These days, AI and ML technology is evolving and maturing incredibly quickly. As AI-powered technology grows and businesses improve their ability to harness and use data to feed AI algorithms, AI decisioning platforms are becoming increasingly crucial. These platforms leverage AI to analyze and act upon massive data sets. Given the right data intelligence and context, AI decisioning platforms bring huge growth potential to MadTech businesses.
To maximize AI decisioning platforms, they need to be fed the right information. When paired with data warehouses for big data processing and knowledge graphs for contextual insight, AI decisioning platforms can reach their true potential. The post explores the integration of these tools, including challenges, applications, future expectations and considerations, and, of course – the Aqfer approach to this evolving landscape.
What Are AI Decisioning Platforms?
AI decisioning platforms are sophisticated systems designed to retrieve, process, and make decisions based on vast amounts of data.
These platforms leverage artificial intelligence and machine learning algorithms to analyze complex datasets and provide actionable insights. Key functionalities include data retrieval from various sources, real-time processing, and automated decision-making across multiple business domains.
Data Warehouses + Knowledge Graphs + AI Decisioning: A Powerful Combination
Data warehouses have long been the backbone of enterprise data management, serving as centralized repositories for structured data from various sources. Knowledge graphs, on the other hand, represent a more recent innovation, offering a semantic network of interconnected entities and their relationships.
Together, AI decisioning platforms, data warehouses, and knowledge graphs unlock unprecedented capabilities. AI can leverage the structured data in warehouses for deep analytical insights, while simultaneously navigating the complex relationships represented in knowledge graphs. This combination enables more contextual, nuanced decision-making.
The Toolkit at Work
For example, an AI decisioning platform could use a data warehouse to access historical sales data, while consulting a knowledge graph to understand product relationships and customer preferences. This holistic view allows for more accurate predictive modeling and personalized recommendations. AI can identify patterns and connections that might be invisible to human analysts, potentially uncovering new opportunities and/or operational efficiencies.
The integration also enhances the ability to handle complex queries. An AI decisioning platform can dynamically determine whether to pull aggregated data from a data warehouse, traverse a knowledge graph for relationship-based insights, or combine both approaches. This flexibility allows for more sophisticated problem-solving and decision support across various business domains, from marketing and sales to supply chain management and product development.
This powerful combination fuels continuous learning and improvement. As the AI makes decisions and observes outcomes, it feeds this information back into the data warehouse and refines the relationships in the knowledge graph, creating a virtuous cycle of ever-improving data quality and decision accuracy.
Challenges of Integration
Integrating AI decisioning platforms with data warehouses and knowledge graphs presents several technical and operational challenges:
Data Silos
Many organizations struggle with fragmented data across different systems, making it difficult to create a unified view for AI-driven decision-making.
Real-Time Processing Demands
AI decisioning often requires real-time or near-real-time data processing, which can strain traditional data warehouse architectures.
Scalability
As data volumes grow exponentially, ensuring that integrated systems can scale efficiently becomes crucial.
Balancing Flexibility & Constraints
AI systems need to be flexible enough to discover new insights while respecting business constraints and rules.
Aqfer’s Solution to Integration Hurdles
At Aqfer, we recognize that the future of AI decisioning platforms lies in their ability to seamlessly interact with diverse data ecosystems. Our approach focuses on:
Flexible Data Architecture
We’re moving beyond traditional data warehouses to embrace a more flexible “data grid” concept, incorporating lake houses and data marts tailored to specific domains and use cases.
Optimized Data Access
We’re developing innovations that allow for extremely fast, cost-effective retrieval of what we call “audience member vectors,” enabling rapid access to population-level data for AI decisioning.
AI Readiness
Our solutions are designed to prepare and stage data in ways that make it readily accessible for various AI applications, from supervised learning to more advanced generative AI approaches.
Identity Resolution
We understand the critical role of identity in connecting disparate data points, and our platform excels in providing efficient access to identity-related time series data.
The Future of AI Decisioning
Looking ahead, we can envision several exciting developments in the integration of AI decisioning platforms with data warehouses and knowledge graphs:
Deep Knowledge Integration
Future AI models may have the ability to internalize complex relationships between customer attributes, allowing for more nuanced decision-making without extensive querying.
Hybrid Querying Approaches
AI decisioning platforms might dynamically choose between direct AI-generated responses and deep dives into data warehouses based on the nature of the query.
Advanced Prompt Engineering
As AI models become more sophisticated, we’ll see improvements in how decisioning platforms formulate queries and prompts to extract the most relevant information from data stores.
Automated Feature Discovery
AI systems may autonomously identify and utilize new data features and relationships, enhancing the value of existing data warehouses and knowledge graphs.
As we move into this new era, businesses must critically evaluate their data strategies. It’s not just about having vast amounts of data or the latest AI models – it’s about creating an ecosystem where these elements work together seamlessly.
At Aqfer, we’re committed to helping organizations navigate this complex landscape, ensuring they’re not just AI-ready, but AI-optimized. Ready to see how Aqfer can help your business chart the course for an AI-powered future? Reach out to get the conversation started.
About the Author
Daniel Jaye
Chief Executive Officer
Dan has provided strategic, tactical and technology advisory services to a wide range of marketing technology and big data companies. Clients have included Altiscale, ShareThis, Ghostery, OwnerIQ, Netezza, Akamai, and Tremor Media. Dan was the founder and CEO of Korrelate, a leading automotive marketing attribution company, purchased by J.D. Power in 2014. Dan is the former president of TACODA, bought by AOL in 2007, and was the founder and CTO of Permissus, an enterprise privacy compliance technology provider. He was the Founder and CTO of Engage and served as the acting CTO of CMGI. Prior to Engage, he was the director of High Performance Computing at Fidelity Investments and worked at Epsilon and Accenture (formerly Andersen Consulting).
Dan graduated magna cum laude with a BA in Astronomy and Astrophysics and Physics from Harvard University.