The Role of DQM in Machine Learning and Predictive Analysis
Enterprise IT leaders are racing to implement Artificial Intelligence for IT Operations (AIOps). Yet, a massive operational disconnect exists: machine learning algorithms are completely useless if they are trained on corrupt, fragmented infrastructure data.
The AIOps Data Disconnect
A recent study polling 200 C-level and Senior IT leaders reveals a stark reality. While 67 percent of respondents have a strong appetite for machine learning and predictive analytics, more than 60 percent have fundamentally low confidence in their own organization’s data quality. Despite this lack of trust, adoption is accelerating rapidly, with 22 percent of enterprises already running algorithmic programs and 42 percent planning implementations within the next 12 months.
Why Predictive Analytics Fail Without DQM
Feeding an algorithmic model un-reconciled, duplicated discovery data trains it to generate false positives. If a machine learning tool cannot distinguish between a retired legacy server and an active ephemeral cloud workload, it cannot automate incident remediation. True predictive analysis—such as identifying cascading memory leaks before they trigger a multi-cloud outage requires the flawless baseline that only Data Quality Management (DQM) can provide
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