Data Management, AI, and Machine Learning: The Recipe for Efficiency
Data is the new gold, though it would be impossible to capitalize on its value without the innovation and investment happening in artificial intelligence.
Data is the new gold, some say. Others say it is even more valuable than gold. With a supply that has been growing exponentially over the past decade, data and data management have become increasingly important areas of focus for businesses, particularly regarding how they can be leveraged to increase efficiency & value. The global data analytics market is projected to be worth $24.63 billion by 2030, posting a 25% CAGR through the year. Good data is key to optimizing processes, making informed decisions, and even predicting the future. With exponential growth in computing technology since the beginning of the 21st century, the production of data has grown even faster. Methods of data management have been developed to determine how data should best be grouped, collected, organized, and cleaned. Management systems have become increasingly complicated with unprecedented growth in the volume of raw data. With such growth creating a need for more powerful management methods, we are seeing increased lucrativeness in advanced data management technologies guided by Artificial Intelligence and Machine Learning. These technologies’ ability to optimize data management not only help businesses and institutions make decisions, but they’ll soon become baseline necessities in any organization that produces or handles mass data. For this, the AI/ML technology industry, with respect to data, can be expected to become a hotbed of innovation and investment in the coming decade and beyond.
To understand AI’s potential impact on data management, one should consider the current and potential scope of the broader AI industry. We have made many posts about the potential of AI, and even how it directly applies to managing vast amounts of unstructured data. There seems to be one key motif: Artificial Intelligence solves problems that humans and current computing methods cannot. The AI market was valued at $65 billion in 2020, and is expected to grow to nearly $300 billion by 2026. Possibly technology’s final frontier, AI offers direct innovation paths for industries like automotive, healthcare, retail, finance, and manufacturing, with the potential to create tremendous efficiency increases in their work processes and even add new customer value propositions to their repertoires. Forbes lists AI driven vendor management as the new customer management, and expects AI to become an integral piece of large companies’ supply chains. AI’s uses are extensive, and provide a resource for managing complex problems in an efficient and effective manner.
The aforementioned exponential growth in the volume of raw data produced by companies and institutions would be an overwhelming prospect if not for the capabilities of AI-driven management. With human power’s inability to manage large quantities of rapidly updating data, AI provides an ulterior method for current and insufficient data management techniques. 88% of the most data-driven companies in the world agree that AI and ML are an important part of any data platform & analytics initiative. An AI-enabled database automates organizational and querying processes, giving organizations the opportunity to drastically improve operational efficiency. McKinsey predicts that AI-enabled databases will be able to reduce supply chain forecasting errors by 50% and costs by up to 40%. Inventory reductions of 50% are also possible. A popular fact about AI’s ability to leverage data is that it is able to recreate the results from the Human Genome Project, a 13-year long effort from 1990 to 2003, in just 24 hours. AI has the ability to directly lay the foundation for a new era of business and innovation.
AI and Machine Learning’s recognized applicability to data management has sparked a surge in the development of new AI/ML technology. The cognitive data management market is expected to grow at a CAGR of 21.5% through 2027. Last month, data & AI platform Databricks raised $1.6 billion to expand its engineering team, bringing them to a market-leading $38 billion valuation. Other significant market developments that are coming from this bubbling innovation are coming from smaller companies like Tredence Inc, Exasol, and Upsolver, who all received significant acclaim and investment in Q2 of this year for breakthroughs in ML and data analytic technologies. In the past year, leading organizations, on average, have developed 8 products or services directly related to data innovation. These same organizations are also twice as likely to say that their data-fuelled innovation has allowed them to capture more market share. Innovation has directly begun to breed new opportunities in the market. IBM recently announced that their new Analog AI will be 100 times more efficient than current AI data management technology. Mythic, a Silicon Valley AI startup, has already announced and released a similar technology. Investment and innovation have never been higher in this field and can be expected to continue well into the next decade.
The most important consideration of growth and innovation within the sphere of AI and data management is how it will impact the general population. It is great to hear about cost cuts and increases in operational efficiency for larger companies and institutions, but how will that impact us at the individual level? One way we all might see a true difference is through the healthcare industry. The healthcare industry accounts for 30% of the global data volume and by 2025, will reach a CAGR of 36%, more than any other industry. AI is already being utilized by healthcare professionals, especially in analysis of Electronic Health Records (EHR) and medical data curated by clinicians. This provides medical professionals with the ability to make more prompt decisions for their patients. We are going to see higher utilization of AI to minimize the cost and time of drug discovery, and even tools for more quickly identifying disease in patients. The Department of Veteran Affairs already uses an ML method that identifies kidney disease 90% of the time correctly, more than 48 hours before traditional care methods. The predictive capabilities of AI/ML data management technology and future innovation will cut medical costs substantially and save millions of lives.
With the unparalleled volume of data we can expect out of the next decade, cognitive data management techniques are going to transform the way we treat and utilize data. The demand for such technology has already ignited a wave of innovation and investment in the field and will continue to catalyze this trend. Capital injection into AI and its potential to revolutionize big data will ultimately transform the way business, medicine, government, and computing operate, forever. The marvel that is Artificial Intelligence will be a key player in many new market developments well into the future.