Bold 2023 Data Predictions

Special thank you to Altair for providing the following set of bold predictions for 2023.

The data world continues to change rapidly and you may want to consider these predictions when planning for the new year.

The rise of generative AI startups: Generative artificial intelligence exploded in 2022. In this next year, we will see text processing and visual art using generative AI continue to improve. Entrepreneurs will look to get in on the action (and the $$) and many startups will emerge that create simple experiences for non-technical people based on generative AI. This could range from advertising copy, SQL queries, documentation copy, blog title ideas, code comments, instructional material, and even deepfake video content. Increasingly the creative output from these models is indistinguishable from— and in many cases superior to— human output.

~ Christian Buckner, SVP, Data Analytics and IoT, Altair

Big data isn’t dead (yet): Providers will attempt to get ahead trends, and we will see many start to advertise that “Big data is dead.” Instead, many organizations are leaning into “smart data” for greater insights. But despite the advertisements, big data will continue to play an important role in business operations— for now. The key is to make sure you have easy to use, self-service tools in place that enable cleansing, verifying, and prepping of the data that can then be plugged into a data analytics model for valuable results and smart decisions. The companies that turn their big data into smart data will be the ones that will benefit from the new ways of thinking about data.

Christian Buckner, SVP, Data Analytics and IoT, Altair

Deep learning is here: The next step for artificial intelligence in 2023 is deep learning. While AI so far has mostly been a mix of supervised machine learning and data analytics, the rise of deep learning will usher in a new era where computers are able to learn without supervision. Advancements in deep learning will lead to innovations in robotics, generative AI, natural language processing and speech recognition, and scientific breakthroughs in health, sustainability, and more. Of course, as with any AI models, the key for organizations to ensure the results are accurate and comply with new regulations emerging is to make sure there is still a human element for routine monitoring and trusted accuracy of the ML models.

~ Rosemary Francis, Chief Scientist, Altair

The need for efficiency: With inflation still raging and a potential recession continuing to loom ahead, efficiency will be a number one priority for organizations. Executives will be looking to squeeze every bit of efficiency out of their systems using data from across the enterprise. There will be an increased emphasis on machine-to-machine communication as data gets increasingly integrated from disparate systems, allowing businesses to utilize predictive analytics and AI-powered prescriptive decision making to make processes and production more efficient.

~ Ravi Kunju, Chief Product & Strategy Officer, Altair

Also, below are AI-specific predictions to be attributed to Dr. Mamdouh Refaat – Chief Data Scientist, Altair

More regulations around the business use of AI will come to the table: Over the last year, more businesses have realized the value of applying artificial intelligence and machine learning to business processes to make more strategic decisions based on real-time insights. The problem is, although not intended when first developed, AI algorithms can take on a mind of their own and have negative effects, as with potential bias. As AI gains more traction within the business community, more regulations are needed to ensure that data is used in fair, equitable, and transparent ways. However, there is a hope that the AI Bill of Rights along with the GDPR and the Transatlantic Data Privacy Framework will consolidate into a working platform for data exchange and applications of AI for the good of humanity. 

AI and machine learning models should be explainable: Building AI models is very complex. For AI to really take off, data science and business professionals need to better understand the technology and have access to tools that are easy to use and efficient in building models and gathering data.

AI pushes digital twins to business primetime: We have seen the adoption of digital twin technology at record speeds. According to a recent survey by Altair, nearly 70% of global businesses are already leveraging digital twins for multiple use cases (e.g., product design, risk assessment, predictive maintenance, sustainability), and 30%  plan to adopt digital twins in the next 1-2 years. As businesses further realize the untapped benefits of taking digital twins to the next level through the convergence of simulation technology, HPC (high-performance computing), and AI, is when we will see the true possibilities of this tech in revolutionizing industries, business processes, and scientific research come to fruition.

Humans should have the final say in critical decisions: Overall, we can expect to see AI catalyze big changes in the near future, but if there is one thing that should be guaranteed, humans should always be involved in AI programming and ensure they are disciplined and responsible for the implementation of this technology, along with safeguards of laws and best practices. Business professionals using the technology should always have the liberty to override algorithms for accurate information and safe and fair decisions.

The predictions above focus on artificial intelligence and data science. The data world is changing. It makes sense to consider these thought-provoking predictions for the coming year.

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Altair Inc. is an American multinational information technology company headquartered in Troy, Michigan. It provides software and cloud solutions for simulation, IoT, high performance computing (HPC), data analytics, and artificial intelligence (AI). The company was founded in 1985 and went public in 2017.

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