Last week, I mentioned that we would revisit data analytics, and here we are. The scope of Artificial Intelligence (AI) is so vast that it is challenging to grasp its full potential. Data analytics, in many ways, serves as the foundation for artificial intelligence, driving much of the insights and decision-making power behind AI systems.
So, over the next few weeks, let us work on demystifying data analytics step-by-step. This deeper exploration should be especially valuable for readers who want to dive into this field and understand its significance.
Artificial intelligence and data analytics are reshaping the business world, transforming how companies handle data and make decisions. These technologies provide powerful tools for understanding vast amounts of information, allowing businesses to act faster and more accurately. From predicting customer needs to helping solve global issues, AI and data analytics have massive potential.
This article explores key trends that are shaping this landscape during the last quarter of 2024 and beyond, from real-time data processing to ethical AI practices.
AI, machine learning
AI-driven analytics is speeding up the process of turning data into insights. In sectors like finance, healthcare, and retail, AI tools can analyse large datasets much faster than humans, finding patterns that would otherwise be missed.
For instance, big global banking institutions use AI-powered tools to detect fraudulent transactions within seconds, protecting customers and reducing losses. Additionally, with tools like Automated Machine Learning (AutoML), businesses without big tech teams can build predictive models. This makes advanced analytics accessible for companies of all sizes, supporting faster and better decision-making.
Real-time, edge data processing
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In today’s fast-paced world, businesses increasingly need real-time insights to stay competitive. Real-time data processing allows companies to act immediately on latest information, which is especially valuable in industries like e-commerce, finance, and healthcare.
Take Amazon, for instance. Real-time data analysis enables the company to adjust prices based on demand or stock, offering customers relevant product recommendations while refining sales.
Data democratisation, storytelling
One exciting trend is data democratisation, which makes data available to everyone in an organisation, not just technical teams.
For example, Coca-Cola’s data platform allows employees across departments to access and use data insights. Tools like Microsoft Power BI and Tableau offer user-friendly dashboards, enabling staff at all levels to make data-driven decisions without needing deep technical ability.
However, as more people use data, it is important for companies to provide data literacy training to avoid misinterpretations that could lead to mistakes.
To make data insights actionable, companies are also focusing on “data storytelling.” This approach combines data with narrative techniques to help people understand the “why” behind the numbers. In America, large retail corporations like Walmart use data storytelling to guide decision-making across vast supply chains.
By visualising data in an engaging way, stakeholders can better grasp the key takeaways, making it easier to act on data insights.
Ensuring responsible data use
With the increased use of data, ethical practices have become essential. As companies collect more personal information, they must handle it responsibly to protect user privacy and build trust. In the EU, for instance, companies must comply with the General Data Protection Regulation (GDPR), a strict regulation on data privacy. Beyond compliance, transparency and fairness are also important.
Organisations are increasingly working to avoid bias in AI models, which is crucial for fairness, especially in areas like hiring and lending.
IBM is leading in this area, promoting AI fairness and transparency through its “AI Fairness 360” toolkit, which helps developers identify and correct bias in machine learning models. By embracing ethical practices, companies can foster trust with customers, ensuring that AI decisions are both exact and fair.
Harnessing IoT
The rise of interconnected devices, known as the Internet of Things (IoT), has led to a flood of new data. From smart home devices to city infrastructure, IoT generates large volumes of data that businesses can analyse for better decision-making.
In cities like Barcelona, smart traffic systems use IoT data to refine traffic flow, reducing congestion and improving air quality. However, with this data growth, concerns over privacy and security have also emerged. Businesses must ensure data protection while using these insights to make better, safer products.
Driving personalisation
Data analytics is key to understanding customer preferences, allowing brands to personalise their products and services. Streaming platforms like Netflix use data analytics to recommend shows based on viewing history, creating a unique experience for each user.
In retail, some popular brands offer personalised product suggestions by analysing customers’ past purchases and preferences, which boost satisfaction and loyalty. As personalisation becomes the norm, companies that use data to understand customer needs will gain a competitive edge.
Supporting initiatives
AI and data analytics are helping address large-scale environmental and urban challenges.
Data-driven climate models help scientists understand and predict climate change patterns. Companies like Google are using AI to improve the accuracy of climate forecasts, which is crucial for disaster preparedness and long-term planning. In urban settings, data analytics supports the development of “smart cities.”
For instance, Singapore uses data to monitor energy consumption and improve waste management, creating a cleaner, more efficient city environment.
Future of analytics
The next wave of analytics involves augmented analytics, Data-as-a-Service (DaaS), and synthetic data. Augmented analytics merges human expertise with AI, making it easier for non-experts to analyse complex data.
In the USA, Salesforce’s Einstein Analytics, for example, offers AI-powered insights that help sales teams make better decisions without needing advanced analytical skills.
DaaS provides businesses with on-demand access to data storage and analysis, often through a subscription model. This service allows small and medium businesses to harness big data without needing costly infrastructure. Additionally, generative AI is enabling the creation of synthetic data—fake data that mirrors real-world data. In sectors like healthcare, where data privacy is sensitive, synthetic data allows companies to train AI models without risking patient confidentiality.
Conclusion
AI and data analytics are transforming businesses and society. These trends show how companies can harness data to gain insights, improve customer experience, and tackle larger challenges.
However, the need for responsible and transparent practices would be crucial to ensure these technologies serve everyone fairly. As companies continue to embrace AI and analytics, those who prioritise ethical use, accessibility, and innovation will be best positioned to succeed in this evolving landscape.
Bangure is a filmmaker with a media degree. He has extensive experience in media production and management. He is a past chairperson of the NEC of the Printing, Packaging and Newspaper Industry. He is an enthusiast and scholar of artificial intelligence. — [email protected].
