Use robust data strategy for strong ML development for AI success
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The widespread adoption of machine learning (ML) and the rapid growth of artificial intelligence (AI) have given rise to heightened operational and security concerns. Consequently, as companies across diverse sectors incorporate these transformative technologies into their workflows, it becomes imperative for them to implement and enforce robust data management practices and optimization strategies.
The crux of successful ML and AI implementation lies in data quality. Once a resilient data architecture is in place, organizations can unlock the benefits, ensuring a sustainable return on investment (ROI) while steering clear of potential operational and security pitfalls. As these technologies become increasingly integral, one cannot overstate the significance of data quality, emphasizing the need for well-defined data management protocols and optimization efforts.
Vice President, Solutions Consulting (Partners) at Appian Corporation.
Success depends on strong data
As a subset of AI, ML takes structured data and learns from it to acquire knowledge with self-learning algorithms to make predictions, rules-based decisions and recommendations. The commercial attractiveness of ML resides in their ability to use the data to provide insights for the users to make a more informed decision based on data that they already hold. Data that can help predict the possible outcome of an action with a degree of confidence. The enhancements these technologies offer, such as intelligent recommendations, knowledge assistants, predictive analytics and forecasts, can be revolutionary to working life.
AI advances ML further by employing General AI to automate tasks such as extracting intents from documents or text content, discerning sentiments from phone calls, and interpreting human emotions in video calls or images. It then recommends optimal courses of action, suggesting the best steps to take in a given circumstance. The availability of various General AI options from different vendors accelerates the adoption of AI services tailored to specific market demands. 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.
Given the benefits, it’s no surprise that by 2025, nearly 100% of enterprises plan to implement some form of AI. However, if not executed properly, common drawbacks of AI and ML use may include slow, costly automation or partial or inaccurate outputs. 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 AI’s and ML’s dependance on the coherence of their organisation’s underlying data layer. One cannot fully train a machine and allow it to perform to its full potential without standardised, integrated and accessible sovereign data. Ultimately, a ML model is only ever as good and accurate as the data on which it’s trained. An AI system is only as clever and helpful as the data and rules it’s based on.
Key considerations for data management design
Using limited, inaccurate, or dated data to build AI and ML models is an inefficient use of company resources. A carefully considered data and optimization strategy is a relatively straightforward way to avoid this. Due to different organizational structures, variety and use of data, the most appropriate strategy will vary for each business, but the same critical principles should apply.
First, organizations must identify their data stores and ensure reliable access to the systems that support their AI- and ML-driven applications to eliminate downtime and accessibility issues. 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.
To operate at full potential, companies should embed consistency, order and structure into the foundational data layer that provides the AI or ML platform with a coherent and all-encompassing framework. Rationalizing data is essential to establishing common standards for metadata, business context, and interoperability. With this alignment, AI and ML platforms can make accurate comparisons when drawing from numerous data sources to allow for instantaneous calculations, advanced analytics, and the execution of AIOps functions such as real-time authentication tasks or alert management.
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.
We are just starting to realize the full scope of capabilities and advancements that AI and ML will provide for modern enterprises. Now is the best time to invest in a resilient data strategy to inform these powerful tools. A key priority to remember during implementation is that a solid data management and optimization strategy will need to be founded on the right underlying data architecture.
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