What Are the Key AI Use Cases & Data Insights? – Part III
In Part I of our series, we explored the advantages of AI, along with the security, compliance considerations, challenges, and governance aspects. In Part II, we focused on AI policies. Now, in Part III, we turn our attention to AI Use Cases and the importance of data.
It’s no secret that nearly every new cybersecurity product now integrates some form of machine learning (ML) or artificial intelligence (AI). AI has become a hot topic, and many organizations are either already using or planning to invest in AI tools. However, the adoption of AI within an organization needs to be approached with a clear understanding of its broader implications. AI must be governed by well-defined policies and procedures, taking into account important considerations like ethics, accountability, and transparency.
To illustrate how AI is being used, let’s look at some real-world use cases provided by the UK’s Office for Artificial Intelligence.
One example is from a signaling company that used AI to help ensure trains run on time. The company responsible for managing railway traffic wanted to predict delays and use these insights to minimize the impact on the train network. By forecasting potential delays, the company could redirect traffic to reduce disruptions.
They developed three separate models to achieve this:
- Forecasting Model – This model used historical data, including train arrival times, positions, lateness, and timetables, to predict delays.
- Pattern Recognition Model – This model analyzed patterns in the data to help controllers better understand how the network operates and what typically causes delays
- Recommendation Engine – This engine suggested alternative platforms to controllers in real-time to help mitigate delays.
The system was able to predict delays with up to 50% greater accuracy than before, providing the company with up to one hour's notice of potential disruptions.
Another interesting use case comes from a research institution studying energy consumption. The institution wanted to understand which appliances were being used in homes at different times, in order to optimize heating and energy usage. Using non-intrusive load monitoring, the researchers gathered data from electricity meters to identify which appliances were active and when.
They employed unsupervised machine learning techniques to analyze this unlabeled data, uncovering patterns in energy consumption. From these insights, they were able to cluster appliances based on their power usage, providing a clearer picture of energy consumption trends.
Although the concept of analyzing data and creating predictive models isn’t new, AI has recently become more effective thanks to advances in computational power and the availability of large datasets.
A key element to the success of AI and ML is data—specifically, the right data and in the right quantity. Organizations often face challenges in getting their data into the right format and ensuring there’s enough of it to create effective AI models. Once the data is gathered, it’s used to train models that can predict or detect patterns, giving the organization valuable insights.
Monica Rogati’s Data Science Hierarchy of Needs pyramid highlights the essential steps required to build intelligence from data.
At the foundation of this pyramid lies data collection. The first step is ensuring that organizations gather the correct datasets. Understanding the flow of data is crucial for effective collection. Once collected, the data must undergo cleaning to address any anomalies before it’s ready for analytics. These steps closely resemble those in traditional business intelligence but go a step further, as the goal is to build predictive models using that data.
It’s essential to have clear use cases at this stage, as they will guide what you want to predict or learn from the data. This direction helps in building the appropriate machine learning models.
AI companies often excel at data organization. Centralizing data in a data warehouse makes it more accessible and efficient for engineers and software systems to work with. Therefore, the process of data gathering and ensuring data reliability is critical to the success of AI implementation.
In conclusion, understanding AI’s use cases and ensuring proper data governance are vital steps in leveraging its full potential. Data, when properly organized and managed, forms the backbone of any successful AI initiative, making it possible to build predictive models that drive meaningful insights and business outcomes.
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