5 Pillars of a Responsible AI Data Platform
Realize the full power of AI to transform every aspect of your business—without jeopardizing your data.
What goes into building a responsible, reliable AI data platform? At Aware, we believe it starts with making informed, conscious choices about how to access, use, and safeguard data. Over time, those decisions evolved into 5 critical pillars of the Aware AI data platform, and together they support its ability to transform massive amounts of unstructured data into real-time insights covering every aspect of the modern enterprise workflow.
Pillar 1: Ingestion
Good data is essential to good AI outputs, so the first component of an AI platform is the data it ingests. Aware ingests data from real-time collaboration conversations in tools like Slack, Teams, Webex, and Zoom via APIs and webhooks to avoid impacting the end user. Additionally, Aware can also ingest written messages from almost any other source, including write-in survey responses and social media platforms like Workplace from Meta and Reddit.
This data is informal, fragmented, unstructured, and filled with slang, jargon, gifs, emojis, and more. It is the unfiltered thoughts, opinions, experiences, and suggestions of an organization’s entire employee base, from the breakroom to the boardroom, delivered in an always-on stream of consciousness that represents a wealth of untapped value—and unmitigated risk. It is the fastest-growing dataset of the modern workplace, and successfully harnessing it can realize unprecedented results.
Pillar 2: Normalization
To make sense of this vast and complicated dataset, it must be normalized. The Aware data platform does so by creating a standard data language at scale that was purpose-built to handle the nuances of this unique dataset. Normalization is difficult but necessary work that underpins how the data is accessed, analyzed, and enriched.
Learn more about how the Aware platform normalizes data
Pillar 3: Enrichment
Aware leverages machine learning (ML) models in tandem with an intelligent data fabric to enrich each message with AI-powered metadata. Complex themes are extracted, and real-time natural language processing (NLP) and computer vision (CV) models identify risky and restricted content and score messages for sentiment, toxicity, and more.
Here, customers can create specific rules for analysis, adding triggers for regular expressions like PII/PHI/PCI or custom inputs such as IP and trade secrets. This process provides the mechanisms to protect sensitive data from unauthorized access or exfiltration and also supports a range of preexisting workflows across the enterprise, from eDiscovery and early case assessment to employee experience and wellbeing programs through the insights that come from the enrichment process.
Pillar 4: Insights
The insights created by these processes define the value of the AI data platform to the enterprise. They can identify the areas of the business where toxicity festers or data risks proliferate. Real-time insights can guide top-down messaging, identify systemic frustrations and inefficiencies, and support the proactive management of company culture to improve employee morale.
Aware AI data platform makes these insights actionable by connecting to existing workflows through APIs, reducing the time from analysis to decision. Generative AI summaries further support end users by providing easily digestible reports into insights, complete with verbatims for enhanced traceability.
Pillar 5: Governance
Data governance is essential to building trustworthy, responsible AI. As the fifth pillar of the Aware platform, we ensure we have the right processes in place to safely hold customer data and protect it from exfiltration. In-platform controls like RBAC further restrict data access, delivering insights to the right people to take action on what they contain. The result is an AI data platform purpose-built to solve multiple use cases across the enterprise—without jeopardizing its data.