Before you automate systems, decide which data wins
Ask an IT team a simple question, “How many laptops do we have?” and the answer often depends on who you ask and which console they open. For years, that ambiguity was manageable. People reconciled the differences in their heads, in spreadsheets, or in meetings. Ivanti research shows that 34% of organizations still track IT assets in spreadsheets, which shows how much reconciliation still happens…
When contemplating automation of IT systems, the crucial first step is determining which data sources provide the most reliable foundation. Companies often struggle with inconsistencies in asset tracking, with some still relying on spreadsheets for 34% of organizations, according to Ivanti research. The stakes rise significantly when software executes decisions without human oversight, making it imperative to establish clear data ownership and authority.
A "system of record" refers to the authoritative source for specific data types, ensuring that all processes and tools trust this source when discrepancies arise. Ideally, this involves designating separate records for different domains, such as device identity, user information, and service relationships. Relying on a single database for all data is often impractical in most environments.
Without clear data ownership and authority, automated workflows can lead to errors, such as applying patches to the wrong device or flagging active devices as potentially lost due to outdated check-in dates. The accuracy and reliability of automated decisions are only as good as the data feeding them. Therefore, it is essential to define which data attributes the automation system will rely on, assigning each an authoritative source and accountable owner.
Traceability is equally important, as it enables teams to quickly understand why an automated action behaved a certain way. By tracing actions back to specific records and attributes, along with the last update date, troubleshooting becomes more efficient. The Ivanti Neurons platform connects asset data with service maps, ownership, and business priorities, providing the necessary context for automated decisions.
While the workflows themselves are visible and measurable, the data foundation is often overlooked. Automating inconsistencies leads to months spent resolving side effects. Real value in AI-driven autonomous endpoint management comes from trusting the underlying data foundation. Organizations that prioritize this investment reap rewards across all automated workflows, avoiding the pitfalls of guessing and ensuring that automation is grounded in reliable data.
Written by urgent.news from Computerworld's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
