Use a robust data strategy for strong ML development for AI success
The widespread adoption of machine learning (ML) and the rapid growth of artificial intelligence (AI) have given rise to greater operational and security concerns. As companies across industries integrate 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 a successful ML and AI implementation lies in data quality. Once a resilient data architecture is in place, organizations can realize the benefits, ensuring sustainable return on investment (ROI) while avoiding potential operational and security pitfalls. As these technologies become increasingly integral, the importance of data quality cannot be overstated, highlighting 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 uses structured data and learns from it to acquire knowledge with machine learning algorithms to make predictions, rule-based decisions and recommendations. The commercial attractiveness of ML lies in their ability to use the data to provide insights to the users, allowing them to make a more informed decision based on data they already possess. Data that can help predict the possible outcome of an action with some degree of confidence. The improvements these technologies provide, such as intelligent recommendations, knowledge assistants, predictive analytics and predictions, could be revolutionary for working life.
AI further advances ML by using general AI to automate tasks such as extracting intent from documents or text content, discerning feelings from phone calls, and interpreting human emotions in video calls or images. Optimal courses of action are then recommended, suggesting the best steps to take in a given circumstance. The availability of several common AI options from different vendors accelerates the adoption of AI services tailored to specific market requirements. However, this represents only one facet of the AI automation journey. The real potential lies in leveraging your own data to optimally and efficiently improve business operations.
Given the benefits, it’s no surprise that by 2025, nearly 100% of companies plan to implement some form of AI. However, if not done properly, common disadvantages of using AI and ML can include slow, costly automation, or partial or inaccurate results. These pitfalls can arise from organizations trying to automate without strict access rules and clear definitions for their data. Introducing automation into organizations with unmanaged, fragmented, or duplicated data only exacerbates existing dysfunctions. It increases inefficiencies and security issues, both of which can be avoided with a more advanced data strategy.
Business leaders must consider the dependence of AI and ML on the coherence of their organization’s underlying data layer. Without standardized, integrated and accessible sovereign data, you cannot fully train a machine and get it to perform optimally. Ultimately, an ML model is only as good and accurate as the data it is trained on. An AI system is only as smart and helpful as the data and rules on which it is based.
Key considerations when designing data management
Using limited, inaccurate, or outdated data to build AI and ML models is an inefficient use of assets. A carefully considered data and optimization strategy is a relatively simple way to prevent this. Due to different organizational structures, variety and use of data, the most appropriate strategy for each company will differ, 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 avoid knowledge gaps and latency issues. Human users and automated authentication protocols used by systems must have efficient and secure access to data. This is especially important in scenarios that require real-time analytics, time-sensitive decision support, or AIOps automation.
To function optimally, companies must embed consistency, order and structure into the foundational data layer that provides the AI or ML platform with a coherent and comprehensive framework. Data rationalization is essential for 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, enabling instant calculations, advanced analytics, and the execution of AIOps functions such as real-time authentication tasks or alert management.
Organizations can benefit more from AI and ML investments by developing an adequate optimization and data management strategy. This approach will simultaneously increase ROI while mitigating potential risks such as inaccuracies, cyber-attacks and compliance issues.
We are only now beginning to realize the full scope of the possibilities and improvements that AI and ML will bring to modern enterprises. Now is the best time to invest in a resilient data strategy to support these powerful tools. A key priority to remember during implementation is that a solid data management and optimization strategy must be based on the right underlying data architecture.
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