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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In order to get the maximum out of technology, businesses are adopting data analytics trends

The power of data and analytics is no longer hidden. Today businesses of all sizes, starting from small to medium and big are availing data analytics in their routine to streamline operations. Without data analytics, companies are blind and deaf. Data analytics allows businesses to understand the market and their customers’ preferences and suggests solutions that could yield big profits. A rough estimation suggests that data analytics in business will increase five-fold by 2024 because of the rapid rise in technology adoption. Once upon a time, data analytics was confined to the tech industry. Only IT professionals, data engineers, and top-level enterprise executives got their hands on the technology. But things changed when laymen started embracing artificial intelligence. Today, big data, machine learning, cloud computing, data analytics, and many more technologies are a part of our everyday life. Many companies unveil data analytics in business to optimize business processes, cut costs, increase revenue, improve competitiveness, and accelerate innovation. In order to get the maximum out of technology, businesses should adopt recent data analytics trends. Data analytics trends such as decision intelligence, edge computing, data storytelling, etc are unraveling a world where businesses can understand their customers and address their needs like never before. In this article, Analytics Insight takes you through some of the top data analytics trends that businesses should follow in 2021.

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Top Data Analytics Trends for Business

Moving to Scalable AI

Post the Covid-19’s first and second wave, people’s preference has drastically changed. Businesses can no more use the historical data they have collected so far to optimize business decisions. Therefore, companies are moving to scalable and responsible AI that could pave the way for more data analytics and decision-making. Gartner predicts that 75% of enterprises will shift from piloting to operationalizing AI by 2024, driving a five times increase in streaming data and analytics infrastructure. Besides, healthcare and pharmaceutical companies are using scalable AI to expand their medical supplies and manage the supply chain.

Decision Intelligence as the Powerhouse of Decision Making

In modern times, many companies make decisions based on what machines suggest. Yes, we are already there. Artificial intelligence-powered machines are created by humans to analyze the overall performance of the company and its outcomes. Therefore, they have better knowledge than human employees in decision-making. Decision intelligence is a composite field containing artificial intelligence and data science along with some concepts of managerial science. It helps company executives and stakeholders pick the right choice based on reliable data.

Augmented Data Management to Shorten Data Delivery Time

The next goal for the business is to get data in real-time and acquire answers at the earliest. To move further with the motive, companies are adopting a new method called augmented data management. Organizations are now utilizing machine learning, data fabrics, and active metadata to connect, optimize and automate data management processes to shorten the time of data delivery. In the future, augmented data management will help companies reduce the delivery time by 30%. They can also convert metadata with the help of machine learning and artificial intelligence techniques from getting used in auditing, lineage, and reporting to powering dynamic systems. Considering its impacts, data analytics leaders are working on augmented data management to simplify and consolidate their architecture.

Edge Data and Analytics at the Core of Operations

The inflow of data has increased tenfold in recent years, thanks to the spiking adoption of IoT devices. However, businesses are in the positive end when it comes to benefiting from data. But a complex task here is their role to analyze the incoming data in real-time. Unfortunately, companies don’t have the leniency to decide on what data they want to be processed, instead, the concept has moved to how they are implying edge data analytics to come up with decisions rapidly. It also reduces data latency and enhances data processing speeds.

The Stronghold of the Cloud Continues

Initially, cloud architecture came into the business picture when companies moved from office spaces to the remote mode of working due to the pandemic. Although the pandemic is half gone and the world is preparing to get back to normal, cloud computing seems to have a stronghold on business operations. According to Gartner, public cloud services are expected to underpin 90% of all data analytics innovation by 2022. Besides, cloud data warehouses and data lakes have emerged as go-to data storage options for collating and processing massive volumes of data to run artificial intelligence and machine learning projects. Even research and development initiatives are moving to cloud methods to minimize cost and fast-track trials.

No more Big Data, Let’s go to Small and Wide Data

For almost two decades, big data was the center of attraction. Big data was vastly hailed for its nature to provide answers. Although it can’t perform alone, big data was often seen as the core of any decision-making process. Finally, businesses are moving from big data to small and wide data. The emerging trend in data is expected to solve a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases.

Automation at its Best

Business outcomes rely on data. But over the past few years, big data is getting more complex. For example, the inflow of data is in various forms like videos, images, documents, texts, files, etc. Besides, there are also two other categories called structured and unstructured data, which makes data processing even more hectic. The only way out of this is by automating the process of data discovery, preparation, and blending of disparate data. Besides, automating the data discovery and analysis process helps analysts focus on high-value-added activities.

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Analytics Insight provides a glimpse at the Top 10 AI Trends you must know in 2021.

It is very difficult to find out one industry, that has not adopted smart machines and models, integrated with Artificial Intelligence across the world. The world has embraced the amazing functionalities of Artificial Intelligence and machine learning algorithms to boost productivity as well as ensure higher customer engagement. People are using these smart machines, even in their homes to adjust to this fast-paced life in the tech-driven era. Indeed, there is an immense scope of Artificial Intelligence in the upcoming years to enhance the standard of living of society.  The Artificial Intelligence market size is expected to reach US$266.92 billion by 2027, with a CAGR of 33.2%. Artificial Intelligence trends are instigating organisations as well as common people to wait for further new AI innovations. Hence, let’s take a glimpse at the top 10 Artificial Intelligence trends in 2021 to know what is waiting for us in the nearby future.

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Top 10 Artificial Intelligence Trends you must know in 2021

Ethical AI

Some reputed companies such as Google, Microsoft, Apple, Facebook and other tech giants are building ethical AI to follow an ethical framework with four essential principles for effective data governance— fairness, accountability, transparency well to explainability. This is currently the most popular Artificial Intelligence trend in 2021 for providing the inside look into its own system to stakeholders. These companies are initiating multiple programmes and research to encourage other companies to adopt ethical AI with personalised strategies as per the requirements of a business.

Explainable AI

Explainable AI is a part of ethical AI that provides a complete explanation of how the Artificial Intelligence models and machine learning algorithms are working inside to generate the appropriate meaningful business insights and predict the future. Companies leveraging disruptive technologies are required to maintain transparency to stakeholders with a full explanation. But it is creating controversy because companies do not want to disclose all their steps and processes to the public for patent purposes in a cut-throat competitive market.

Predictive Analytics

Predictive analytics enables all kinds of businesses to identify the trends of consumers for a better understanding of consumer behaviour in the current scenario. It predicts all potential responses from the target audience by employing personalised data that are collected for a long time. The advancement in Artificial Intelligence and machine learning algorithms are providing more accurate predictions and insights to maintain better customer engagement and gain higher ROI from the global market.

Emotional AI

Emotional AI, is one of the most popular Artificial Intelligence trends in 2021because this technology can sense, learn and interact with multiple human emotions. It is also known as affective computing that enhances human-robot communication to a whole new level. Emotional AI can understand consumer behaviour through verbal as well as non-verbal signals. Hi-tech cameras and Chatbots can easily detect various types of human emotions by studying the reactions to certain contents, products and services. This advancement in Artificial Intelligence has an immense scope in the retail industry in the nearby future.

AI with AR and VR

Augmented Reality and Virtual reality are already providing immersive experiences to consumers as well as industries all around the world in these recent years. The combination of these three disruptive technologies- Artificial Intelligence, Augmented Reality and Virtual Reality has the potential to revolutionise the world with its amazing functionalities. The trio has already started to transform the relationship between customers and companies by providing extra personalisation and customisation of products and services to meet the needs and wants of each customer.

AI in Robotics

Robotics is taking over industries with its useful functionalities in every possible way around the world. There is a common presence of Artificial Intelligence in Robotics solutions that makes robots smarter and intelligent like never before. It is, indeed, a powerful combination to enhance customer service cost-effectively. Robots can perform successful surgeries, dance, protect employees from harmful environments and many more activities by leveraging Artificial Intelligence into RPA.

AI in Cybersecurity

The data-driven world has created a data explosion in these recent years that is difficult for organisations to protect the sets from malicious hackers. The integration of Artificial Intelligence in cybersecurity has created more advanced and powerful defence against harmful cyberattacks like phishing, ransomware, virus and so on. AI can instantly detect any unusual activity in the existing systems and alert the employees as soon as possible. It is making it more difficult for hackers and frauds to enter any system. Artificial Intelligence enhances cybersecurity through intelligent code analysis and configuration analysis with activity monitoring.

AI in Computer Vision

The integration of Artificial Intelligence, in Computer Vision, has transformed existing computer systems into smart computers with the following functionalities— analysing human posture and movements, tracking humans and vehicles for collecting data as well as for law enforcement officers, analysing videos with the help of hi-tech CCTVs, facial recognition of the needed person, detecting different levels of diseases as well as identifying objects for autonomous vehicles.

AI in IT

The IT sector has embraced the functionalities of Artificial Intelligence amidst the ongoing coronavirus pandemic. It is continuously revolutionising the IT sector and helping in boosting productivity efficiently. Artificial Intelligence is providing the utmost security to protect the confidential data from potential threats and data breaches, helping programmers in writing better code by overcoming software bugs, taking over the boring, tedious and repetitive back-end duties, identifying and predicting complex problems, assuring the quality of products and services and much more assistance without any human intervention.

AI in IoT

Artificial Intelligence, has a tremendous scope in IoT (Internet of Things) with the help of 5G network. The implementation of Artificial Intelligence into IoT can help smart devices such as wearable devices, virtual assistance, refrigerators, etc. to analyse data and make smart decisions efficiently based on the collected data without any human intervention. It is used to optimise a system and enhance performance to meet the needs and wants of the target audience.

The post TOP 10 ARTIFICIAL INTELLIGENCE TRENDS YOU MUST KNOW IN 2021 appeared first on Artificial Intelligence.

Marketing Analytics Enable Business Organizations to Transform

With an objective to evolve interminably, business organizations and companies deem marketing analytics as to the crucial slices in the realm of marketing as they are the primary drivers of successful markets. Marketing analytics promise to deliver more bottom-line impact. Especially, in times of uncertainty, which tends to inflict adversities on markets, market analytics assist to assess and evaluate the appropriate statuses of markets in order to pave ways for better planning and loss compensation.

With a paradigm shift in 2021, marketing analytics is turning virtual that comes with the backings of machine learning and artificial intelligence. It enables business organizations to improve their target advertisements and remarketing strategies to optimize their ads through advanced marketing attribution, thereby, increasing customer loyalty and customer retention.

Analytics Insight anticipates the ten best marketing analytics trends that can be leveraged for a business facelift.

The Best Marketing Analytics Trends

The Rise of Real-time Marketing Analytics

Action reciprocations in real-time is a soaring trend, induced by Covid. Business organizations and companies strive to revert back with solutions to customer queries in real-time. Organizations run low-latency customer data platforms to let marketers know about the current position and the success of their marketing campaigns and strategies.

Real-time marketing analytics also help marketers to detect underlying threats and problems. Underlying threat detection in a market is also known as SWOT analysis.

Emphasis on Data Security and Regulatory Compliance

Insulating market data against cybercrimes and cyber breaches is an important issue of address today. 2021 has witnessed a heightened increase in data breach cases that have also fractured the business infrastructures to degrees unimaginable. This has led marketers to invest more in technologies that facilitate encryption, access control, network monitoring, and physical security measures.

Customer Privacy and Data Handling

Protection of consumer and customer privacy is also imperative for marketers. To materialize consumer and customer privacy, marketers are deploying software by which users can opt-out, purging out data once a user has left a problem.

Accelerating Adoption of Predictive Analytics

Predictive analytics, as the name suggests, helps to anticipate future outcomes, based on the analyses of historical data of an organization. Predictive analyses are executed using software powered by machine learning. Predictive analytics encompasses a look-alike modeling structure, which identifies prospects that are likely to turn into high-value customers.

Enhanced Investment in First-Party Data

2020 marked the extinction of third-party cookies when Google announced the exodus of third-party cookies out of Chrome. Cookies are important to track customer behavior on a business website.

However, in order to make up for the loss, marketers turned to invest more in first-party data that also ensured low-friction tracking of customer and consumer behavior. First-party data are also termed as ‘cookies-less’ entities.

The Emergence of Contextual Customer Experience

With the fall of third-party cookies, contextual customer experience has gained prominence. Marketing analytics has become sensitive towards contextual customer experience.

In the practice of contextual customer experience, marketers are able to employ target messaging based on inferred attitudes of their customers and where are they in their customer journey.

Enhanced Reliance of Third-party Sources

Despite the fall of third-party cookies and the rise of first-party data, marketers will continue to invest in third-party sources that lay out a robust view of customers along with the augmentation of the first-party data they collect.

According to a study conducted by IAB and Winterberry Group in 2020, marketers in the U.S. spend over US$1.19 billion on third-party sources. The numbers have witnessed a steep rise in 2021.

Vehement Adoption of AI and ML

With a rise in AI and ML culture worldwide, marketers are also deploying artificial and machine learning for the refurbishment of their business infrastructures to make them suitable for a virtual world.

A Mix of Marketing and Analyst Roles

In a modern world driven by technology, a marketer performs more than just marketing. The paradigm shift demands marketers to possess analytical skills as well. The spotlight is always on the specialists who mix analytical and marketing skills in a balanced manner.

Investments in Inbound Marketing

The dramatic shift from in-person to work from home has made inbound marketing more prominent with its proven effectiveness. Inbound marketing has emerged to be instrumental in increasing brand awareness and trust-building through refocusing strategies that drive traffic to a website.

Marketers in 2021 are investing heavily in inbound marketing tactics to ensure the evolution of their businesses.

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“A SWOT Analysis of Data Science Platform, Professional Survey Report Including Top Most Global Players Analysis with CAGR and Stock Market Up and Down.”

The global “Data Science Platform market” research report is crafted with the concise assessment and extensive understanding of the realistic data of the global Data Science Platform market. Data collected cover various industry trends and demands linked with the manufacturing goods & services. The meticulous data gathered makes the strategic planning procedure simple. It also helps in creating leading tread alternatives. In addition, it also highlights the dominating players in the market joined with their market share. The well-established players in the market are Dataiku, IBM, Datarobot, Wolfram, Feature Labs, Google, Cloudera, Rexer Analytics, Continuum Analytics, Domino Data Lab, Datarpm, Rapidminer, Bridgei2i Analytics, Alteryx,Microsoft.

Click here to access the report

Most of the data is presented in the form of graphical demonstration with accurately intended figures. The performance of the related key participants, suppliers, and vendors is furthermore explained in the global Data Science Platform report. It also underscores the restraints and drivers keenly from the prudent perceptive of our specialists. Additionally, the global Data Science Platform market report covers the major product categories and segments On-Premises, On-Demand along with their sub-segments Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, Operations in detail.

The perfect demonstration of the most recent improvements and latest technologies offers the user with a free hand to grow ultramodern products and procedures to update the service offering. This ultimately helps to work with perfect business options and apply smart implementations. The global Data Science Platform report highlights the latest trends, growth, new opportunities, and dormant tricks to provide an inclusive view of the global Data Science Platform market. Demand proportion and development of innovative technologies are some of the key points explained in the global Data Science Platform market research report.

The research report also highlights the in-depth analysis of various decisive parameters such as profit & loss statistics, product value, production capability, and many more. The report showcases back-to-back parameters such as application, improvement, product growth, and varied structures & processes. It also highlights a variety of modifications done to improve the process functioning of the global Data Science Platform market.

A well-crafted Data Science Platform market research report is based on the primary and secondary source. It is presented in a more communicative and expressed format that allows the customer to set up a complete plan for the development and growth of their businesses for the anticipated period.

The additional geographical segments are also mentioned in the empirical report.

North America: U.S., Canada, Rest of North America
Europe: UK, Germany, France, Italy, Spain, Rest of Europe
Asia Pacific: China, Japan, India, Southeast Asia, North Korea, South Korea, Rest of Asia Pacific
Latin America: Brazil, Argentina, Rest of Latin America
Middle East and Africa: GCC Countries, South Africa, Rest of Middle East & Africa

Impact Of COVID-19

The most recent report includes extensive coverage of the significant impact of the COVID-19 pandemic on the Heated Jacket division. The coronavirus epidemic is having an enormous impact on the global economic landscape and thus on this special line of business. Therefore, the report offers the reader a clear concept of the current scenario of this line of business and estimates the aftermath of COVID-19.

There are 15 Chapters to display the Global Data Science Platform market

Chapter 1, Definition, Specifications and Classification of Data Science Platform, Applications of Data Science Platform, Market Segment by Regions;
Chapter 2, Manufacturing Cost Structure, Raw Material and Suppliers, Manufacturing Process, Industry Chain Structure;
Chapter 3, Technical Data and Manufacturing Plants Analysis of Data Science Platform, Capacity and Commercial Production Date, Manufacturing Plants Distribution, R&D Status and Technology Source, Raw Materials Sources Analysis;
Chapter 4, Overall Market Analysis, Capacity Analysis (Company Segment), Sales Analysis (Company Segment), Sales Price Analysis (Company Segment);
Chapter 5 and 6, Regional Market Analysis that includes United States, China, Europe, Japan, Korea & Taiwan, Data Science Platform Segment Market Analysis (by Type);
Chapter 7 and 8, The Data Science Platform Segment Market Analysis (by Application) Major Manufacturers Analysis of Data Science Platform ;
Chapter 9, Market Trend Analysis, Regional Market Trend, Market Trend by Product Type On-Premises, On-Demand, Market Trend by Application Marketing, Sales, Logistics, Risk, Customer Support, Human Resources, Operations;
Chapter 10, Regional Marketing Type Analysis, International Trade Type Analysis, Supply Chain Analysis;
Chapter 11, The Consumers Analysis of Global Data Science Platform ;
Chapter 12, Data Science Platform Research Findings and Conclusion, Appendix, methodology and data source;
Chapter 13, 14 and 15, Data Science Platform sales channel, distributors, traders, dealers, Research Findings and Conclusion, appendix and data source.

Reasons for Buying Data Science Platform market

• This report provides pin-point analysis for changing competitive dynamics
• It provides a forward looking perspective on different factors driving or restraining market growth
• It provides a six-year forecast assessed on the basis of how the market is predicted to grow
• It helps in understanding the key product segments and their future
• It provides pin point analysis of changing competition dynamics and keeps you ahead of competitors
• It helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Asia.

About Syndicate Market Research:

At Syndicate Market Research, we provide reports about a range of industries such as healthcare & pharma, automotive, IT, insurance, security, packaging, electronics & semiconductors, medical devices, food & beverage, software & services, manufacturing & construction, defense aerospace, agriculture, consumer goods & retailing, and so on. Every aspect of the market is covered in the report along with its regional data. Syndicate Market Research committed to the requirements of our clients, offering tailored solutions best suitable for strategy development and execution to get substantial results. Above this, we will be available for our clients 24×7.

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Self-driving cars, lifelike robots, and autonomous delivery drones are the sexy, headline-grabbing face of the digital transformation that we see all around us today.

None of these would be possible, though, without data – the oil of the fourth industrial revolution – and the analytic technology we’ve built to allow us to interpret and understand it.

Big Data is a term that’s come to be used to describe the technology and practice of working with data that’s not only large in volume but also fast and comes in many different forms. For every Elon Musk with a self-driving car to sell, or Jeff Bezos with a cashier-less convenience store, there is a sophisticated Big Data operation and an army of clever data scientists who’ve turned a vision into reality.

The term Big Data itself may not be as ubiquitous as it was a few years ago, and that’s purely because many of the concepts it embodies have been thoroughly embedded into the world around us. But just because we’ve heard about it for a while, though, doesn’t mean it’s old news. The fact is that even today, most organizations struggle to get value from a lot of the data they have access to. As a business practice, it’s still very much in its infancy.

So here’s my look at some of the key trends that will influence how data and analytics are used for work, play, and everything in between, this year and in the near future.

AI drives deeper insights and increasingly sophisticated automation

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Artificial intelligence (AI) has been a gamechanger for analytics. With the huge amount of structured and unstructured data generated by companies and their customers, even automated manual forms of analytics can only scratch at the surface of what’s to be found.

The simplest way to think of AI, as it is used today, is machines – computers and software – that are capable of learning for themselves. For a simple example, let’s look at a problem we might use a computer to solve today. Which one of our customers is the most valuable to us?

If we only have traditional, non-learning computing available to us, we might be able to take a stab by creating a database showing us which customers spend the most money. But what if a new customer appears who spends $100 in their first transaction with us? Are they more valuable that a customer who has spent $10 a month for the past year? To understand that we need a lot more data, such as the average customer’s lifetime value, and perhaps personal data about the customer themselves such as their age, spending habits or income level would also be useful!

Interpreting, understanding, and drawing insights from all of those datasets is a far more complicated task. AI is useful here because it can attempt to interpret all of the data together and come up with predictions about what the potential lifetime value of a customer may be based on everything we know – whether or not we understand the connections ourselves. An important element of this is that it doesn’t necessarily come up with “right” or “wrong” answers – it provides a range of probabilities and then refines its results depending on how accurate those predictions turn out to be.

Rich new ways to explore and interpret data

Data visualization is the “final mile” of the analytics process before we take action based on our findings. Traditionally, communication between machines and humans is carried out by visualization, taking the form of graphs, charts, and dashboards that highlight key findings and help us to determine what the data is suggesting needs to be done.

The problem here has been that not all people are great at spotting a potentially valuable insight hidden in a pile of statistics. As it becomes increasingly important that everyone within an organization is empowered to act on data-driven insight, new ways of communicating these findings are constantly evolving.

One area where important breakthroughs have been made is the use of human language. Analytics tools that allow us to ask questions of data and to receive answers in clear, human language will greatly increase access to data and improve overall data capabilities in the organization. This field of technology is known as natural language processing (NLP).

Another is new technologies that allow us to get a better visual overview and understanding of data by fully immersing ourselves within it. Extended reality (XR) – a term that includes virtual reality (VR) and augmented reality (AR) will clearly be seen to drive innovation here. VR can be used to create new kinds of visualizations that allow us to impart richer meaning from data, while AR can show us directly how the results of data analytics impact the world in real-time. For example, a mechanic trying to diagnose a problem with a car may be able to look at the engine wearing AR glasses and be given predictions on what components are likely to be problematic and may need replacing. In the near future, we should expect to see new ways of visualizing or communicating data, widening accessibility to analytics and insights.

Hybrid cloud and the edge

Cloud computing is another technology trend that has had a massive impact on the way Big Data analytics are carried out. The ability to access vast data stores and act on real-time information without needing expensive on-premises infrastructure has fuelled the boom in apps and startups offering data-driven services on-demand. But relying entirely on public cloud providers is not the best model for every business, and when you trust your entire data operations to third parties, there are inevitably concerns around security and governance.

Many companies now find themselves looking towards hybrid cloud systems, where some information is held on Amazon Web Service, Microsoft Azure, or Google Cloud servers, while other, perhaps more personal or sensitive data, remains within the proprietary walled garden. Cloud providers are increasingly on-board with this trend, offering “cloud-on-premises” solutions that potentially provide all of the rich features and robustness of public cloud but allowing data owners full custody of their data.

Edge computing is another strong trend that will affect the impact that Big Data and analytics have on our lives over the next year. Essentially this means devices that are built to process data where it is collected, rather than sending it to the cloud for storage and analysis. Some data simply needs to be acted on too quickly to risk sending it backwards and forwards – a good example here is the data gathered from sensors on autonomous vehicles. In other situations, consumers can be reassured that they have an additional level of privacy when insights can be gleaned directly from their devices without them having to send data to any third party. For example, the Now Playing feature on Google’s new Android phones continuously scans the environment for music so it can tell us the names of songs playing in the supermarket or movies we’re watching. This wouldn’t be possible with a purely cloud-based solution as users would reject the idea of sending a constant 24/7 stream of their audio environment to Google.

The rise of DataOps

DataOps is a methodology and practice that borrows from the DevOps framework often deployed in software development. While those in DevOps roles manage ongoing technology processes around service delivery, DataOps is concerned with the end-to-end flow of data through an organization. In particular, this means removing obstacles that limit the usefulness or accessibility of data and deployment of third-party “as-a-service” data tools.

There’s no formal training needed to work in DataOps. The evolution of the role makes it a great opportunity for anyone with experience or interest in an IT career that wants to work on the most exciting and innovative projects, which are often data projects. We will also see the growth in popularity of “DataOps-as-a-service” vendors, offering end-to-end management of data processes and pipelines on-tap and pay-as-you-go. This will continue to lower the barriers of entry to small and startup organizations with great ideas for new data-driven services but without access to the infrastructure needed to make them a reality.

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From AI’s increased use in healthcare, finance and HR to the challenge of talent acquisition and the growing importance of structured and unstructured data, 16 AI startup founders reveal their artificial intelligence trends for 2021

In the final article of a three part series* focusing on what AI startup founders are doing to navigate the fast growth AI industry, Information Age spoke to 16 founders of some the UK’s leading AI startups and scaleups to understand their artificial intelligence trends for 2021, including its growing use in a variety of industries, the importance of data and talent, the impact of Covid-19 and the democratisation of AI.

*The first in the series, focusing on the main pain points of running an AI business, can be read here and the second, focusing on the challenges of running a fast growth business, can be read here.

The growing use of AI

“Fundamentally, AI is becoming more accepted and utilised across all areas of life and people are experiencing the benefits,” says Mark Nicholson, CEO, Vivacity Labs.

This growing use and acceptance of AI has been driven, in part, by the disruption caused by the Covid-19 pandemic.

“More and more businesses that are not fully digitalised want to undergo a digital transformation. [AI and] automation is the most popular technology these new clients want to adopt,” confirms Daniel Cooper, managing director at Lolly Co.

AI in healthcare

The healthcare sector, including drug discovery in the pharmaceutical industry, has experienced a tremendous challenge over the last year.

But, AI is increasingly being used in the medical sector to help tackle the virus “by analysing and interpreting data on the virus’s spread,” according to Dr Alex Young, founder and CEO at Virti.

“It is also being used in healthcare to help with treatment and medical training. With AI and machine learning being the ultimate problem-solving tools (tools which are only getting smarter), we can expect to see the use of AI in medicine continue to grow over the coming months and years,” he says.

Chris Ganje, CEO and founder at AMPLYFI, agrees and says “UK science has played a pivotal role in tackling the pandemic globally. AMPLYFI has continually tracked these exciting projects that cover everything from data modelling, to DNA sequencing to rapidly deployed research trials.”

Looking at the pharmaceuticals industry, Miriam Cha, co-founder and COO of Rahko explains that: “we are seeing much broader adoption of AI in discovery pipelines. What have traditionally been extremely slow, expensive processes are being significantly accelerated with drastically reduced costs…we are seeing a huge appetite and real excitement for AI in the more innovative companies, and quantum computing in the most innovative of these.”

AI and innovations in technology are also changing the way people provide and receive care.

“Assistive technologies such as virtual assistants and wireless devices have been enabling seniors to live more securely at home for longer, by keeping them connected with family and friends whilst helping them with daily living activities,” adds Philip Marshman, founder and CEO at Sentai.

Following the pandemic, “with the number of people 65 years old and over expected to increase significantly over the next decade, technology will likely continue to evolve to help them with a variety of physical, mental, and emotional challenges. Incorporating artificial intelligence, robotics and other technology trends that help them live independently, seniors are likely to continue embracing such technologies as long as they are easy enough to use,” he says.

AI in finance

Matthew Hodgson, CEO and founder of Mosaic Smart Data, says AI and automation is “permeating virtually every corner of capital markets.”

He believes that this technology will form the keystone of the future of business intelligence for banks and other financial institutions. The capabilities and potential of AI are enormous for our industry.

According to Hodgson, recent studies have found that companies not using AI are likely to suffer in terms of revenue.

“As the link between AI use and revenue growth continues to strengthen, there can be no doubt that AI will be a driving force for the capital markets in 2021 and in the decade ahead — those firms who are unwilling to embrace it are unlikely to survive,” he continues.

Hodgson predicts that with the continued tightening regulatory environment, financial institutions will have to do more with less and many will need to act fast to remain both competitive and relevant in this ‘new normal’.

“As a result, we are seeing that financial institutions are increasingly looking to purchase out-of-the-box third-party solutions that can be onboarded within a few short months and that deliver immediate results rather than taking years to build their own systems with the associated risks and vast hidden costs,” he adds.

AI in HR

Like in healthcare and financial services, Dr Alan Bourne, CEO and founder at Sova, believes AI can meet a lot of the new demands on HR, such as surging applicants and staff pressures, which have been exacerbated by Covid.

He says: “As the tech has moved beyond the experimental phase and much more into the applied world, businesses can have greater confidence in what they’re buying. It’s no longer a case of what sells, but what works. This means fewer point solutions and more meaningful, integrated data to support business talent management for the long-term.

“There is also a growing excitement around how AI can dramatically reduce bias in recruitment, which supports wider ESD (Ethical, Social and Diversity) goals that have become an increasingly important business priority.”

Covid-19 and remote working

Prior to Covid, Ofri Ben-Porat, CEO at Edgify, says that AI was looked at by industries as something very futuristic.

“The reality today, is a trend towards simpler AI processes first; just to run some tasks that will allow companies to avoid the need to have to bring people physically together during a pandemic,” he says.

Ky Nichol, CEO at Cutover, agrees at says that remote team orchestration for critical work is now essential. Businesses need to adopt AI “processes and systems that work independent of location and that are visible.”

The Covid-19 pandemic highlighted the importance of all things digital. However, looking forward, “a high performing digital sector is not just about handling the pandemic. It is also fundamental to the future prosperity of the UK in a rapidly changing world where digital not only underpins value generation but is also a product in itself,” according to Ganje.

Adaptability

Tim Weil, CEO, Navenio, says a key trend that he has seen this year that will continue to be prevalent in 2021 is adaptability.

He explains: “Over the past year, technology companies have been focused on providing customers with a service that allows them to adapt to future challenges.

“As well as helping customers to adapt, businesses themselves will need to be flexible and agile in the way that technology is rolled out over the coming months — especially given that companies are still working remotely.

“The concept of digital transformation is still a huge feat for many companies. Our work with the NHS, for example, is helping increase efficiency during this challenging time, but can only be done without causing any unnecessary upheaval.

“In essence, companies like Navenio, have shown how to adapt to different industries and quickly provide services and support where needed. We expect many more businesses to follow this approach in 2021.”

Hiring talent

Access to talent and staff retention have been identified as one of the greatest challenges for founders of AI and fast growth businesses.

To cope with this, Safe Hammad, CTO and co-founder at Arctic Shores, says the trend of psychometric testing to support hiring decisions is increasing.

“Talent leaders everywhere are firstly identifying that they need help making fairer, more accurate hiring decisions. And, then, they’re seeing that an objective assessment can really provide that help.

“Another theme is the ever-increasing focus on the candidate experience. The hiring process is one of the first impressions you’ll get of a company, so it really matters. A good experience reflects well on the employer and will help keep candidates engaged.”

It’s all about the data

“An organisation’s ability to acquire and analyse high quality reliable data, as well as recognise the growing importance of unstructured data over structured data, is as important to the development of new digital products as it is to the tackling of a coronavirus pandemic,” says Ganje.

Secure, unbiased data is what makes AI work.

But there’s a problem. Darko Matovski, CEO and co-founder of causaLens, explains that the reality is “current AI systems work very well when they are fed ‘new’ data similar to what they’ve been trained with, but they fail when these datasets are different in any statistically significant way.”

The solution to this, he suggests, is getting AI systems to understand cause and effect.

“The industry is racing to develop these truly intelligent systems — a major step towards true AI. Customers increasingly want more than just automating repetitive tasks or even getting accurate predictions. They want to be able to answer more sophisticated questions such as why? what if? and how? Machines that understand cause and effect can evaluate interventions and ‘imagine’ alternative scenarios to be able to answer these questions,” he says.

Democratising AI

Finally, Dr Richard Ahlfeld, founder and CEO at Monolith AI, explains that the democratisation of AI is a growing trend in the AI space.

“In the past, solving problems in this way was dedicated to the experts. But, over the last few years, more companies are trying to democratise the complex parts of virtual engineering software, essentially making it more user friendly.”

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Artificial Intelligence (AI) has the peculiar ability to simultaneously amaze, enthrall and intimidate. The possibilities of AI are innumerable and beyond the scope of our imagination. AI would be the bedrock of the era of connected mobility, automation and Industry 4.0, powering everything from analytics, decision-making, agriculture, logistics, to construction, aerospace and robotics. The global AI market is expected to reach $ 118.6 billion by 2025, and the intense rivalry between the US and China in the arena of AI is fast gaining momentum.

In 2017 China’s artificial intelligence startups took 48% of all dollars going to AI startups globally, which is more than that of the USA. In deep learning also China publishes six times more patents than the US.  In the coming years, AI won’t be limited to powerful supercomputers and big devices but would be ubiquitous. Let’s have a look at some of the major AI trends for 2020.

Evolution of robotization

A legion of programmed robots replacing workers at the assembly line has become the most recurring and powerful image when we think of AI. Robotization is already underway, with a lot of companies trying their hands at robots for different purposes. What’s however new is that robots that were hitherto only employed for manual and tedious tasks would now begin to take on semi-skilled and skilled work as well: filling forms, creating reports, making animations, giving instructions etc. In short, from partial automation, we are looking at complete automation by training machines to do the requisite task.  In Japan, by 2025, more than 80% of elderly care would be done by robots, not caregivers.

This will not only increase efficiency but also give us ample time and energy to focus only on core tasks that require human intelligence.

Data access enabling ubiquity

Data may or may not be the new oil, but it is certainly fueling and fortifying AI and making it more versatile. What often hinders the transition to AI-powered automated decision making is the lack of accurate and reliable information. This is among the major challenges which are gradually being overcome with the ongoing digitalization and options like a simulacrum of the real world. This has streamlined processes, slashed costs, improved research capabilities, and also allowed for gaining accurate data and information in the test phase.

For instance, developers of autonomous vehicle software can get access to uncountable hours of driving data without even the vehicle hitting the street. There would be a drastic increase in the use of AI for accurate real-world simulations. The increase in AI sophistication and capacity will lead to cost-effectiveness and widespread availability, with AI being incorporated in most of the gizmos that we use on a daily basis, making way for interconnected devices that can process information and learn on their own.

Enhanced customization

For staying ahead of their competitors and preparing themselves for the hyper-transforming realm of business, companies fall over themselves to gain insights in real-time, know about customer preferences and deliver services to them accordingly. Real-time data and all-pervasive location intelligence has mainstreamed customized services in almost every field, from urban mobility to online marketplaces. With soaring customer expectations, companies need to provide more personalized and relevant services to retain customer base and remain relevant. This is where AI will come in the picture. The increasing use of AI-based applications in every field, coupled with advances in location intelligence, will augment the capacity for offering more refined personalized services.

Boosting Cybersecurity

Mounting concerns about data privacy and security breach have also lead to questions being raised about the security preparedness in the age of AI and IoT. It is estimated that by 2020 over 24 billion devices connected to the internet would be installed. The number of connected devices used in households is predicted to increase from 9 devices per household currently to 500 by 2022, as per the research house Gartner.

Increased Cloud capacity and Machine Learning trained algorithms would also mean that cyberattacks like hacking and phising would be more high-tech and difficult to detect. Conventional cybersecurity will come to naught in this case and we will need expert AI-bolstered cybersecurity mechanisms to safeguard ourselves from such malicious attacks.

Without using AI, conventional systems will no more be able to continuously detect and monitor new malware that is created at an unprecedented pace. Big cybersecurity firms have started using AI for pattern recognition to detect viruses and malware. These new systems can spot even the smallest aberration or distortion even before the malware intrudes into the system. Another advantage of AI is that it has the power of predictive functioning.

AI complementing humans

Bill Gates, Microsoft founder and philanthropist, said last year that AI can be our friend and be beneficial for the society. AI won’t compete or supersede humans. Nor would there be a ‘rise of machines’ like ominous scenario. While a lot of jobs would be lost due to AI, many others would be created and human but capacities enhanced with the help of real-time data analytics. Reskilling would be common in the age of AI and as per a survey by IDC by 2025 over 75% of organizations will invest in reskilling programs to bridge the rising skill gap. According to a 2018 report, the approximate number of qualified researchers currently in the field of AI is 300,000, while companies require a million or more AI specialists.

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