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Advancements in technology continue to mould the way people work and the types of roles that organisations are hiring. Those with skills in python programming, cyber security, and Blockchain, will continue to be in high demand this year, along with the almost omnipotent AI and big data competencies that are fundamentally changing roles both inside and outside the tech sector.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. The main machine learning programming languages are Python, C++, Java, R, and JavaScript, but Julia, Scala, MATLAB, and SAS also fall under this specialism. The languages used are dictated by the environment and sector they are applied in. Data scientists in financial services may use a combination of Python, SAS, and Matlab, to enable all aspects of data modelling, algorithm development, risk/statistical analysis, web analytics, and data extraction to be incorporated.
Big data is a term that describes the large volume of data – both structured (from internal CRM/data systems or external Information providers) and unstructured (internet/web-based) – that inundates a business on a day-to-day basis. Big data can be analysed for insights that lead to better business decisions (e.g. product enhancement) and commercial strategy. Defined by the 3 ‘V’s (volume, velocity and variety) key challenges revolve around data capture, storage, analysis, transfer, querying, and privacy. Key technologies/tools are: Amazon EMR, Apache (Hive, Pig, Spark), MapR, Hadoop, MongoDB, and NoSQL.
Python is an increasingly popular tool for data analysis and data science. Python was explicitly designed a) so code written in Python would be easy for humans to read, and b) to minimise the amount of time required to write code. Many highly trafficked websites, such as YouTube, Spotify, and Dropbox, are created using Python as it allows you to create, update, store, and retrieve data in a variety of libraries and operating systems. Python is widely used for data science, statistical analysis, modelling, and machine learning, using extensions such as SciKit, SciPy, Pandas and Numpy.
Often regarded as the ‘Holy Grail’ within the technology community, artificial intelligence (AI) is an area of computer science that emphasises the creation of intelligent machines and systems that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: predictive analytics, speech recognition, learning, planning, and problem-solving. Knowledge engineering and machine learning sit at the heart of AI engineering, and are vastly complex. Real-life situations when you come across the advances in this field include Siri, Alexa, Pandora, Netflix, Chatbots, and Smart Cars. Development languages are those mirrored in machine learning.
‘Cyber-security’ is the practice of defending computers servers, mobile devices, electronic systems, networks, and data from malicious attacks. The term applies in a variety of contexts but is more aligned to the core technical operations elements of information security (with InfoSec being the umbrella term). New security threats emerge every single day, so it's important that professionals in this space are adaptable and inquisitive, and able to think on their feet when it comes to devising counter-strategies.
Data visualisation tools go beyond the standard charts and graphs used in Excel spreadsheets, displaying data in sophisticated ways such as infographics, dials/gauges, sparklines, heat maps, and fever charts. Visualisation tools such as Tableau, QlikView, PowerBI, and Microstrategy help companies to make better strategic and commercial decisions by pulling data from finance, sales, marketing and operations systems. The creation of dashboards within these tools enables end users to manipulate them or drill into the data for enhanced querying and analysis. The quality of output is dependent on the quality of data input from databases/data warehouses and is normally extracted via coding in Microsoft SQL or Oracle SQL (PL/SQL).
Data governance is a data management concept that enables an organisation to ensure that high data quality exists throughout the complete lifecycle of the data. The key focus areas of data governance include availability, usability, consistency, data integrity, and data security. This includes establishing processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data which an enterprise has, can be used by the entire organisation effectively.
Blockchain is a growing list of records (called blocks) which are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data. A Blockchain is resistant to modification and is "an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable, and permanent way". For use as a distributed ledger, a blockchain is typically managed by a peer-to-peer network validating new blocks and cannot be altered retroactively without alteration of all subsequent blocks. Originally invented in 2008 to serve as the public transaction ledger of the cryptocurrency bitcoin, the invention of the blockchain for bitcoin made it the first digital currency to solve the “double-spending” problem without the need of a trusted authority or central server.
Information security is a term that describes the process of ‘safeguarding’ a firm’s intellectual property and ‘data’ (otherwise known as ‘data security’). Organisations now hold vast volumes of data (in both structured and unstructured forms). A firm needs to have a clearly defined risk management strategy, on how to protect their data. Furthermore, a firm needs to have the correct processes, tools, and policies, necessary to prevent, detect, document, and counteract threats to digital and non-digital information. While the threat of data breaches and leaks increases, so too does the scrutiny which is placed on firms, the level of compliance, and IT resilience that they are required to meet.
One of the key skills that many businesses should look for is commerciality within their sector and current market with the view to maximise profit. A professional should be aware of, and involved in, any upcoming challenges and opportunities that the business is facing, now or in the future, and make the best of them. The ability to construct and oversee a deal, which will increase profit, or the ability to consider the wider picture to improve processes and deliver better revenue, is a desirable skill across sectors.
