What is Analytics? How is it Evolving in 2024?

Evolution of analytics

Analytics refers to the processes and techniques used to analyze data in order to draw insights, discover patterns, and make better business decisions. While analytics has been around for decades, advances in technology have enabled new innovations that are transforming the field. In 2023, analytics is more powerful and accessible than ever before.

A Brief History of Analytics

Analytics as a formal discipline emerged in the mid-20th century, though its origins stretch back centuries to early statistical analysis. In the 1950s and 1960s, analytics centered on statistical techniques like regression as well as quantitative methods from operations research. With the rise of computing in the 1970s and 1980s, analytics expanded to large databases stored on mainframes and early data warehouses.

Major milestones included the development of statistical analysis software like SAS and SPSS in the 1960s, the rise of data warehouses in the 1980s, and the emergence of visualization and business intelligence (BI) tools like Tableau in the 1990s and 2000s. The field rapidly evolved through the 2000s and 2010s with exponential growth of big data, artificial intelligence, cloud computing, and other transformative technologies.

Today, prescriptive and predictive analytics powered by machine learning are unlocking unprecedented business insights. According to IDC, the analytics and BI software market is predicted to grow from $215 billion in 2021 to over $420 billion by 2025.

Descriptive, Diagnostic, Predictive and Prescriptive Analytics

There are four main types of analytics, each providing deeper insights:

Descriptive analytics focuses on summarizing what happened in the past using reports, dashboards, and visualizations. This provides the foundation for all other analytics. Useful techniques include data aggregation, business metric monitoring, data mining, and exploratory data analysis.

Diagnostic analytics digs deeper into the data to understand why something happened. Statistical techniques like regression analysis, hypothesis testing, and drill-down querying reveal insights into causes and effects. Diagnostic analytics uncovers the drivers behind trends spotted in descriptive analytics.

Predictive analytics uses machine learning algorithms and advanced statistical models like neural networks, decision trees, and regression to forecast what could happen in the future based on current data. Predictive analytics is a fast-growing category, with revenue predicted to grow from $7.3 billion in 2019 to $23.2 billion by 2024. [1]

Prescriptive analytics takes it a step further by recommending what actions to take to achieve desired outcomes. It combines data, statistical models, and business rules to analyze possible decisions and potential influences. According to Gartner, over 50% of analytics insights will be prescriptive by 2023. [2]

Evolution of analytics

Trends and Innovations in Analytics

Analytics is rapidly evolving thanks to new technologies like artificial intelligence, machine learning and cloud computing. Here are some key trends shaping analytics in 2024:

  • Automated analytics powered by AI and machine learning makes insights more accessible for non-technical users through natural language query interfaces and voice-enabled analytics tools.

  • Real-time analytics leverages streaming data and fast in-memory databases for instant insights. This enables more agile decision making compared to traditional batch analytics.

  • Augmented analytics combines AI, ML and NLP to automate data preparation, model building, insight generation and more. According to Gartner, this can boost productivity of data scientists and analysts by up to 50%. [3]

  • Embedded analytics seamlessly integrates analytics directly into business applications and workflows for contextual insights. According to IDC, over 80% of new business applications will have embedded intelligence by 2023. [4]

  • Democratization of analytics makes tools and capabilities available to more roles through easy-to-use interfaces and self-service models. This breaks down silos and spreads data-driven decision making.

Analytics Use Cases Across Industries

Analytics is being applied in diverse and innovative ways across many industries:

  • Retail analytics is used for pricing optimization, inventory management, supply chain visibility, personalized marketing, and more. Companies like Walmart and Amazon rely heavily on analytics.

  • Healthcare organizations leverage analytics to improve patient outcomes, reduce costs, enhance research, and support public health. The healthcare analytics market is predicted to grow steadily from $18.7 billion in 2019 to $75.2 billion by 2027. [5]

  • Manufacturing uses predictive maintenance analytics to anticipate equipment failures, while also optimizing supply chain, production, logistics and more. According to IDC, manufacturers that use analytics achieve productivity gains of over 20%. [6]

  • Financial services firms apply analytics across applications like fraud detection, risk management, client sentiment analysis and product recommendations. Advanced machine learning techniques can uncover insights humans could easily miss.

Nearly every industry is tapping the potential of analytics to unlock data-driven competitive advantages. The cross-industry analytics market is forecast to grow from $58.5 billion in 2024 to $118 billion by 2026. [7]

Implementing an Analytics Strategy

For organizations looking to ramp up their use of analytics, here are some best practices:

  • Start with key business problems and questions. Maintain focus on driving impact vs. chasing technology trends.

  • Foster an analytics culture through training at all levels. Get leadership commitment to invest in people and processes.

  • Develop data literacy and analytics talent. Look beyond traditional statistics skills to emerging areas like ML engineering.

  • Choose flexible, scalable tools that align to your users, data, and evolving analytics maturity. Cloud-based platforms offer many advantages.

  • Prioritize data governance, security and quality from the start. Follow the maxim "garbage in, garbage out."

  • Start small, demonstrate quick wins, then expand into more advanced analytics as your capabilities mature. Don‘t boil the ocean early on.

The Future of Analytics is Bright

According to leading industry analysts, the continued growth of big data, artificial intelligence and cloud-based platforms will propel analytics to new heights. IDC predicts worldwide revenues for big data and analytics will reach $274.3 billion in 2024 and surge to $420 billion by 2025. [8]

Forrester foresees an analytics revolution with AI-driven insights providing the biggest competitive advantage. They advise that now is the time to embrace an analytics mindset. [9]

As technology makes advanced analytics accessible to more organizations, the future looks bright for harnessing data-driven insights to optimize decisions and performance. Virtually every business can benefit from getting on board with analytics.