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The crux of successful ML and AI implementation lies indataquality.
Vice President, Solutions Consulting (Partners) at Appian Corporation.
Data that can help predict the possible outcome of an action with a degree of confidence.
It then recommends optimal courses of action, suggesting the best steps to take in a given circumstance.
However, this represents just one facet of the automation journey with AI.
The true potential lies in leveraging your own data to enhance business activities optimally and efficiently.
These pitfalls can stem from organizations attempting to automate without strict access rules and clear definitions for their data.
Introducing automation to organizations with ungoverned, fragmented or duplicated data only exacerbates existing dysfunctions.
It amplifies inefficiencies and security concerns, both of which are avoidable with a more sophisticated data strategy.
Business leaders must consider AIs and MLs dependance on the coherence of their organisations underlying data layer.
Ultimately, a ML model is only ever as good and accurate as the data on which its trained.
An AI system is only as clever and helpful as the data and rules its based on.
A carefully considered data and optimization strategy is a relatively straightforward way to avoid this.
The strategy should include meticulously mapping the locations of all data repositories to prevent knowledge gaps and latency issues.
Human users and automated authentication protocols used by systems should have efficient and secure access to data.
This is particularly important in scenarios that demand real-time analysis, time-sensitive decision support, or AIOps automation.
Rationalizing data is essential to establishing common standards for metadata, business context, and interoperability.
Organisations can reap more benefits from AI and ML investments by crafting a sufficient optimisation and data management strategy.
This approach will simultaneously increase ROI while mitigating potential risks such as inaccuracies, cyberattacks, and compliance issues.
Now is the best time to invest in a resilient data strategy to inform these powerful tools.
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