Revolutionizing Supply Chains: How Semantic Visions is Transforming Risk Management with AI

The deprecation of the models also removes features like model layouts and context menu shortcuts that supported quick report creation using these models, Microsoft said. Instead, users will have to explicitly create and manage semantic models through a new entry point under the Home tab. Cubes and data extracts were introduced to overcome the performance issues of analytics and data platforms. This approach introduces data copies, adds complexity, destroys agility, and introduces latency. A modern semantic layer improves performance regardless of the underlying data model, whether it’s a snowflake, a star, or purely OLTP schema. By automatically creating and managing aggregates or materialized views inside the underlying data platform, a semantic layer learns from user query patterns and optimizes the data platform’s performance and cost without data movement.

• Semantic Visions addresses this by offering AI-powered tools that monitor risks and identify opportunities, even within often-overlooked lower tiers of the supply chain.
• To create a universal semantic layer, data teams must first develop the business logic and information that go into a semantic data model.
• For companies just starting their digital transformation journey, he advises focusing on critical pain points and adopting widely used platforms for smoother transitions.
• By combining open-source intelligence (OSINT) data with AI, Semantic Visions enables companies to uncover similar inefficiencies that might otherwise go unnoticed.

Allen Institute’s Semantic Scholar now searches across 175 million academic papers

When business and data science teams collaborate using a semantic layer, they enhance their historical data with predictive insights. Closing the gap between business intelligence and data science teams provides more visibility into the output of data science initiatives throughout the organization and enables organizations to leverage their data for predictive and prescriptive analytics. Augmented intelligence (also called augmented analytics or decision intelligence) brings AI-generated insights into traditional business intelligence workflows to improve data-driven decisions. Once data becomes highly accessible, teams can collaborate not just within their four walls, but blend data from second and third-party data sources to unlock the power of data and analytics for everyone. Closing the gap between business intelligence and data science teams is the key to achieving a high level of data analytics maturity and applying all types of analytics at scale.

• The big data wave has hit the digital landscape, and with data being king, applications and semantics like AtScale will continue to offer valuable and critical services for all businesses, regardless of industry, according to Lynch.
• For example, their AI-driven tools can detect subtle supply chain disruptions, such as delays caused by specific suppliers or inefficient processes, by analyzing unstructured data from global sources.
• His expertise shapes the company’s efforts to use advanced technologies like artificial intelligence (AI) and data-driven tools to help businesses tackle these issues head-on.
• In contrast, augmenting data through the unification of BI and data science adds AI-enhanced data to the semantic layer, providing the same insights across the consumer spectrum, regardless of the tool used.
• Knowledge graphs serve as an interface in between, providing high-quality linked and normalized data.

AWS imposes caps on Kiro usage, introduces waitlist for new users

The platform can manage multiple projects simultaneously and integrate user profiles to create a conversational digital environment that accelerates digital ecosystem improvements. SQUADRA, a technology consultancy specialising in supporting companies in their Digital Transformation journeys, has announced the launch of Genius, a multipurpose platform driven by AI. Brazilian consultancy SQUADRA has launched Genius – a multipurpose AI-powered platform developed to accelerate legacy system modernisation and digital solution delivery.

• APIs for integration integrate with various endpoints using AI, GraphQL, MDX, REST and SQL APIs, enabling seamless data access and usage across platforms. If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

Ultimately, every organization wants to empower every individual to make data-driven decisions. A semantic layer can become the vehicle for delivering augmented intelligence to a broader audience by publishing the results of data science programs through existing BI channels. By feeding data science model results back into the semantic layer, your organization can capture benefits beyond just historical analysis.

Google’s Looker requires users to define their own models using LookML, and more recently the hyperscaler has added Gemini to it to enable AI-generated model suggestions and chat-based analytics to accelerate insights generation. Microsoft is not the only hyperscaler that is betting on enterprises adopting a well-structured semantic layer instead of one auto-generated on the fly. As digital transformation accelerates, leaders like Balatka with Semantic Visions are paving the way for a more resilient and efficient supply chain industry. Balatka predicts that automation and the use of both software and hardware robots will play a key role in addressing labor shortages and improving efficiency. For companies just starting their digital transformation journey, he advises focusing on critical pain points and adopting widely used platforms for smoother transitions. Genius powers SQUADRA’s Legacy Modernization by Genius initiative using intelligent tools to extract value from legacy applications, document the systems’ implicit knowledge and reintegrate it into modern applications.

An alternative approach is to develop tools for analysts to directly access an enterprise knowledge graph to extract a subset of data that can be quickly transformed into structures for analysis. A universal semantic layer is an important feature as AI leaves the testbed and enters the real world. This is just one illustration of how a semantic layer helps to prevent models from providing results that they otherwise couldn’t be certain to be accurate. With a universal semantic layer, the LLM can select items in a text-to-semantic layer query when it is questioned, and those objects’ properties are defined there to provide answers, not throughout the entire body of knowledge that the LLM has been educated on.

The role of AI in shaping the semantic layer: Insights from AtScale

In many industries, such as finance and healthcare, it is becoming increasingly important to implement AI systems that make their decisions explainable and transparent, incorporating new conditions and regulatory frameworks quickly. At the same time, knowledge graphs have been recognized by many industries as an efficient approach to data governance, metadata management and data enrichment and are increasingly being used as data integration technology. But knowledge graphs are also more and more identified as the building blocks of an AI strategy that enables explainable AI through the design principle called human-in-the-loop (HITL). The future of law enforcement technology isn’t just about speed — it’s about strategic accuracy with human expertise. Putting a universal semantic layer between your data sources and consumers can initially feel disruptive.

SQUADRA invests US$3.6 million in AI platform Genius to fast-track Digital Transformation

They can also use the same governed data to reliably “drill down” into the details of a prediction. As a result, your organization can foster more self-service and greater data science literacy and generate a better return on data science investments. As illustrated by the table above, business users typically focus on historical analysis while data scientists are working to predict the future.

To create a universal semantic layer, data teams must first develop the business logic and information that go into a semantic data model. Over two million users have adopted Semantic Scholar to date to analyze the academic literature, surfacing phenomena from male bias in clinical studies to the accelerating pace of China’s AI research. And now, the Allen Institute hopes to lay the semantic groundwork for the next few million users to come.