Why AI and ML are transforming business analytics

Artificial intelligence (AI) is rapidly gaining widespread adoption across various applications, and its role in business and data analytics is becoming increasingly significant. The process of uncovering valuable insights and diving deeper into data is essential for extracting genuine business intelligence.

For instance, by posing specific questions such as “Why a particular month’s sales slump?” or “Where’s that user surge coming from and the reason behind it?” – Businesses can leverage AI chatbots to scan through datasets, identifying trends and correlations to provide comprehensive answers.

As per a Forrester report, companies with a robust AI strategy currently have a Chief AI Officer (CAIO) overseeing overall strategy, constituting 12 per cent of such companies. Looking forward, CAIOs are expected to be present on one out of every eight executive leadership teams, signalling a shift in AI leadership dynamics.

Today, sectors involved in data analysis can capitalise on the inventive uses of AI technologies and companies are actively seeking methods to secure a competitive advantage, with AI playing a pivotal role in this pursuit.

ML – a subset of AI and pathway to gain a competitive edge

Machine learning (ML) employs algorithms to enable computers to learn from data, expediting the analysis of extensive datasets for actionable insights. In business analytics, it is vital for predictive analytics, forecasting future trends, customer behaviours, and market dynamics from historical data, optimising resource allocation and marketing strategies.

It also plays a key role in customer segmentation, facilitating personalised marketing and satisfaction. Furthermore, ML is widely utilised in recommendation systems, employing natural language processing (NLP) for sentiment analysis and chatbots. It also enhances efficiency and profitability in business operations by optimising supply chains, pricing strategies, and resource allocation.

Ultimately, its role in modern business analytics is crucial, ensuring informed decisions through the analysis of historical and real-time data, and improving precision in tasks like forecasting, fraud detection, and quality control.

Automation of tasks leads to cost savings and a competitive edge, and its scalability fosters innovation in data analysis. Additionally, machine learning contributes to risk assessment in industries such as finance, cybersecurity, and healthcare, ultimately enhancing customer satisfaction and driving overall business success.

GenAI and its impact on business intelligence and applications

Generative AI (GenAI) is transforming business intelligence and data analytics through task automation, improved content creation, and heightened efficiency.

Data analytics addresses time-consuming tasks such as finding sources and consolidating information, benefiting industries with personalised experiences, streamlined data preparation, and advanced predictive analysis. For example, GenAI streamlines tasks such as finding data sources, consolidating excel files, and searching for relevant information, making advanced analytics accessible.

AI chatbots and GenAI simplify decision-making and data preparation, optimising processes and enhancing predictive analysis. In risk management, GenAI enables real-time monitoring, assists in risk identification and treatment, and provides simulations for proactive mitigation, particularly valuable in financial sectors for fraud detection and strategy testing.

Shift to cloud-based AI and analytics

Businesses are constantly feeling the competitive pressure to embrace AI and analytics opportunities. To effectively harness these opportunities, companies are encouraged to build a strategic, AI-enabled data platform with three core pillars. The first pillar involves creating a unified data foundation in the cloud, allowing seamless data integration from various sources.

The second pillar emphasises responsibly democratising data, ensuring accessibility and understanding for individuals without advanced technical skills. Lastly, the third pillar accelerates data value creation by streamlining data preparation processes using AI and analytics technology.

By utilising cloud-based tools to unify, democratise, and extract actionable insights from data, organisations can unlock endless possibilities for adding value.

The changing business landscape around us

AI and ML, now prominent with large language models, provide businesses with a competitive edge through enhanced intelligence, automating tasks for accurate predictions and streamlined decision-making. Intelligent models empower individuals with superpowers for intelligent choices, while manual methods risk obsolescence.

In sectors such as smart energy management, machine learning is vital for navigating extensive datasets, and contextualising information for decision-makers. In cybersecurity, AI identifies and prevents threats by monitoring data patterns.

Customer relationship management is transformed with personalised messages and deals, especially in finance. AI in internet research offers a customisable experience for small businesses, and digital personal assistants streamline internal operations, freeing up time for business growth.

In a nutshell, handling the vast and complex data generated by enterprises across industries becomes challenging for humans. Integrating artificial intelligence into business intelligence supports enterprise digital transformation.

Leveraging AI and ML in business intelligence enhances operational data utilisation and business intelligence offerings. The latest AI in analytics improves data handling and streamlines processes, empowering businesses to leverage vast amounts of data efficiently.

The increasing prevalence of AI in data analytics and its significance will continue to grow over time, owing to its advantages in speed, data validation, data democratisation, and automation. The future of AI in data analytics appears promising, with the ongoing development of numerous new tools and applications.

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The author is the systems engineering manager, Middle East at NETSCOUT.