root of CEP is event correlation, a technique for making sense of a large number of events and pinpointing the few events that are most important in that mass of information. This is accomplished by looking for and analysing relationships between events. These events may be happening across the various layers of an organisation as sales leads, orders, or customer service calls. Or, they may be news items, text messages, social media posts, stock market feeds, traffic reports, weather reports, or other kinds of data. An event may also be defined as a "change of state," when a measurement exceeds a predefined threshold of time, temperature, or other value. The vast amount of information available about events is sometimes referred to as the event cloud.
CEP has become an enabling technology in many systems that are used to take immediate action in response to incoming streams of events, effectively helping the business side communicate better with IT and service departments. Applications can be found in many sectors of business including stock market trading systems, mobile devices, internet operations, fraud detection, security monitoring, business activity monitoring, and governmental intelligence gathering.
The goal of Data Architecture is to translate business needs into data and system requirements and to manage data and its flow through the enterprise. It describes the structure of an organisation's logical and physical data assets and data management resources. Data architecture is an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organisations. An organisation's data architecture is the purview of data architects.
Six principles form the foundation of modern data architecture:
- Data is a shared asset
- Users require adequate access to data
- Security is essential
- Common vocabularies ensure common understanding
- Data should be curated
- Data flows should be optimised for agility