Humans are Data Generating Machines
Each day, humans create about 2.5 quintillion bytes of data (FYI: there are 18 zeros in one quintillion). By 2025, we will generate about 185 times more data. Perhaps more astonishing is that the world’s data grew 900% from 2017 to 2019, and 80 to 90% of the global data is unstructured, meaning we have not extracted value from it.
Devices are Generating Massive Data Sets too
In addition, with the “Internet of Things” (“IoT”), Internet connected devices are growing in scope and reach, from about 2 billion devices in 2006 to a predicted 200 billion devices by 2020. These devices include, Voice Controlled Navigation Systems, Smart-Televisions, Wearable Technology (i.e. ‘fitness trackers’), Smart Home Control and Security Systems, Internet-connected appliances. Most of these devices generate and collect data as they operate.
The Rise of Cloud Computing Boosts the “Data Ecosystem”
The growing adoption of cloud-based computing (i.e. computing performed on centralized ‘cloud’ servers) has led to a proliferation of connected devices, applications, and social media, resulting in an explosion of digital data. According to industry consultant IDC, there will be 175 zettabytes (each zettabyte has 1021 bytes) of data by 2025, representing a Compound Annual Growth Rate (CAGR) of 27% from 33 zettabytes of data in 2018. According to IDC, 49% of data will be stored in public cloud environments by 2025, an increase from approximately 30% today.
Accessibility to data is improving as well. In the past, data and analytics technologies were only accessible for few, highly trained individuals. This access is now more open, and, as a result, in an increasingly digital economy, many more individuals are becoming data consumers. For example, a 2020 study by IDC found 60% of enterprises cited more widespread adoption of analytics solutions by more employees and faster time to insights as first order benefits of data and analytics.
This widespread data, likely contains valuable insights for organizations, including key business and performance metrics, customer attributes and behavior, and product strengths and capabilities.
Unlocking Valuable Insights from Big Data Sets
Collecting data is one thing; extracting insights from big data sets required new technology. As early as 1923, the Los Angeles Police Department leveraged IBM tabulating equipment to analyze arrest records in order to identify criminal methods to help solve crimes. Today, we rely upon artificial intelligence (“AI”) and machine learning (“ML”) to make sense of big data.
According to Stanford University, “Artificial Intelligence” “is the science and engineering of making intelligent machines, especially intelligent computer programs.” IEEE defines “Machine Learning” as “the study of computer algorithms that improve automatically through experience.” For the purposes of this report, AI/ML solutions are about writing computer code smart enough to learn.
Sound data is key to successful ML. GE recognized this and implemented systems to analyze several big data sets for Southwest Airlines, helping them to save US$100 million per year in fuel costs. However, it doesn’t matter how good AI code is written – if this code is given bad data to process, it will generate bad insights.
AI / ML – Market Size
Today, enterprises are deploying AI/ML to unlock the insights from their hidden big data assets. With all the potential AI/ML applications, market forecasts are large:
- Global AI was valued at US$62.4 billion in 2020 and forecasted to grow at 42.2% CAGR to US$733.7 billion by 2027;
- Spending on AI systems will reach US$97.9 billion in 2023, more than 2.5x the US$37.5 billion spent in 2019;
- By 2025, AI software will be about US$100 billion global market;
- In 2020, the global AI software market should grow about 54% year-on-year to US$22.6 billion;
- AI-related semiconductors could account for almost 20% (about US$67 billion) of all semiconductor demand by 2025;
- Global ML was a US$8.43 billion market in 2019 and could grow to US$117.19 billion by the end of 2027 (39.2% CAGR);
- Global ML will grow fromUS $1.408 billion in 2017 to US$14.279 billion by 2024;
- Global business intelligence and analytics software is forecast to increase from US$13.9 billion 2018 to US$16.5 billion in 2023;
- Global big data and business analytics market was valued at US$168.8 billion in 2018 and is forecast to grow at a 13.2% CAGR to US$274.3 billion by 2022.
Build A Horizontal Machine to Address Numerous Vertical Markets
As third-party market forecasts suggest, the AI/ML industry is a large market that will likely get much larger. One reason for this is that big data is not limited to a handful of industries. With enterprise adoption of ERP, CRM, and cloud during the 1990s and 2000s to capture data, companies across all verticals now have access to AI/ML to unlock the value of their data assets. Some examples of industries disrupted by AI/ML include:
- Energy: America has 2.7 million miles of energy pipelines. Proper maintenance and operation of these assets are imperative to ensuring the safety of communities, workers, and the environment. High-tech devices called PIGs collect pipeline data, but analysis is left largely to Excel macros. But AI/ML has disrupted this legacy analysis. Read more HERE.
- Financial Services: Regulations like MiFID II demand trading transparency for investors. Historically, investors have had to rely upon brokers to fill the best trade, and these trades mostly relied upon human decisions. And although we believe most brokers act in good faith, we cannot deny that there is limited transparency for investors. RBC’s Aiden® platform uses the computational power of AI to improve trading results and insights for clients. Read more HERE.
- Healthcare: The COVID-19 pandemic is at the forefront of health, business, and political news. Researchers are challenged not by data availability but by unification. There are no standards to data formatting, and researchers can spend 90% of their time formatting datasets to make them usable. To help researchers focus their time on data analysis rather than data formatting, AI/ML has been used to create a data lake to unify the datasets. Read more HERE.
- Agriculture: Farmers must track and analyse numerous variables to make decisions that will affect their businesses over several years. Improper selection and application of fertilizers, for example, can cause chemical burns to plants, contaminated water, and soil acidification. Using AI to analyze crop images can help determining plant health and when fertilizers should be applied. Read more HERE.
Again, these are a few AI/ML examples in a universe of applications that is expanding. We believe that horizontal platforms applicable across multiple industry verticals will create the most value for AI/ML companies and their shareholders.
Investors Are Expecting Big Benefits from Extracting Big Data Insights
Underscoring the importance of data to the software ecosystem and investors, we point to the stock performance of recent IPO, Snowflake [SNOW: NYSE]. The company’s value proposition of enabling customers to consolidate data from disparate systems and platforms into a single source in order to formulate business insights, build data-driven applications, and share data clearly resonated with customers and investors. For example, in the month of July 2020, Snowflake processed an average of 507 million daily queries across all its customer accounts, up from an average of 254 million daily queries during July 2020, or up nearly 100% y/y.
The company reached the US$500 million revenue milestone in record time: six years. Recognizing the value of data, investors were enthusiastic and the company’s recent US$3.7 billion raise was the largest software IPO ever. Furthermore, according to equity analysts at JP Morgan, “the steepest valuation we can recall in the software industry in the last couple of decades.” Investor enthusiasm has already been factored into the stock’s valuation, which a few weeks ago, was trading at about 57 times next year’s revenue versus Microsoft’s 9 times.
Sophic Capital client AnalytixInsight Inc. (“AnalytixInsight” or the “Company”) [ALY:TSXV; ATIXF:OTC] is a pure play artificial intelligence and machine learning small cap stock, against a backdrop where investors do not have many publicly traded small cap stocks to benefit from this secular theme. The company’s FinTech solutions are used by The Wall Street Journal, Morningstar, Refinitiv, and Intesa Sanpaolo. Most investors we’ve spoken with, are generally well aware of AnalytixInsight’s financial products solutions, CapitalCube and MarketWall. Some investors are also aware of the Company’s workforce optimization offering, Euclides. However, the Company’s core platform, which is capable of over 100 billion daily computations, creating machine-generated insights and content is horizontally adaptable to different industries beyond these initial two verticals. As a result, we believe Analytixinsight’s target market could be much larger than the initial capital markets and workflow optimization markets.
Recall our belief that a horizontal AI/ML platform deployed across multiple verticals will create the most value for shareholders. AnalytixInsight is one such AI/ML company, and we will delve deeper in upcoming Sophic Capital reports.
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