Big Data Industry Predictions for 2023

InsideBIGDATA’s annual technology predictions round-up! The big data industry has significant inertia moving into 2023. In order to give our valued readers a pulse on important new trends leading into next year, we here at insideBIGDATA heard from all our friends across the vendor ecosystem to get their insights, reflections and predictions for what may be coming. We were very encouraged to hear such exciting perspectives. Even if only half actually come true, Big Data in the next year is destined to be quite an exciting ride. Enjoy!

Standards-based Semantic Layers Will Power Data Selection through Business Terms: Data fabrics, data lakes, and data lake houses contain a surplus of unstructured and semi-structured data from external sources. In 2023 there will be a significant uptick in organizations applying W3C standards-based semantic layers atop these architectures, where data assets are described by metadata in familiar business terms and enable business users to select data through a lens of business understanding. This method will provide a seamless business understanding of data that fosters a culture of data literacy and self-service, while simplifying data integration and improving analytics. – Jans Aasman, Ph.D., an expert in Cognitive Science and CEO of Franz Inc.

Causal Knowledge Graphs will Emerge: The next few years will see growth in Causal AI starting with the creation of Knowledge Graphs that discover causal relationships between events. Healthcare, pharma, financial services, manufacturing and supply chain organizations will link domain-specific knowledge graphs with causal graphs and conduct simulations to go beyond correlation-based machine learning that relies on historical data. Causal predictions have the potential to improve the explainability of AI by making cause-and-effect relationships transparent. – Jans Aasman, Ph.D., an expert in Cognitive Science and CEO of Franz Inc.

Natural Language Understanding Will Become Part of AI Models: In 2023 we will start to see natural language understanding become possible for AI applications. There will be a transition from simple pattern matching to language understanding within the underlying model. By starting with taxonomies, ontologies, speech technology and new rule based approaches – it will be possible to take natural language understanding and instantly turn it into triples that describe the pragmatics of the world. These triples become the underlying ontological description of the world, which is essential to produce high-quality AI using natural language. – Jans Aasman, Ph.D., an expert in Cognitive Science and CEO of Franz Inc.

Read all the predictions at Inside Big Data.