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In the dynamic landscape of business intelligence, it is essential for organizations to stay ahead of the game if they want to make well-informed decisions and drive innovation. As we move into 2024, several transformative trends are changing the landscape of data analytics. Businesses are adopting advanced technologies such as continuous intelligence, data literacy, natural language processing (NLP), and predictive analytics to gain a competitive advantage. In this blog, we will explore these key trends that are shaping the future of business intelligence.

Continuous Intelligence (CI):

Continuous Intelligence (CI) is a significant trend that represents a shift in the way real-time analytics are integrated into business operations. Previously, analytical processes relied on predefined metrics tracked on schedules. However, CI uses a machine-driven approach to automate data extraction from diverse sources, providing a continuous flow of real-time insights. This allows organizations to move beyond static metrics and leverage CI to identify trends, growth opportunities, and anomalies that might remain hidden.

Real-time analysis is at the core of CI applications, with historical data complementing it to provide a complete 360-degree view. Automated data ingestion, streamlined data management, and advanced in-memory technology define CI tools, enabling organizations to optimize day-to-day operations and accelerate time-to-action in various scenarios.

Data Literacy

Data literacy is increasingly recognized as a vital component for creating data-driven cultures within businesses. By 2024, it is expected that data literacy will become essential for driving business value, as lack of data literacy poses a significant challenge for the Chief Data Officer’s office. As data continues to play a critical role in strategic decision-making, organizations are realizing the importance of providing every individual with the ability to efficiently understand, read, write, and communicate data. 

Gartner suggests implementing training programs and tools, beginning with identifying skilled data users who can act as mediators for non-skilled groups. The aim is to enable users at all levels to conduct advanced analysis, reducing dependence on data scientists and promoting a more collaborative and efficient data-driven environment.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a powerful branch of AI that is changing the way we analyze data. It is projected to grow from $3 billion in 2017 to $43 billion by 2025, making it a popular choice across various industries. NLP helps businesses to manage unstructured text data from sources such as emails, social media, and surveys. BI software providers are incorporating language insight features for applications such as BI data assistants and sentiment analysis.

Data assistants are similar to chatbots, and they allow users to ask questions in human language, receiving automated insights without complex calculations. Sentiment analysis helps businesses extract valuable insights from textual data, enhancing product development and brand positioning. NLP’s self-service approach in BI software empowers users to conduct efficient analyses without the need for intricate calculations.

Predictive & Prescriptive Analytics Tools

Predictive analytics is becoming increasingly prominent in the field of business analytics, with a focus on forecasting future probabilities by analyzing existing datasets. Predictive analytics is being used across various industries, from airlines optimizing ticket pricing to marketers predicting customer responses. The availability of predictive models is becoming a key factor for BI vendors and companies, leading to a new era of self-service analytical possibilities. Data scientists commonly use popular predictive analytics methods such as Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Averages (ARIMA). While ANN processes data by mimicking biological neurons, ARIMA is a time series analysis model that uses historical data to forecast future occurrences.

Prescriptive analytics takes the forecasting a step further, determining the decisions that should be made to achieve specific goals. Prescriptive analytics incorporates techniques such as graph analysis, simulation, complex event processing, and machine learning. This approach significantly improves decision-making by considering future outcomes in predictions, optimizing scheduling, production, inventory, and supply chain design.

Embedded Analytics

Embedded analytics is changing the way data analytics is integrated into users’ workflows. This trend involves incorporating BI components, such as dashboards or reports, into applications. By doing so, it enhances decision-making processes, leading to increased productivity. According to Allied Market Research, the embedded analytics market is expected to reach $77.52 billion by 2026. Companies are using embedded analytics to create sales reports, send dashboards to clients, and encourage collaboration among all stakeholders. 

This trend is particularly significant in the healthcare industry, where powerful healthcare analytics software can optimize processes, from clinical to operational and financial viewpoints. Embedded analytics is poised to become a standard in business operations, as organizations seek professional solutions for presenting data without building their software.

Augmented Analytics

Augmented analytics is a growing trend in data analytics that uses machine learning and AI to improve data preparation, insight generation, and sharing. Its main objective is to automate the process of identifying and communicating meaningful patterns within data, making analytics more accessible to non-technical users. 

Augmented analytics platforms simplify tasks such as data exploration, feature engineering, and model selection, allowing a broader audience within organisation to make data-driven decisions. 

Edge Analytics

Edge analytics is becoming more popular as businesses look to process data closer to where it is generated, which reduces latency and bandwidth requirements. Instead of relying on centralised cloud or data warehouse processing, edge analytics involves analysing data on devices or local servers at the edge of the network. 

This approach is particularly relevant in the context of the Internet of Things (IoT), where many devices generate data continuously. By analysing data at the edge, organisations can respond quickly to events, make faster decisions, and optimise data transfer and storage. The adoption of edge analytics is expected to increase with the growing deployment of IoT devices across various industries.

Automated Machine Learning (AutoML)

Automated Machine Learning (AutoML) is a solution that simplifies machine learning by automating the entire process of applying it to real-world problems. AutoML platforms intend to streamline tasks such as feature engineering, model selection, hyper parameter tuning, and deployment, which makes machine learning more accessible to users with varying levels of expertise. 

This trend is empowering organisations to gain valuable insights and predictions from their data without requiring a high level of expertise in data science and programming.

Bottom Line

A business intelligence analyst has to understand the rapidly evolving landscape in 2024, with organisation leveraging advanced technologies to enhance decision-making, optimise operations, and gain a competitive edge. From the automation of analytical processes to the integration of AI and machine learning, businesses are embracing these trends to unlock the full potential of their data. As we navigate the future, staying abreast of these trans formative trends is essential for organisations aiming to thrive in an increasingly data-driven world.

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