The global pandemic has transformed the way we transact. With much of the world moving online, e-commerce, cloud computing, and enhanced cyber security measures are just the tip of the iceberg when it comes to assessing current trends in data analysis.
Managing risk and keeping costs low have always been important considerations for businesses. However, having access to the right machine learning technology that can analyse data effectively is becoming crucial for any company wanting a competitive edge.
Why should trends matter to model creators?
Our roundup of the top data analysis trends for 2022 and beyond should give our creators a good idea of where the industry is heading.
By keeping on top of trends in data science and adjusting their models to fit current standards, creators can make their work truly invaluable. Whether these data analysis trends inspire you to brainstorm new models or update the existing ones in your toolkit, the choice is entirely yours.
The creator economy comes to AI: think Airbnb for AI artifacts
Following the trend in computer gaming, with user-generated content (UGC) monetized as an integral part of gaming platforms, we see similar monetization happening in data science. This starts with simple models, such as classification, regression, and clustering models, all repurposed and uploaded to dedicated platforms. These are then made available to a global marketplace of business users who want to automate everyday business data and processes.
This will be quickly followed by deep model artifacts, such as convnets, GAN’s, and autoencoders that are tuned and applied to solve business problems. These are designed to be safe in the hands of commercial analysts rather than teams of data scientists.
Data scientists selling their skills and experience as consultancy gigs or uploading models to code repositories is nothing new. However, 2022 will see monetization of these skills through double-sided marketplaces, giving a single model access to a global marketplace. Think Airbnb for AI.
Environmental AI becomes front of mind
Whilst most research is understandably focused on pushing the boundaries of complexity, the reality is that training and running complex models can have a big impact on the environment.
It’s predicted that data centres will represent 15% of global CO2 emissions by 2040, and a 2019 research paper, “Energy considerations for Deep Learning,” found that training a natural language translation model emitted CO2 levels equivalent to four family cars over their lifetime. Clearly, the more training, the more CO2 is released.
With a greater understanding of environmental impact, organisations are exploring ways to reduce their carbon footprint. Whilst we can now use AI to make data centres more efficient, the world should expect to see more interest in simple models that perform as well as complex ones for solving specific problems.
Realistically, why should we use a 10-layer convolutional neural network when a simple bayesian model performs equally well while using significantly less data, training, and compute power? “Model efficiency” will become a byword for environmental AI, as creators focus on building simple, efficient, and usable models that don't cost the earth.
Hyper-parameterized models become the superyachts of big tech
Not unlike the space tech race of Musk and Bezos, big tech have their own exciting race: who has the biggest deep learning model?
In 3 years, the number of parameters in the largest models rose from 94 million parameters in 2018 to a staggering 1.6 trillion in 2021, as Google, Facebook, Microsoft, OpenAI, etc., push the boundaries of complexity.
Today, these trillions of parameters are language-based, allowing data scientists to build models that comprehend language in detail, enabling models to write human-level articles, reports, and translations. They can even write code, develop recipes and understand sarcasm and irony in context.
In 2021 and beyond, we can expect similar human-level performance from vision models which are capable of recognising images without the need for huge data sets. For example, you can show a toddler a bar of chocolate once, and they’ll know chocolate every time they see it without needing to be retrained!
Creators are already applying these models to specific opportunities. One of the most prolific examples of this comes from games developer Dungeon.AI, who developed a series of fantasy games based on the 1970’s craze Dungeons and Dragons. These realistic worlds are based on the 175 billion parameter model GPT-3. We expect to see more of this activity from creators, as models are applied to specific areas like understanding legal text, copywriting ad campaigns, or categorizing images or video into specific groups.
An Augmented Workforce of digital co-workers
As cognitive technologies and machine learning models become more readily adopted by businesses around the world, the days of mindless admin and assigning menial tasks to the human workforce are fading into obscurity.
Instead, businesses are opting for an augmented workforce model that sees humans and robots working side by side. This technological advancement makes work scalable and easier to prioritize, giving humans the ability to focus on the consumer first.
Although creating an augmented workforce is certainly one of the data analysis trends for creators to keep tabs on, deploying the correct AI and countering any teething problems that come with automation is a major challenge. What’s more, employees are understandably reluctant to jump on the bandwagon with enthusiasm when faced with statistics that claim one in three jobs will be replaced by robots by 2025.
These concerns are valid to some extent, but there’s also a well-founded belief that machine learning and automation will only enhance the lives of employees, allowing them to make crucial decisions more quickly and without uncertainty. Despite its potential drawbacks, an augmented workforce lets individuals spend more time on quality assurance and customer care while simultaneously solving complex business problems as they arise. 
With so many companies keen to move over to Robotic Process Automation (RPA), machine learning, and cognitive augmentation as part of their future modeling, this is one of the trends in AI that all aspiring data analysts should be aware of.
As most businesses have been forced to invest in an increased online presence during the pandemic, improved cybersecurity has become one of the top data analysis trends for 2021.
A single cyber-attack can completely derail a business, but how can companies track potential points of failure without massive cost and time investment? The answer to this burning question lies in excellent modeling and a commitment to understanding risk. AI’s ability to analyze data quickly and accurately means that greater risk modeling and threat perception is possible.
Unlike humans, machine learning models can work through data at a rapid pace, offering insights that keep threats under control without much external input. According to IBM’s analysis of AI for cybersecurity, this technology gathers insights on everything from malicious files to unfavorable IP addresses, enabling businesses to respond to threats up to 60 times faster.  As the average cost saving from containing a breach is $1.12 billion, investing in excellent cybersecurity modeling is not something that businesses should overlook. 
In short, by keeping their networks guarded with this data analysis trend, businesses can better protect their bottom line.
Low-code and No-code AI
With so few data scientists available to fill roles on a global scale, enabling non-experts to create workable applications from pre-defined components makes low-code and no-code AI one of the most democratic trends to appear in the industry for years.
Essentially, this approach to AI requires little to no programming, allowing anyone to “tailor applications to their needs with the use of simple building blocks.” 
Recent trends have shown that the job market is looking extremely positive for data scientists and engineers, with LinkedIn’s emerging job report claiming that approximately 150 million global tech jobs will be created in the next 5 years. Considering over 83% of businesses now see AI as a crucial factor when it comes to staying relevant, this isn’t exactly news.
However, the intense demand for AI-related services simply can’t be met in the current climate. What’s more, over 60% of AI’s finest talent is being nabbed by the technology and finance sectors, leaving few potential employees available to work in other industries. 
Therefore, creating low-code and no-code AI solutions that give businesses the power to compete without data specialists is the key to keeping industries open and competitive.
The Rise of the Cloud
The pandemic has made the move to cloud computing one of the most inevitable data analysis trends to emerge in recent years. With more data to contend with than ever before, sharing and managing digital services through the cloud has been rapidly adopted by businesses around the world.
Machine learning platforms take data bandwidth requirements to the next level, but the rise of the cloud makes it possible to complete work faster and with company-wide visibility. With 94% of enterprises already using cloud services and the public cloud infrastructure set to grow by 35% at the end of 2021, companies not taking advantage of the cloud will simply fall behind. 
Able to keep data secure, protect businesses from cyber-attacks, and improve scalability, the rise of the cloud has more benefits than drawbacks, making it one of the key data analysis trends for creators to watch out for in the coming years.
Small Data and Scalable AI
As more of the world moves online, the ability to create scalable AI in response to broader datasets is more important than ever before. Although the use of big data that arrives quickly is still fundamental for creating effective AI models, it’s small data that adds value to customer analysis. This isn’t to say that big data doesn’t have value, but it’s almost impossible to pick out meaningful trends from such large datasets.
As you might expect from its name, small data consists of a small number of data types that contain enough information to measure patterns but not so much as to overwhelm companies. Allowing insights to be drawn from specific cases, marketers can model consumer behavior more effectively and translate their findings into increased sales through personalization. 
Improved Data Provenance
Defined by Boris Glavic as “information about the origin and creation process of data,”  data provenance is one of the data science trends that keeps data produced by the industry reliable.
To remain profitable, businesses need to be able to trust the data that they use for marketing and advertising purposes. While ample data is good to have, it can only be useful if analyzed properly. Inaccurate forecasting and poor data management can severely impact businesses, but improvements to machine learning models have made this less of a problem over time.
Now able to use targeted algorithms, these models can determine which data sets should be used and which should be discarded. For data analysts, tracking intelligent features and keeping all files up to date should make relevant data easier to pick out.
Migration to Python and tools
Providing a more user-friendly approach to coding with its simple language and syntax, Python is a high-level programming language that is rocking the tech industry.
Although R is unlikely to disappear from the world of data science anytime soon, Python is considered more readily accessible by global businesses as it prioritizes logical code and readability. Unlike R, which is used primarily for statistical computing and graphics, Python can be readily deployed for machine learning as it collects and analyzes data on a deeper level than its predecessors.
Able to give data analysts a leg-up in the industry, the use of Python in scalable production environments is one of the trends in data science that shouldn’t be ignored by budding creators.
Deep Learning and Automation
Deep learning is related to machine learning, but its algorithms are inspired by the neural pathways in the human mind. For businesses, the use of this technology ensures accurate predictions and useful models that can be easily understood. 
Although deep learning isn’t appropriate for every industry, the neural networks used in this subfield of machine learning improve automation and allow businesses to be highly analytical without much human intervention.
Found in everything from digital assistants to Shell’s modernized smart sensors in the Gulf of Mexico, the use of deep learning and automation is one of the trends in AI that turns high-quality data into guaranteed top-line growth.
Being able to assess data in real-time is one of the most exciting data analysis trends to appear in recent years. Sentiment analysis and real-time automated testing have become more popular with businesses during 2021, and companies have taken advantage of data advancements to assess consumer behavior as it happens. Allowing tweaks and changes to be made as soon as issues arise, real-time analytics make businesses more proactive.
According to research and advisory company Gartner, more than 50% of new business systems will use real-time data to improve decision-making by 2022.  Not only will this improve the customer experience and enhance profit margins for businesses, but real-time data happens to be one of the data analysis trends that removes the costs associated with historical, on-premises data reporting.
Departing from DataOps and Approaching XOps
With so much data available to businesses in our modern world, engaging in manual processing is simply out of the question.
Although DataOps is efficient when it comes to gathering and assessing data, a decisive move towards the more sophisticated XOps is proving to be one of the top data analytics trends for the coming year. To further support this point, Gartner has confirmed the importance of XOps, stating that it is an effective way to combine data processes for a more cutting-edge approach to data science.
You may already be familiar with DataOps, but if you’re scratching your head at the thought of a new term, then let us fill you in.
According to the data management experts at Salt Project, XOps is a “catch-all, umbrella term to describe the generalized operations of all IT disciplines and responsibilities.”  It encompasses DataOps, MLOps, ModelOps, AIOps, and PlatformOps for a multi-pronged approach that boosts efficiency, enables automation, and shortens development cycles for several industries.
By drawing these programs together, businesses can take advantage of the latest IT software to make their data investigation seamless, saving time, energy, and money.
Data analysis trends for the future: what are the key takeaways?
The data science trends for 2021 are incredibly progressive and prove that accurate and digestible data is more valuable to businesses than ever before.
However, data analysis trends are never going to be stationary, as the amount of data available to businesses is constantly growing. This makes finding effective data processing methods that work for all a constant challenge.
With accessibility, democratization, and automation becoming key priorities for the data industry moving forward, creators should aim to keep their models easy to understand and, where possible, future-proof.
 Joshua Barajas, “Smart robots will take over a third of jobs by 2025, Gartner says”, PBS, last modified Oct 7, 2014, https://www.pbs.org/newshour/economy/smart-robots-will-take-third-jobs-2025-gartner-says
 Bill Cline, Maureen Brady, David Montes, Chris Foster, Catia Davim, KPMG, The Augmented Workforce, https://home.kpmg/xx/en/home/insights/2018/06/augmented-workforce-fs.html
 IBM, “Artificial Intelligence for a smarter kind of cybersecurity”, last modified Oct 4, 2021, https://www.ibm.com/uk-en/security/artificial-intelligence
 IBM Corporation, “Beyond the Hype: AI in your SOC”, July 2020, https://www.ibm.com/downloads/cas/9EDONM6M
 Anton Vaisbud, “Low Code AI in Enterprise”, last modified February 26, 2021, https://towardsdatascience.com/low-code-ai-in-enterprise-benefits-and-use-cases-b9692ee13168
 David Kelnar, “The State of AI: Divergence 2019”, MMC Ventures, last modified 5 Mar 2019, https://www.stateofai2019.com/introduction
 Nick Galov, "Cloud Adoption Statistics for 2021", last modified August 1, 2021, https://hostingtribunal.com/blog/cloud-adoption-statistics/
 Shane Hill, “Forget ‘big data’: It’s the small data that delivers value”, last modified 13 October 2020, https://techmonitor.ai/ai/small-data-not-big-data
 Boris Glavic, “Big Data Provenance: Challenges and Implications for Benchmarking”, Specifying Big Data Benchmarks, 2014, Volume 8163, Abstract
 IBM Cloud Education, “Deep Learning”, last modified 1 May 2020, https://www.ibm.com/cloud/learn/deep-learning
 Susan Moore, “Gartner Top 10 Data and Analytics Trends for 2019”, last modified November 05 2019, https://www.gartner.com/smarterwithgartner/gartner-top-10-data-analytics-trends
 Rhett Glauser, “What is XOps?”, Last modified 06 May 2020, https://saltproject.io/what-is-xops/
Original. Reposted with permission.
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