We gauged the perspectives of experts in data science, asking them about the biggest emerging trends in data science

As one of the fastest evolving areas of tech, data science has seen a rise up the corporate agenda as less and less leaders base business decisions on guess work. With added capabilities such as artificial intelligence (AI) and the edge complementing the work of data scientists, the field is becoming more accessible to employees, but this still requires training of data skills, on the most part. In this article, we explore some key emerging trends in data science, as believed by experts in the field.

Increased involvement of AI and ML

Firstly, it’s believed that the involvement of AI and machine learning (ML) will increase further, and enable more industries to become truly data-centric.

“As businesses start to see the benefits of artificial intelligence and machine learning enabled platforms, they will invest in these technologies further,” said Douggie Melville-Clarke, head of data science at Duco.

“In fact, the Duco State of Reconciliation report – which surveyed 300 heads of global reconciliation utilities, including chief operating officers, heads of financial control and heads of finance transformation – found that 42% of those surveyed will investigate the use of more machine learning in 2021 for the purposes of intelligent data automation.”

Data science in insurance

Melville-Clarke went on to cite the insurance industry, often perceived as a sector that’s had difficulty innovating due to high levels of regulation, as an example for future success when it comes to data science.

He explained: “The insurance industry, for example, has already embraced automation for processes such as underwriting and quote generation. But the more valuable use of artificial intelligence and machine learning is to increase your service and market share through uses like constrained customisation.

“Personalisation is one of the key ways that banks and insurance companies can differentiate themselves, but without machine learning this can be a lengthy and expensive process.

“Machine learning can help these industries tailor their products to meet the individual consumers’ needs in a much more cost-effective way, bettering the customer experience and increasing customisation.”

The evolution of hyperautomation

Along with rising use of AI and ML models, organisations have been combining AI with robotic process automation (RPA), to reduce operational costs through automating decision making. This trend, known as hyperautomation, is predicted to help companies to continue innovating fast in a post-COVID environment in the next few years.

“In many ways, this isn’t a new concept — the key goal of enterprise investment in data science for the past decade has been to automate decision-making processes based on AI and ML,” explained Rich Pugh, co-founder and chief data scientist at Mango Solutions, an Ascent company.

“What is new here is that hyperautomation is underpinned by an ‘RPA-first’ approach that can turbocharge process automation and drive increased collaboration across analytic and IT functions.

“Business leaders need to focus on how to harness enterprise automation and continuous intelligence to elevate the customer experience. Whether that is embedding intelligent thinking into the processes that will drive more informed decision making, such as deploying automation around pricing decisions to deliver a more efficient and personalised service, or leveraging richer real-time customer insights in conjunction with automation to execute highly relevant offers and new services at speed.

“Embarking on the hyperautomation journey begins with achieving some realistic and measurable future outcomes. Specifically, this should include aiming for high-value processes, focusing on automation and change, and initiating a structure to gather the data that will enable future success.”

SaaS and self-service

Dan Sommer, senior director at Qlik, identified software-as-a-service (SaaS) and a self-service approach among users, along with a shift in advanced analytics, as a notable emerging trend in data science.

“To those in the industry, it’s clear that SaaS will be everyone’s new best friend – with a greater migration of databases and applications from on premise to cloud environments,” said Sommer.

“Cloud computing has helped many businesses, organisations, and schools to keep the lights on in virtual environments – and we’re now going to see an enhanced focus on SaaS as hybrid operations look set to remain.

“In addition, we’ll see self-service evolving to self-sufficiency when it comes to effectively using data and analytics. Empowering users to access data, insights and business logic earlier and more intuitively will enable the move from visualisation self-service to data self-sufficiency in the near future.

“Finally, advanced analytics need to look different. In uncertain times, we can no longer count on backward-looking data to build a comprehensive model of the future. Instead, we need to give particular focus to, rather than exclude outliers – and this will define how we tackle threats going forward too.”

Data fabric

With employees gradually becoming more comfortable with using data science tools to make decisions, while aided by automation and machine intelligence, a concept that’s materialised as a hot topic for the next stage of development is the concept of ‘data fabric’.

Trevor Morgan, product manager at comforte AG, explained: “A data fabric is more of an architectural overlay on top of massive enterprise data ecosystems. The data fabric unifies disparate data sources and streams across many different topologies (both on-premise and in the cloud), and provides multiple ways of accessing and working with that data for organisational personnel, and with the larger fabric as a contextual backdrop.

“For large enterprises that are moving with hyper-agility while working with multiple or many Big Data environments, data fabric technology will provide the means to harness all this information and make it workable throughout the enterprise.”

New career paths and roles

Another important trend to consider regarding the future of data science is the new career paths and jobs that are set to emerge in the coming years.

“According to the World Economic Forum (WEF)’s Future of Job’s Report 2020, 94% of UK employers plan to hire new permanent staff with skills relevant to new technologies and expect existing employees to pick up new skills on the job,” said Anthony Tattersall, vice-president, enterprise, EMEA at Coursera.

“What’s more, WEF’s top emerging jobs in the UK — data scientists, AI and machine learning specialists, big data and Internet of Things — all call for skills of this nature.

“We therefore envision access to a variety of job-relevant credentials, including a path to entry-level digital jobs, will be key to reskilling at scale and accelerating economic recovery in the years ahead.”

The ‘Industrial Data Scientist’

In regards to new roles to emerge in data science, Adi Pendyala, senior director at Aspen Technology, predicts the emergence of the ‘Industrial Data Scientist’: “These scientists will be a new breed of tech-driven, data-empowered domain experts with access to more industrial data than ever before, as well as the accessible AI/ML and analytics tools needed to translate that information into actionable intelligence across the enterprise.

“Industrial data scientists will represent a new kind of crossroads between our traditional understanding of citizen data scientists and industrial domain experts: workers who possess the domain expertise of the latter but are increasingly shifting over to the data realm occupied by the former.”

New tools

Many organisations are being impacted by a shortage of data scientists in proportion to demand, but Julien Alteirac, regional vice-president, UK&I at Snowflake, believes that new tools, powered by ML, could help to mitigate this skills gap in the near future.

“When it comes to analysing data, most organisations employ an abundance of data analysts and a limited number of data scientists, due in large part to the limited supply and high costs associated with data scientists,” said Alteirac.

“Since analysts lack the data science expertise required to build ML models, data scientists have become a potential bottleneck for broadening the use of ML. However, new and improved ML tools which are more user-friendly are helping organisations realise the power of data science.

“Data analysts are empowered with access to powerful models without needing to manually build them. Specifically, automated machine learning (AutoML) and AI services via APIs are removing the need to manually prepare data and then build and train models. AutoML tools and AI services lower the barrier to entry for ML, so almost anyone will now be able to access and use data science without requiring an academic background.”

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Artificial intelligence (AI) is one of the most popular topics in any works of fiction set in the future. It’s easy to see why because it is one of the most used assets in modern society. Anything with a computer chip runs with AI. Your desktop and mobile devices are some examples. Other products like cars and refrigerators with ‘smart’ in the name also have AI.

What Is Artificial Intelligence?

Artificial intelligence is a programmed thinking process. Each one is created differently for specific purposes. Depending on the reason for its creation, it can be simple such as doing the same task over and over.

It can also be complex at the level that it can represent a human being for decisions that need a deep understanding of logistics. Some AI are built to act as super-computers capable of serving multiple users across the internet. Sometimes, they’re just made to automate electronic games like slots in sites like Casinodays or enemy fighters in arcades.

Commonly Known Categories of AI

Many industries also use AI. It automates tasks that can be seen as tedious so human resources can be focused on other stuff that needs smarter decision making. It also lessens the possibility of human error in an otherwise streamlined production process. The use of AI decreases the need for human operators for heavy machines as well. Here are some examples of AI used in different industries.

Robotics

The word ‘robot’ is often imagined as humanoid machines. It can take so many forms, depending on the task they are made for. If you enter a factory for pharmaceutical labs or manufacturing companies, you can see machines with no human operators. Some look like cranes while others are huge blocks where materials go in and products go out.

These are ‘embodied AI’ or ‘AI with bodies’. They help make mass production faster and more accurate. When used in healthcare, robots can help readjust an entire room or specific machines to cater to the patients’ needs.

AI also works great to ensure the safety of workers. In mining and the military, robots are sent to places that are too dangerous for a person to enter. That being said, robotics are also used for processing minerals and manufacturing weapons.

Entertainment Bot

There are plenty of AI created for entertainment purposes. They can go by different names but most would simply refer to them as ‘entertainment bots’. The most popular of which are video game AI to serve as the players’ virtual opponent. AI is present in every video game. It can be the armed NPC trying to counter the player’s tactics or the game itself trying to generate a new, unique arena for each level. AI is also present in gambling games.

Informational Bot

Informational bots, or information bots, are meant to help visitors when looking for specific articles or conversation threads related to a keyword they want to use. Specialized forums, institutions, and online casinos like Casinodays have them. They work like search engines but the contents they find are limited to the pages of the website.

Chatbot

Chatbots are AI programmed to respond to users’ questions. Some were made for emotional or psychological help like Replika. Others serve as virtual assistants representing websites like the Wall Street Journal. Just like informational bots, chatbots help users find their way around a database. The main difference is that they are also programmed to respond directly just as a human agent would.

Most are designed to be formal while others can be casual, depending on how the company wants to be represented. Many c hatbots are also designed to learn from their interactions. Some of them were given the freedom to operate their social media accounts or scour the internet for references.

Web Crawlers/Spiders

Search engines like Google and Bing use AI to browse the entire internet for content related to the keywords you give. The same AI also curates its findings by sorting them in the order of the most relevant to the least.

The appropriately named ‘web crawlers’ scour the World Wide Web even when you are not searching yet. These AI will make copies of data that exist in the network then index them. When you do eventually look for them, the browser can produce them quickly.

Automation Bots

Automation bots perhaps the kind of AI that is mostly offered for individuals or small businesses. They are programmed to complete actions on a person’s behalf. They don’t need prompts or commands to work for an entire day. Most of the time, they are left unchecked for a full week.

Just like robots, they were meant to complete simple tasks that are necessary but too tedious for a human worker. The difference is that they can be implemented on the internet or software as a plug-in program. They are mostly used for many things related to finance.

For investors, editing the portfolio regularly is a must, but the actions that need to be done can be as easy as a set of if-and-then instructions. That is one of the most basic programming commands. Many companies develop financing bots. Some are more flexible than others so the client can give it more advanced work.

Bottom Line: AI Greatly Helps Modern Society

AI offers a wide variety of services. Many can use them for important and streamlined jobs such as mass production and data gathering. Others can serve as virtual playmates such as enemy soldiers in a shooting game or a virtual dealer in Casinodays.

The main purpose of an AI is to make certain tasks automatically so there’s no need to hire skilled personnel. Some of these tasks may need skills such as mathematical precision and careful hands. Such skills are hard to find in a person so hiring a large number of skilled professionals is not economically plausible. AI technology grows along with the innovation of computer science and many industries eagerly await what the future holds.

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