The widespread adoption of machine learning (ML) and the rapid growth of artificial intelligence (AI) have led to increased operational and security concerns. Consequently, as companies across various sectors incorporate these transformative technologies into their workflows, it becomes imperative that they 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 implemented, organizations can unlock the benefits, ensuring a sustainable return on investment (ROI) while avoiding potential operational and security issues. As these technologies become increasingly comprehensive, the importance of data quality cannot be overstated, emphasizing the need for well-defined data management protocols and optimization efforts.
Vice President of Solutions Consulting (Partners) at Appian Corporation.
Success depends on solid data
As a subset of AI, ML takes structured data and learns from it to gain insights with self-learning algorithms to make predictions, rule-based decisions, and recommendations. The selling point of ML lies in its ability to use data to provide information to users so they can make a more informed decision based on the data they already have. Data that can help predict the possible outcome of an action with a certain degree of confidence. The improvements offered by these technologies, such as intelligent recommendations, knowledge assistants, predictive analytics and forecasting, can be revolutionary for work life.
AI further advances machine learning by employing general AI to automate tasks such as extracting intent from documents or text content, discerning sentiment 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 only one facet of the AI automation journey. The real potential lies in leveraging your own data to improve business activities optimally and efficiently.
Considering the benefits, it is no surprise that by 2025, almost 100% of companies plan to implement some form of AI. However, if not executed correctly, common drawbacks of using AI and ML can include slow and costly automation or partial or inaccurate results. These obstacles can arise from organizations trying to automate without strict access rules and clear definitions for their data. Introducing automation into organizations with ungoverned, fragmented, or duplicate data only exacerbates existing dysfunctions. It amplifies inefficiencies and security issues, which can be avoided with a more sophisticated data strategy.
Business leaders should consider the dependence of AI and ML on the consistency of their organization's underlying data layer. You cannot fully train a machine and allow it to reach its full potential without standardized, integrated, and accessible sovereign data. 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 useful as the data and rules it is based on.
Key Considerations for Data Management Design
Using limited, inaccurate, or outdated data to create AI and ML models is an inefficient use of company resources. A carefully considered data and optimization strategy is a relatively simple 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 supporting their AI and ML-powered applications to eliminate downtime and accessibility issues. The strategy should include meticulous mapping of 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 particularly important in scenarios that demand real-time analytics, urgent decision support, or AIOps automation.
To operate at their full potential, businesses must build consistency, order, and structure into the foundational data layer that provides the AI or ML platform with a coherent, overarching framework. Streamlining data is essential to establishing common standards for metadata, business context, and interoperability. With this alignment, AI and ML platforms can make accurate comparisons by pulling from numerous data sources to enable instant calculations, advanced analytics, and the execution of AIOps functions, such as real-time authentication tasks or alert management.
Organizations can reap more benefits from AI and ML investments by developing a sufficient data management and optimization strategy. This approach will simultaneously increase return on investment while mitigating potential risks such as inaccuracies, cyber attacks, and compliance issues.
We are just beginning to realize the full range of capabilities and advancements that AI and ML will provide modern businesses. 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 based on the right underlying data architecture.
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