The rise of artificial intelligence (AI) is transforming the business intelligence (BI) landscape, reshaping how organizations gather, analyze, and act on their data. For decades, BI has enabled data-driven decision-making, powering operational improvements, enhancing product quality, and increasing efficiency across sectors. But with the advent of generative AI, a new era has begun – one where advanced AI models extend the power of BI by bringing deeper insights, creativity, and adaptability to the forefront of business strategy.
Challenges with AI and data analytics
By integrating AI seamlessly into your data experiences, you can supercharge insights across any application. However, AI by itself is not a complete analytics solution. AI relies on the quality of data being fed into it. Data inconsistencies and misinterpretations decrease the output quality, resulting in less effective AI models or worse, inaccurate results that do more harm than good.
Challenge 1: AI does not fix data quality and data silo problems
Even the most sophisticated AI can only query and understand the data it interacts with. Inconsistent or poor-quality data will return flawed results. This is particularly evident in organizations where data analysis happens via spreadsheets or point-solution BI. The absence of a single version of the truth (SVOT) leads to non-standardized definitions and conflicting interpretations.
Such discrepancies lead to disagreements over the results which undermine trust in the data and the decisions derived from them. When data lacks a unified reference point it exists in silos. Implementing AI on top of data silos exacerbates the problem of data inconsistencies. There is no unified data source that AI is accessing and analyzing.
Challenge 2: AI and the problem of inaccurate data interpretation
Current AI large language models (LLMs) are engineered to generate human-like text. They understand context and perform a wide variety of natural language processing tasks.
However, these models are not specifically designed for reliable data calculations. They are trained on textual datasets, so their ability to perform numerical operations depends on how well math is described in the data. This limitation can lead to LLM hallucinations, resulting in inaccuracies when making precise mathematical computations.
Solution: a semantic graph to solve AI’s data challenges
Data silos and the associated data inconsistencies, as well as limitations of AI in data analysis, can be solved with a semantic graph, a technology layer that provides centralized and reusable data structure. A semantic graph acts as an interpretive layer, translating source data into meaningful and unified business concepts and relationships. It standardizes an organization’s business logic and definitions, forming data relationships that provide an enterprise-wide single version of the truth. Not only does it help to bolster data integrity, but it also serves as a vital component for AI integration, guiding the AI in its understanding and interpretation of the data.
From a data abstraction perspective, basic productivity tools like Microsoft Excel often lack a semantic layer/model altogether, whereas point-solution BI tools usually confine semantic definitions within individual datasets. In contrast, more robust BI platforms utilize a foundational semantic graph that resides below the dataset level, serving as a base to create various upstream objects, including datasets.
Strategy’s semantic graph
It creates a shared understanding of data and business rules across an organization, allowing for your data to be sorted in real-time according to patterns that are pre-mapped by the platform engine. Objects created in the Semantic Graph are reusable, inheritable, and privacy aware. Its dynamic, centralized data model permeates the entire platform, guiding and governing AI-generated insights.
Strategy’s unique semantic graph technology allows you to uncover rich insights and deep connections within your data, going beyond conventional analytics. Ready to try AI yourself? Contact us!
(source: https://www.strategysoftware.com/research-and-reports/the-rise-of-trusted-ai)
