Understanding the Five Ws of AI

By Philip Miller, Customer Success Manager for Progress MarkLogic.

  • 3 months ago Posted in

Undoubtedly, AI is set to impact organisations across all industries, with a recent Forbes survey finding that almost all (97%) of business owners believe that AI will play a role in helping their business.  


As enterprises and public sector organisations increasingly adopt this emerging technology to manage key systems and to find data solutions at scale, the need for accountability in the decisions generated by AI grows. After all, data must be considered in context, and without knowing the justification behind certain choices, organisations will lose the key to data governance - traceability.   


The current AI and ML information gap   


As data and tech leaders rush to explore the possibilities AI can generate for their businesses, it’s critical to address how one’s AI formulates the decisions it makes. And this is where the human intelligence behind AI comes into play. To trust and utilise the decision-making processes within artificial intelligence, it is first necessary to question the reasoning behind it and effectively determine the root of AI decision-making through data context.   

To do so, organisations must implement the Five Ws of AI: what happened to your data, when it happened, who made those decisions, where it happened and why changes or classifications were made to the data. If they risk overlooking the clarity provided by the answers to these questions, trust for any AI or ML (machine learning) application or system will be hard to generate.  


The potential benefits of AI   


Organisations are creating and storing vast quantities of data, and many don’t know how to go about improving data quality or know how to utilise it. AI can help improve data quality by using mechanical tools to spot relationships and patterns in data that are beyond human capabilities and can build a business process to make sense of these patterns.  


At a basic level, success in AI relies on training the system on whether a suggestion is good or bad and what that looks like by putting in feedback mechanisms - specialists in data transformation can help with this. Telling AI that it made a good recommendation is the secret to making intelligent decisions which will impact customer loyalty and the bottom line.  


AI-driven insights are defining a new era for ecommerce, where brands can truly understand their customers and what makes them tick to boost customer loyalty and increase revenue. One key area for deploying AI, for instance in a retail environment, is about how to make good product and service recommendations to customers based on data and how AI can develop feedback mechanisms that allow these predictions.  

AI can recognise the habits and behaviours of a customer to prompt them into action using a set of rules which determine how they are likely to react, and using natural language transformers like ChatGPT can provide sales assistants with message prompts that will encourage them to engage.  


Data analytics can also make predictions to help with inventory management and streamline back-room processes. Other key applications for AI in an ecommerce context include price and promotional optimisation, in-store / on-shelf availability, social media monitoring / sentiment analysis, demand forecasting and even fraud or threat detection, allowing them to monitor activity and make changes that are relevant to both the customer and the business.  


Determining the five Ws of AI  


It is essential to understand that data context is everything and that organisations must know the changes that have happened to the data—why it has been classified in such a way, what further context has been added and why certain data is more relevant to a query—and suddenly understanding human or AI-based decisions downstream becomes a lot easier to explain. Confidence in such decisions grows, and the ability to explain the rationale behind the decision becomes much easier.  


In fact, there is a core feature in the MarkLogic Data Platform that can assess geospatial metadata to answer these ‘where’ questions, as well as role-based access which clarifies data’s where and who.  


Once all this information is brought together, businesses are somewhere on their journey to answering the how of AI decision-making. Otherwise, the internal mysteries of AI are such that the decision-making process inside the algorithm or model is too complex for anyone to understand. But in looking at the five Ws of your data in conjunction with Semaphore Semantic AI technology, this offers a fully auditable, scalable and rule-based data management, classification and governance data platform designed with the future of data in mind.  


Putting data to use  


Organisations should start with a robust, transparent, secure and agile data platform to underpin the builds of enterprise-ready, business-critical applications. This dual capability is critical for the future of AI in the enterprise space, which has so much change on the horizon. A trusted technology provider can help develop, deploy and manage high-impact applications, like AI and ML solutions, ChatGPT, an automated AI agent or something completely new. But once you grasp the five Ws and have complete trust in the data, this is where the magic truly happens.  


Data lineage and auditing in the context of AI  


Auditability, traceability and lineage are aspects of a data governance system which should not be overlooked. In fact, this is never more true than when it comes to data used in AI, and how AI uses it to make decisions. In other words, real accountability means using the right technology or tooling as part of a robust data management and governance strategy. One key feature to choose when selecting an AI system is one which allows users to track both the valid time (when data is true in the real world) and transaction time (when data was entered into the database) for each record. This feature enables users to manage temporal data more effectively, making it easier to track changes over time and manage large volumes of changes in the data platform itself without the need for additional technologies in your architecture. The bottom line is that this simplifies the entire tech stack and reduces overall system cost.  


Giving AI the human touch will reach its potential  


It’s clear that AI is not a plug and play solution and that, despite the vast potential of AI technology, implementing it in today’s complex enterprise environments can seem like a daunting task. It’s a strong start for enterprises to eliminate data and knowledge silos with an enterprise-grade, unified data platform that lets them respond quickly to business change while providing rigorous data governance and transformational data security. However, those organisations that are working to find the perfect blend of human skills and technology to promote the best outcomes using AI will be the winners.  

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