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Different Types of Business Analytics – Definition, Applications & More

In the pulsating realm of business analytics, a symphony of innovation unfolds, showcasing a trinity of methodologies that sculpt the present and forecast the future. These three pillars, Descriptive, Predictive, and Prescriptive Analytics, serve as the navigational stars guiding modern enterprises through the cosmic sea of data. The BLS prophecy heralds an 11% surge in business analytics employment, a meteoric rise transcending temporal bounds from 2021 to 2031. 


This propulsion, swifter than the industry’s average, illuminates the escalating trend of flexibility in labour, an essence permeating the very fabric of 2023’s professional landscape. In this era where data isn’t just king but the entire kingdom, the applications of business analytics unfurl a tapestry of possibilities, painting a portrait of informed decisions, streamlined operations, and unprecedented growth.


Table of Contents:



What is Business Analytics?


Business analytics encompasses a range of methodologies and technological tools utilised to address business challenges by employing data analysis, statistical models, and quantitative approaches. This iterative and systematic process involves thoroughly delving into an organisation’s data, placing a strong focus on statistical analysis to steer decision-making.


Companies driven by data regard their data as a valuable asset, continuously seeking avenues to leverage it for a competitive edge. Achieving success in business analytics relies on the quality of available data, proficient analysts well-versed in both technology and business operations, and a dedicated commitment to utilising data for valuable insights that guide business strategies.


Why is Business Analytics Important?


Business analytics stands as a foundation for organisational success, playing a major role in informed decision-making and performance enhancement. By harnessing the power of data-driven insights, businesses can steer their operations towards efficiency, innovation, and strategic growth. The benefits of business analytics include:


  • Improved decision-making
  • Cost savings
  • Enhanced customer experience
  • Increased efficiency
  • Improved strategic planning


Application of Business Analytics


Business analytics serves as a cornerstone in contemporary business operations, offering a transformative impact across diverse sectors. Its applications span various industries, providing critical insights and strategic guidance in decision-making processes. Here are prominent sectors where business analytics plays a pivotal role:


  1. Banking

    Utilising credit and debit card data, business analytics extracts valuable insights into consumer spending habits, financial status, behavioural trends, and lifestyle preferences. This information aids in tailored customer services, risk assessment, and fraud detection.


  1. Customer Relationship Management (CRM)

    CRM systems leverage analytics to study demographics, buying patterns, and socio-economic information, facilitating personalised customer experiences and fostering long-term loyalty.


  1. Finance

    In the volatile financial landscape, business analytics assists in optimising budgeting, financial planning, forecasting, and portfolio management, providing insights crucial for informed decision-making.


  1. Human Resources

    Analysing data related to high-performing candidates helps HR departments in talent acquisition, retention strategies, and forecasting the best fit between candidates and the organisation’s culture.


  1. Manufacturing

    Business analysts analyse data to enhance operational efficiency by identifying factors impacting operations, such as equipment downtime, inventory levels, and maintenance costs, leading to streamlined inventory and supply chain management.


  1. Marketing

    Analysing marketing metrics, consumer behaviour, and market trends, business analytics guides effective advertising campaigns, social media strategies, and product preferences, optimising marketing efforts.


These applications often intersect, showcasing the interconnected nature of business analytics. By consolidating data-driven insights, various departments collaborate more effectively, aligning efforts towards common organisational objectives. Business analysts play a pivotal role in identifying areas for improvement and facilitating cohesive strategies that transcend departmental boundaries. Through business analytics, organisations navigate complexities, innovate, and strategise for sustained success in a dynamic business landscape.


Types of Business Analytics


The foremost categories of business analytics include descriptive, diagnostic, predictive, and prescriptive analytics. A recent addition, cognitive analytics, harnesses AI, ML, and deep learning. While each of these types of business analytics proves effective on its own, their combined application amplifies their effectiveness significantly.


  • Descriptive Analytics


Descriptive Analytics serves as a foundational tool in analysing historical data to comprehend how a unit responds across a defined set of variables. Its primary focus is on assessing key performance indicators (KPIs) to gain a comprehensive understanding of a business’s present condition.


This analytical approach unfolds through a structured process comprising five essential steps:


  • Defining Relevant Business Metrics: This initial step involves identifying and selecting the most pertinent metrics that align with the organisation’s objectives and goals.


  • Data Identification: Understanding the current state of the business requires pinpointing the necessary data sources vital for analysis.


  • Data Collection and Preparation: Once identified, the data undergoes a series of processes like de-duplication, transformation, and cleansing to ensure accuracy and consistency.


  • Pattern Analysis: This phase involves scrutinising the data for underlying patterns and trends, enabling the measurement of performance against predefined metrics.


  • Visualisation and Reporting: To facilitate comprehension for non-analytics professionals, the insights derived from the analysis are translated into visual representations such as charts and graphs.


Examples of Descriptive Analytics applications encompass a broad spectrum:


  • Summarising Past Events: Examining historical data to understand patterns and outcomes, aiding in informed decision-making based on past occurrences.
  • Exchange of Data and Social Media Usage: Analyzing trends in data exchange and social media interactions to gauge customer behaviour and market trends.


Therefore, descriptive Analytics is a fundamental tool that offers a comprehensive snapshot of a business’s current state, leveraging historical data to decipher patterns and trends critical for strategic decision-making. Its ability to distil complex information into understandable formats empowers businesses to take proactive steps towards growth and improvement.


  • Diagnostic Analytics


Diagnostic Analytics serves as a crucial facet within the realm of business analytics, delving deep into the ‘why’ behind past occurrences. Unlike Descriptive Analytics, which primarily captures the ‘what’ and ‘how’ of historical data, Diagnostic Analytics aims to uncover the underlying causes and drivers using techniques like drill-downs, data mining, discovery, and correlations.


This advanced analytical approach acts as a precursor to Descriptive Analytics, laying the groundwork to understand the rationale behind specific outcomes in diverse sectors such as finance, marketing, cybersecurity, and beyond.


Key components of Diagnostic Analytics include:


  • Drill-Downs: Delving into detailed data subsets to uncover specific insights and trends that contribute to particular outcomes.
  • Data Mining: Extracting patterns and relationships within large datasets to unveil hidden correlations and causal links.
  • Data Discovery: Exploring data through visualisations and interactive tools to identify unexpected trends or anomalies.
  • Correlations: Establishing connections between different variables to understand how changes in one factor affect others.


Examples of Diagnostic Analytics applications span various domains:


  • Examining Market Demand: Understanding the reasons behind fluctuations in market demand for products or services.
  • Identifying Technical Issues: Investigating the root causes of technical glitches or inefficiencies within systems or processes.
  • Explaining Customer Behavior: Unraveling the motivations behind customer actions and preferences to tailor better experiences.
  • Improving Organizational Culture: Analyzing internal data to comprehend factors influencing employee satisfaction and productivity.


Diagnostic Analytics stands as a vital tool in uncovering the deeper layers of data, offering invaluable insights that empower businesses to not only understand past occurrences but also to proactively address underlying issues and optimise strategies for future success.


  • Predictive Analytics


Predictive Analytics stands at the forefront of leveraging historical data to envisage potential future outcomes. This forward-looking analytical approach utilises a range of sophisticated techniques such as data mining, machine learning algorithms, and statistical modelling to forecast the probability of specific events.


The primary goal of Predictive Analytics is to provide insights that enable proactive decision-making and strategic planning across various facets of business operations. Its applications span a wide spectrum, including:


  • Enhanced Customer Service: By predicting customer preferences and behaviours, businesses can tailor their services to meet individual needs, fostering stronger relationships and loyalty.
  • Operational Efficiency: Anticipating future trends and demands aids in optimising business operations, ensuring resources are allocated efficiently to meet forthcoming needs.
  • Fraud Detection and Risk Management: Predictive models help identify potential fraudulent activities or assess risks, enabling timely intervention and mitigation strategies.
  • Profitable Customer Growth: By understanding customer responses and behaviours, businesses can focus efforts on nurturing relationships with the most profitable customer segments.


Examples of Predictive Analytics applications are diverse and impactful:


  • Predicting Customer Preferences: Foreseeing what products or services a customer might be interested in based on past behaviour and preferences.
  • Detection of Employee Intentions: Identifying factors that indicate the likelihood of employee turnover or commitment to the organisation.
  • Recommending Products: Suggesting products or services to customers based on their purchasing history and preferences.
  • Predicting Staff and Resource Needs: Forecasting staffing requirements and resource allocation based on anticipated demand.


In essence, Predictive Analytics empowers organisations to move beyond reactive strategies by harnessing the power of historical data to foresee potential outcomes. By doing so, businesses can make informed decisions, optimise operations, and capitalise on opportunities, positioning themselves strategically for future success.


  • Prescriptive Analytics


Prescriptive Analytics represents a transformative approach in the realm of data analysis, offering actionable recommendations based on historical performance to navigate future scenarios. This advanced analytical methodology harnesses a variety of tools, statistical techniques, and machine learning algorithms, utilising both internal and external data sources to generate informed suggestions.


At its core, Prescriptive Analytics dives beyond predictive insights, providing a comprehensive understanding of potential future occurrences, their timing, and underlying causes.


Examples of Prescriptive Analytics applications span various industries and domains:


Tracking Fluctuating Manufacturing Prices: By analysing past price fluctuations, Prescriptive Analytics assists in strategising to manage future price variations more effectively.


  • Improving Equipment Management: Leveraging historical data on equipment performance, this approach suggests optimal maintenance schedules and operational strategies to enhance equipment efficiency.


  • Suggesting the Best Course of Action: It offers tailored recommendations based on comprehensive data analysis to guide decision-makers towards the most advantageous actions.


  • Price Modeling: Predictive insights on pricing trends coupled with Prescriptive Analytics assist in devising optimal pricing models for products or services.


  • Evaluating Rates of Readmission: In healthcare, this analytics approach aids in predicting and mitigating instances of patient readmission by analysing historical patient data and suggesting preventive measures.


  • Identifying Testing Strategies: By examining past testing methodologies and outcomes, Prescriptive Analytics aids in designing efficient and effective testing strategies for various purposes.


Prescriptive Analytics acts as a proactive tool, enabling businesses to anticipate and prepare for future scenarios by harnessing the power of historical data. It doesn’t just stop at predicting outcomes; it guides decision-makers by recommending the best courses of action based on data-driven insights.


By providing insights into potential future events and their rationales, Prescriptive Analytics empowers organisations to optimise strategies, mitigate risks, and seize opportunities, ultimately leading to more informed and successful decision-making across diverse industries.


  • Cognitive Analytics


Cognitive Analytics emerges as a cutting-edge fusion of Artificial Intelligence (AI) and Data Analytics, representing a novel frontier in the realm of business analytics. This innovative approach transcends traditional analytical methods by leveraging AI-powered capabilities to derive insights and unearth optimal solutions from vast datasets.


At its core, Cognitive Analytics delves into the wealth of available data within knowledge bases, employing sophisticated techniques to discover optimal solutions for posed questions. It doesn’t just analyse structured data but extends its scope to tap into unstructured sources like images, text documents, emails, and social media posts.


The realm of Cognitive Analytics encompasses a diverse array of analytical techniques aimed at scrutinising extensive datasets and monitoring customer behaviour patterns and emerging trends. By integrating AI-driven capabilities, it not only interprets data but also learns from it, enabling a deeper understanding of complex information and uncovering previously unseen correlations.


Examples of Cognitive Analytics applications include:


  • Tapping Unstructured Data Sources: By harnessing AI algorithms, Cognitive Analytics unlocks insights from unstructured data sources like images, text documents, emails, and social media posts. It goes beyond traditional structured data analysis, offering a comprehensive understanding of diverse data formats.


  • Monitoring Customer Behavior and Emerging Trends: Cognitive Analytics excels in deciphering customer behaviour patterns and detecting emerging trends by analysing vast datasets. This insight aids businesses in making proactive decisions and staying ahead in dynamic market landscapes.


The integration of AI-driven cognitive capabilities in data analysis signifies a monumental shift, enabling businesses to extract invaluable insights from diverse and previously untapped data sources. By embracing Cognitive Analytics, organisations gain the ability to make data-informed decisions, predict trends, and derive actionable insights that drive innovation and strategic growth.


The Bottom Line


In the ever-evolving landscape of today’s business world, the demand for leveraging business analytics has become more pronounced than ever. At Hero Vired, embracing the power of business analytics isn’t just a choice but a strategic imperative. In a realm where data reigns supreme, the ability to harness insights from vast data sets can be the defining factor for success.


By opting for the Accelerator Program in Business Analytics and Data Science at Hero Vired,  you will not be merely adapting to the changing times; rather, you will be positioned at the forefront of innovation and strategic decision-making. The fusion of descriptive, diagnostic, predictive, prescriptive, and cognitive analytics empowers us to unlock unprecedented insights, enabling us to anticipate trends, optimise processes, and make informed, data-driven decisions.





The five types of business analytics are:
    • Descriptive Analytics 
    • Diagnostic Analytics
    • Predictive Analytics
    • Prescriptive Analytics
    • Cognitive analytics
Soft skills essential to data analytics, known as the five C's, encompass communication, collaboration, critical thinking, curiosity, and creativity, with many of these skills intertwined.
Prescriptive analytics stands out as the most crucial form of business analytics. It forecasts business occurrences and potential outcomes based on specific actions, yielding guidelines and suggesting actionable steps. Due to its ability to propose suitable actions, prescriptive analytics is favoured alongside cognitive analytics, which has revolutionised the industry, making both leading contenders in this field.
Cognitive Analytics mimics human intelligence in specific tasks by merging technologies like semantics, artificial intelligence algorithms, deep learning, and machine learning, learning from both data interactions and human inputs. In contrast, predictive analytics forecasts likely future occurrences, while prescriptive analytics recommends actionable steps to be taken.
  • Descriptive Analytics: highlights past events and occurrences within a business.
  • Predictive Analytics: forecasts potential future outcomes for a business.
  • Prescriptive Analytics: recommends optimal actions to be taken within a business.

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