Move Over Dashboards: Enterprise Intelligence Is What Comes Next

Asato turns fragmented IT data into decision‑grade intelligence through normalization, classification, linking, scoping, and scoring. The result is accurate, consistent, and continuously improving data that leaders can trust for licenses, renewals, and shadow IT decisions.

Asato’s asset knowledge graph (MKG) is the intelligence layer that enables accurate IT asset optimization by solving foundational data issues such as inconsistent naming and noisy application inventories. It begins with multi‑signal entity resolution, using fuzzy logic, semantic vectors, purpose-built embeddings, and aggregated scoring to reliably match products despite typos, abbreviations, or ambiguous labels. Once an entity is correctly identified, the MKG enriches it with deep metadata, including application type, business function, vendor lineage, compliance attributes, and risk posture, enabling meaningful filtering and classification.

CASB and network discovery tools often report hundreds or thousands of “applications” in an enterprise environment. While technically accurate, these numbers are misleading because they include every service detected in network activity, not just the tools employees actually use for work. The list typically contains infrastructure services, CDNs like Cloudflare and Akamai, authentication endpoints such as accounts.google.com, browser extensions, APIs, and backend platform components. When these are treated the same as real applications, Shadow IT detection and license optimization become unreliable.

Organizations often see identity counts in systems like Entra ID that are two to three times higher than their actual employee headcount. This is not a data error, it’s because directories track every identity object, not just human employees. These include service accounts, guest users, shared accounts, test accounts, agents, disabled identities, admin accounts, federated users, and orphaned accounts discovered across systems. When platforms label this raw number as “Total Users,” it creates confusion because IT leaders expect “users” to represent people.

We break down why enterprise IT numbers rarely match reality and how contradictory identity, application, and spend data erodes trust. We dig into how inflated user counts, noisy app discovery, and fragmented purchasing emerge from systems that are each locally correct, and why true accuracy requires curated, decision‑grade data enriched with context, normalization, linkage, and consistent scoping.

At Asato, our security is embedded directly into infrastructure design, development workflows, and system architecture. I dive into how treating security as code, leveraging Infrastructure as Code (IaC), Terraform, multi-tenant isolation, service meshes, container security, and data protection strategies come together to build systems secure by design.

Asato’s CTO, Vinay Saxena, believes true business observability demands more than dashboards, it needs an AI-native, execution-first foundation. He emphasizes starting fresh: no legacy BI, no bolt-on AI. Instead, a system that links context to action, enabling enterprises to understand, decide, and execute, all in one intelligent loop.

Asato brings real-time clarity to complex IT environments by unifying usage, cost, access, and context into one intelligent, metadata-driven platform. Instead of static dashboards, it enables business observability—helping teams ask better questions and act with confidence.

TL;DR: Asato has assembled a top-tier team, including new board members Lip-Bu Tan and Kumar Sreekanti, and key leaders like Vinay Saxena, Rathna Raj Talacauvery, Nanda Vijaydev, and Rishi Sharma to build a Business Observability solution for CIOs.