Undeniably, artificial intelligence (AI) affects organizations across all industries. Most business owners (97% according to a survey by Forbes) believe that AI will play a role in supporting their business.
As the adoption of innovative technology for key system management and finding data solutions at scale grows, both enterprises and public sector organizations will need accountability in the decisions generated. Without considering data in context or knowing the reasons behind certain choices, organizations will lose traceability – the key to data governance.
The Information Gap Between AI and Machine Learning
It is important for data and tech leaders exploring the possibilities of AI to address how one’s AI devises the decisions it makes. The importance of human intelligence behind AI is vital to this process. Trusting and using the decision-making processes and determining the foundation of AI-driven decisions through data context requires analysis of the reasoning behind those decisions.
Here is where The Five Ws of AI come into play:
- Who made the decisions
- What happened to the data
- When did it happen
- Where did it happen
- Why were the changes or classifications made
It is difficult to build trust for any AI or ML application or system when overlooking the clarity generated from answers to these questions.
Many organizations do not know how to improve the quality of or to use the enormous amounts of data they are creating and storing. Using mechanical tools that identify data relationships and patterns beyond human capabilities allows AI to make sense of the patterns by leveraging the business process.
AI’s Potential Benefits
However, AI’s success comes from training the system with feedback loops that help determine whether a suggestion is good or bad. Data transformation specialists can help. Improving customer loyalty and improving the bottom lines comes from AI’s ability to make intelligent decisions. The secret to building successful AI-driven outcomes relies on telling the application that it made a good recommendation.
Let us look at this in context. In ecommerce, AI-driven insights are defining a new era for this industry. Brands can more easily and accurately understand their customers and what motivates them to purchase, which can improve customer loyalty and increase revenue. Savvy e-tailers have deployed AI to help customers feel confident in their product and service decisions by presenting recommendations to customers. These recommendations are based on data bolstered by AI’s ability to develop feedback mechanisms that allow these predictions.
Using a set of rules that decide how a customer is likely to react allows AI to recognize the habits and behaviors of a customer to prompt them into action. Encouraging engagement comes from using natural language transformers like ChatGPT that provide sales assistants with appropriate messages.
Data analytics can also predict events that affect inventory management and streamline back-room operations. Applications where AI shines help e-tailers monitor activity and make changes relevant to the customer and the business, including price and promotional optimization, in-store / on-shelf availability, social media monitoring / sentiment analysis, demand forecasting and fraud or threat detection.
The Knowledge of the Five Ws of AI
Data context is everything to an organization’s understanding of changes that have happened to the data.
Understanding human or AI-based decisions downstream becomes a lot easier to explain when organizations know:
- Why data has been classified in such a way and why certain data is more relevant to a query
- What other context was added
A clear understanding of the answers to these questions helps grow confidence in output and makes it easier to explain the rationale behind the decisions. Adopting a solution that can assess geospatial metadata to answer these ‘where’ questions, as well as role-based access which clarifies data’s where and who will help companies reach their AI goals more quickly.
When this information is brought together, businesses can start their journey to answering the how of AI decision-making. The decision-making process inside the algorithms or model is too complex for anyone to understand due to the internal mysteries of AI. When assessing the Five Ws of data, the promise of a future-built fully auditable, scalable, and rule-based data management, classification, and governance data platform further strengthens digital transformation efforts.
Using Data Effectively
So where should organizations start? A robust, transparent, secure and agile data platform serves as the cornerstone to building enterprise-ready, business-critical applications. The enterprise space has so much change ahead that this dual capability is critical for the future of AI. 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. The magic begins after understanding the five Ws and gaining complete trust in the data.
In the Context of AI, Data Lineage and Auditing are Important
There are three aspects of a data governance system that should not be overlooked: Auditability, traceability and lineage. They are key to effective data usage and decision-making in AI.
Using the right technology or tooling as part of a robust data management and governance strategy leads to real accountability. When selecting an AI system, choose one that allows users to:
- Track the valid time (when data is true in the real world)
- See transaction time (when data was entered into the database) for each record
This lets users manage temporal data more effectively and makes it easier to track changes over time. Users can also manage large volumes of changes in the data platform itself without the need for additional technologies in the architecture. The goal is to simplify the entire tech stack and reduce overall system cost.
The Human Touch Helps AI Reach its Potential
AI is not a plug-and-play solution. Despite the potential of AI technology, implementing it in today’s complex enterprise environments can seem overwhelming. However, AI allows enterprises to eliminate data and knowledge silos when using an enterprise-grade, unified data platform that lets them respond quickly to business changes. Rigorous data governance and transformational data security are also benefits. The true winners are those organizations working to find the perfect blend of human skills and technology to promote the best outcomes using AI.
