Category: Analytics

The digital age has ushered in an era of unprecedented data generation. From social media interactions and sensor readings to customer transactions and financial records, businesses are constantly accumulating vast amounts of information. This data, often referred to as “big data” due to its volume, velocity, and variety, holds immense potential for uncovering valuable insights and driving strategic decision-making.

However, harnessing the power of big data requires specialised techniques and a comprehensive understanding of different analytics approaches. Big data analytics empowers businesses to sift through this complex data landscape, transforming raw information into actionable intelligence. By leveraging various analytical tools, companies can gain a deeper understanding of customer behaviour, identify emerging trends, and optimise operations for improved efficiency and profitability.

In this article, I will delve into the different types of big data analytics, exploring their unique applications. This guide will also equip you with the knowledge to choose the right analytical approach for your specific needs, empowering you to unlock the hidden potential within your data and push businesses towards data-driven success.

The Different Types of Big Data Analytics

Let us learn about the different types of big data analytics as well as check out some of their applications and real-world examples. We will also explore some of the key types of data analysis techniques used for working with big data. These analysis techniques are various applications of data science types that are used in carrying out advanced analytics on big data.

Understanding the Past: Descriptive Analytics

Descriptive analytics forms the foundation of big data exploration. Its primary focus is summarising historical data to gain insights into past events and identify trends. Think of it as painting a clear picture of what has already transpired. Descriptive analytics relies on a variety of techniques to achieve this:

• Measures of Central Tendency: These metrics, like mean (average), median (middle value), and mode (most frequent value), provide a snapshot of the typical values within a dataset. For instance, a retail company might use the average sales figures over the past year to understand their baseline performance.
• Measures of Dispersion: Variance and standard deviation quantify the spread of data points around the central tendency. This helps identify outliers and assess the consistency of data. For example, analysing the standard deviation of daily website traffic allows an e-commerce platform to understand how much traffic fluctuates throughout the week.
• Data Visualisation: Charts and graphs bring data to life, making it easier to identify patterns and trends. Descriptive analytics utilises various visualisations like bar charts, pie charts, and line graphs to showcase key insights from the data. A social media company might use bar charts to visualise user engagement metrics like likes and shares across different social media platforms.

Real-World Example: Optimising Inventory Management with Descriptive Analytics

Take a large retail chain struggling with inventory management as an example. Using descriptive analytics, they can analyse historical sales data to identify peak sales seasons and products with higher demand. By calculating average sales figures and analysing seasonal trends, they can predict future demand and optimise inventory levels accordingly. This reduces the risk of stockouts during peak seasons and prevents excess inventory holding costs during slow periods.

Diagnosing the Root Cause: Diagnostic Analytics

While descriptive analytics paints a picture of the past, diagnostic analytics delves deeper, asking “why” things happened the way they did. This approach helps identify the root causes of problems or phenomena observed in the data.

Here are key techniques used in diagnostic analytics:

• Data Mining: This process uncovers hidden patterns and relationships within large datasets. Advanced algorithms sift through vast amounts of data to identify previously unknown correlations that might explain specific trends or issues. For example, a manufacturing company experiencing a rise in product defects could use data mining to identify correlations between specific machine operators, production times, or raw material batches and the occurrence of defects.
• Drill-Down Analysis: This technique involves progressively zooming in on specific data points or segments within a larger dataset. This allows for a more granular exploration of potential causes behind observed trends. Let us take a hospital analysing patient readmission rates as an example. Through accurate types of data analysis such as drill-down analysis, they can identify specific diagnoses or patient demographics with higher readmission rates, allowing them to investigate the underlying reasons.
• Anomaly Detection: This technique focuses on identifying data points that deviate significantly from the expected pattern. These anomalies can signal potential problems or areas requiring further investigation. For instance, a financial institution might use anomaly detection to identify unusual customer transactions that could be indicative of fraudulent activity.

Real-World Example: Predicting Machine Failures with Diagnostic Analytics

A manufacturing company experiencing frequent machine breakdowns can leverage diagnostic analytics to identify root causes and predict potential failures. By analysing sensor data from the machines, such as temperature readings and vibration patterns, data mining algorithms can uncover correlations between specific data points and machine failures. This allows for preventive maintenance scheduling, preventing costly downtime and ensuring smooth production operations.

Predicting the Future: Predictive Analytics

Moving beyond the past and present, predictive analytics takes centre stage. This powerful approach leverages historical data and statistical models to forecast future trends and events. Think of it as using the past to predict what is likely to happen next.

Here are some key techniques used in predictive analytics:

• Regression Analysis: This technique establishes a mathematical relationship between dependent variables (what we want to predict) and one or multiple independent variables (factors that influence the dependent variables). For instance, a marketing team might use regression analysis to predict future sales based on historical data points like advertising spend and product pricing.
• Time Series Forecasting: This technique analyses data collected over time to predict future values in a time series. It’s particularly useful for forecasting trends in areas like website traffic, stock prices, or customer demand. Think of an e-commerce platform using time series forecasting to predict upcoming sales peaks based on historical seasonal trends.
• Machine Learning Algorithms: These algorithms learn from existing data patterns to make predictions on new data. They are particularly powerful for complex data sets and can identify non-linear relationships that might be missed by traditional statistical methods. For example, a travel company might use machine learning algorithms to predict customer travel preferences and personalise travel recommendations based on past booking behaviour and demographic data.

Real-World Example: Assessing Creditworthiness with Predictive Analytics

Financial institutions rely heavily on predictive analytics to assess customer creditworthiness and identify potential loan defaults. By analysing a borrower’s credit history, income data, and other financial information, machine learning algorithms can predict the likelihood of a customer repaying a loan. This allows banks to make informed lending decisions and minimise the risk of bad debt.

Optimising Decisions: Prescriptive Analytics

Predictive analytics tells you what might happen, but prescriptive analytics goes a step further. This approach leverages data and potential future scenarios to suggest optimal courses of action. Think of it as using data-driven insights to determine the best way to move forward.

Here are some key techniques used in prescriptive analytics:

• Optimisation Algorithms: These algorithms identify the best solution within a defined set of constraints. They can be used to optimise logistics routes, resource allocation, or pricing strategies based on various factors and potential outcomes. For instance, a delivery company might use optimisation algorithms to find the most efficient delivery routes for drivers, considering factors like traffic patterns and delivery locations.
• Simulation Modeling: This technique involves creating a digital model of a real-world system. By simulating different scenarios and their potential outcomes, businesses can make informed decisions about resource allocation, marketing strategies, or product development. A pharmaceutical company using simulation modelling to test the potential effectiveness of a new drug before investing in large-scale clinical trials would be an example.
• Decision Trees: These are flowchart-like structures that represent a sequence of decision-making steps. Decision trees can be used to model complex decision-making processes and identify the optimal course of action based on different conditions. For instance, a retail company might use decision trees to personalise product recommendations for customers based on their past purchase history and browsing behaviour.

Real-World Example: Optimising Delivery Routes with Prescriptive Analytics

A transportation company struggling with inefficient delivery routes can leverage prescriptive analytics to optimise operations. By considering factors like traffic patterns, delivery locations, and driver availability, optimisation algorithms can suggest the most efficient routes for each delivery. This reduces delivery times, minimises fuel costs, and improves overall customer satisfaction.

Analysing in Real-Time: Real-Time Analytics

The world of big data is not static, information is constantly being generated. Real-time analytics allows businesses to analyse data streams as they are produced, enabling immediate insights and actions. Think of it as having a live feed of insights that can be used to react to situations in real time.

Here are some key techniques used in real-time analytics:

• Stream Processing: This technique involves processing data streams as they are generated, allowing for near-instantaneous analysis. This is crucial for applications like fraud detection or stock market analysis, where immediate action might be necessary.
• Apache Spark: This is a popular open-source framework for processing large datasets in real time. It allows businesses to analyse data streams efficiently and extract valuable insights quickly.
• Real-Time Dashboards: These dashboards provide a visual representation of key metrics and data trends as they occur. They enable real-time monitoring of critical business processes and facilitate data-driven decision-making at the moment. For example, a social media platform that uses real-time dashboards to monitor brand sentiment and identify potential PR crises as they unfold.

Real-World Example: Monitoring Brand Sentiment with Real-Time Analytics

A social media platform can leverage real-time analytics to monitor brand sentiment and identify potential PR crises. By analysing social media posts and online conversations as they occur, real-time sentiment analysis tools can detect negative brand mentions and assess the overall public perception of the brand. This allows for proactive social media engagement and rapid response to negative feedback, minimising reputational damage.

Exploring Advanced Analytics Techniques

The world of big data analytics is constantly evolving, with new techniques emerging to address increasingly complex data challenges. While the core functionalities explored above provide a solid foundation, venturing into advanced approaches can unlock even greater potential:

• Graph Analytics: This technique focuses on analysing relationships between data points within a network. It is particularly useful for social network analysis, fraud detection, and understanding complex system dynamics. Think of a social media platform using graph analytics to identify influential users within their network and optimise marketing campaigns accordingly.
• Natural Language Processing (NLP): This technique empowers machines to understand and process human language. It is another popular application of mainstream data science types. NLP allows businesses to extract insights from unstructured text data like social media posts, customer reviews, and even financial documents. For instance, a travel company might use NLP to analyse customer reviews and identify emerging travel trends or areas for improvement in their services.
• Big Data and AI: Big data analytics plays a crucial role in fueling Artificial Intelligence (AI) applications. Machine learning algorithms rely on vast amounts of data for training, and big data analytics helps provide the necessary fuel for AI to learn, evolve, and make increasingly accurate predictions. You can think of a self-driving car using big data analytics to analyse real-time traffic data and optimise its route for maximum efficiency and safety.

You can enrol in a solid data science course to learn everything you need to work with advanced data analytics.

The Future of Big Data Analytics

The big data analytics landscape is constantly in flux, with innovative techniques emerging to address the ever-growing complexity of data. While the core functionalities explored earlier provide a solid foundation, venturing into these new frontiers can unlock even greater potential.

One such advancement is graph analytics. This approach delves into the relationships between data points, treating information as interconnected nodes within a network. For example, in a social media platform, graph analytics can identify influential users and their connections, allowing for targeted marketing campaigns. It also proves valuable in fraud detection, uncovering hidden patterns within financial transactions that might indicate suspicious activity.

Another exciting frontier is natural language processing (NLP). Here, machines gain the ability to understand and process human language. This empowers businesses to extract valuable insights from unstructured text data like social media posts, customer reviews, and even emails. For example, a travel company could leverage NLP to analyse customer reviews and identify emerging travel trends or areas for improvement in their services.

Finally, big data analytics plays a critical role in the ever-evolving world of Artificial Intelligence (AI). Machine learning algorithms require massive datasets for training, and big data analytics steps in to provide the fuel. Think of a self-driving car where big data analytics processes real-time traffic data, allowing the car to optimise its route for efficiency and safety. By staying informed about these advancements and exploring techniques that align with their specific needs, businesses can unlock a treasure trove of knowledge from their data and gain a significant competitive edge.

Choosing the Right Types of Big Data Analytics

The different types of big data analytics allow us to utilise a solution for every challenge. However, the key to unlocking its true power lies in selecting the right tool for the job. By carefully evaluating these factors, you can choose the big data analytics technique that best equips you to tackle your specific business challenges and unlock the hidden potential within your data. Consider these factors when making your choice:

• Business Problem: Clearly define the issue you are trying to address. Are you looking to understand past trends (descriptive analytics) or predict future outcomes (predictive analytics)?
• Data Type: The nature of your data (structured, unstructured) will influence the appropriate technique. For instance, real-time analytics is ideal for analysing data streams, while NLP is best suited for extracting insights from text data.
• Available Resources: Consider the computational power and technical expertise required for different techniques. Some approaches, like machine learning, might require significant resources and specialised skills.

Wrapping Up

From understanding past trends with descriptive analytics to predicting future outcomes and optimising operations with advanced approaches, big data analytics empowers businesses to transform raw data into actionable intelligence. Remember, in today’s data-driven world, information is power. By leveraging big data analytics, businesses across all industries can gain a deeper understanding of their customers, optimise operations, and make data-driven decisions that propel them towards success. 

Also, by staying informed about these emerging trends and exploring advanced techniques that align with their specific needs, businesses can unlock even greater value from their data and gain a significant competitive edge in the ever-evolving big data landscape.

So, embark on your big data analytics journey today by enrolling in Imarticus Learning’s Postgraduate Program in Data Science and Analytics. With the help of this data science course, you will be able to explore the techniques that resonate with your specific needs and unlock the hidden potential within your data to gain a significant competitive edge. If you wish to know more about the core data analytics meaning, enrol today.

Frequently Asked Questions

1. What is the difference between descriptive and predictive analytics?

Descriptive analytics focuses on summarising past data to understand what happened. It uses techniques like calculating averages and creating charts to identify trends. Predictive analytics, on the other hand, leverages historical data and statistical models to forecast future events. It helps businesses anticipate what might happen and make informed decisions based on those predictions.

1. How can big data analytics help my business?

Big data analytics can benefit businesses in numerous ways. It can help you:

• Understand your customers: Analyse customer behaviour patterns and preferences to personalise marketing campaigns and improve product offerings.
• Optimise operations: Identify inefficiencies and bottlenecks in your processes to streamline operations and reduce costs.
• Make data-driven decisions: Gain insights from data to support strategic decision-making across all departments.
• Manage risk: Analyse data to identify potential problems and take proactive measures to mitigate them.

1. Is big data analytics too complex for my small business?

Not necessarily. While some advanced techniques require significant resources, there are many basic big data analytics tools available that are affordable and user-friendly for businesses of all sizes. You can start by focusing on techniques that address your most pressing business needs and gradually expand your analytics capabilities as you gain experience.

1. Where can I learn more about big data analytics?

There are many online data science courses available to help you learn more about big data analytics. You can enrol in Imarticus Learning’s Postgraduate Program in Data Science and Analytics to learn about big data analytics.

In today’s data-driven world, organisations are bombarded with information. But data itself is meaningless without the ability to transform it into actionable insights. This is where data analytics comes in. It is a process that takes raw data and extracts knowledge that can inform strategic decisions and drive business growth.

In this article, we will dive deep into the data analytics lifecycle, going beyond the surface level. We will explore the hidden gems within all the phases of data analysis lifecycle, offer practical tips and techniques, and showcase cutting-edge trends that will set your data analysis apart. By the end of this read, you will be equipped to transform raw data into actionable insights that unlock real business value.

Stage 1: Data Discovery

The data analytics lifecycle begins with data discovery, a critical stage that lays the foundation for your entire analysis. In this first phase of the life cycle of data analytics, we will move beyond simply defining the problem you are trying to solve. We will delve into techniques that help you uncover hidden data needs within your organisation and identify the most valuable data sources to address them.

Uncovering Hidden Needs

Data storytelling isn’t just about captivating presentations, it is a powerful tool for data discovery. By crafting narratives that showcase the potential of data to address business challenges, you can spark conversations and uncover hidden data needs across departments. User interviews also play a crucial role. Talking directly to stakeholders allows you to understand their specific challenges and information gaps, helping you identify data-driven solutions they might not have even considered.

Unconventional Data Sources

Traditionally, data analysis has relied on structured data stored in databases. But the world is brimming with unconventional data sources waiting to be explored. Sensor data from IoT devices can reveal valuable insights into equipment performance or customer behaviour patterns. Social media sentiment analysis can provide a real-time pulse on brand perception or identify emerging trends. By incorporating these unconventional sources, you can paint a more holistic picture and gain a deeper understanding of the business landscape.

Ensuring Data Integrity

Data cleaning is a vital first step, but true data quality goes beyond removing typos and missing values. Advanced data quality checks like anomaly detection can help identify outliers that might skew your analysis. Additionally, data bias is a growing concern. Techniques like bias identification can help you uncover potential biases within your data and develop strategies to mitigate their impact. By ensuring the quality and integrity of your data from the very beginning, you lay the groundwork for reliable and trustworthy insights.

Stage 2. Data Preparation

Data discovery has unearthed a treasure trove of information, but it likely exists in a raw and unrefined state. Data preparation is the stage where you transform this rough ore into a usable form, ready for analysis. Here, we will explore practical techniques and best practices to ensure your data is clean, consistent, and ready to yield valuable insights.

Wrangling the Data: Practical Tips and Tools

Data wrangling might sound like wrangling cattle, but it’s a crucial process for shaping your data into a usable format. Here are some practical tips and tools to help you navigate this stage:

• Data Transformation: This might involve converting data types (e.g., changing dates from text to a consistent format), standardising units of measurement, or creating new calculated fields based on existing data. Tools like spreadsheets or programming languages (Python, R) can be used for these tasks.
• Handling Missing Values: Missing data points can be a headache, but there are strategies to address them. Depending on the data and the analysis, you can choose to remove rows with missing values, impute missing values using statistical methods, or group similar data points together.
• Data Integration: Often, valuable insights lie at the intersection of data sets from different sources. Data integration tools can help you seamlessly merge data from various databases, spreadsheets, or even external APIs.

Version Control for Peace of Mind

Data is a living entity, and it’s likely to evolve throughout the analysis process. Data version control systems, similar to those used in software development, are essential for keeping track of changes made to your data. This ensures you can revert to previous versions if needed and facilitates collaboration within data teams, allowing everyone to work on the same, up-to-date version of the data.

Protecting Privacy With Anonymisation

In today’s data-driven world, privacy is paramount. When working with sensitive data, anonymisation techniques can help protect individual identities while still allowing you to extract valuable insights. Common anonymisation methods include removing personally identifiable information (PII) like names or addresses or using techniques like k-anonymity, which ensures there are at least k records with similar attributes for each data point.

Stage 3. Data Exploration and Modeling

With your data wrangled and ready, we now embark on a journey of discovery. This stage, data exploration and modelling, is where you delve into your data to unearth hidden patterns, identify key relationships, and ultimately build models that can make predictions or inform decisions. Here, we will explore techniques that go beyond the basics and delve into the heart of uncovering the secrets your data holds.

Exploratory Data Analysis (EDA)

Exploratory Data Analysis is often the first step in this phase. But we will move beyond simply calculating basic statistics and generating histograms. Techniques like dimensionality reduction can be immensely valuable, particularly when dealing with high-dimensional data. These techniques help identify underlying structures and reduce the number of variables without losing significant information. Additionally, advanced outlier visualisation methods can help you spot anomalies that might hold hidden insights or indicate potential data quality issues. By incorporating these advanced EDA techniques, you gain a deeper understanding of your data’s characteristics and relationships.

Feature Engineering

Think about building a house, you would not use random bricks, you would select and prepare the ones that best fit your design. Feature engineering plays a similar role in data modelling. Here, you identify the most relevant features (variables) from your data and potentially transform them to improve the performance of your model. Feature selection techniques help you choose the most informative features, while transformation techniques like scaling or encoding can ensure all features are on a similar scale and contribute equally to the model’s learning process. By carefully crafting your features, you lay the groundwork for building robust and accurate models.

Model Selection and Interpretability

Machine learning offers a vast array of models for different purposes. We will explore various models beyond the basic classification and regression algorithms. Decision trees, for example, can be powerful for understanding the logic behind a model’s predictions. Additionally, with the growing emphasis on explainability, interpretable models are gaining traction. These models allow you to understand not just what the model predicts, but also why it makes those predictions. This transparency is crucial for building trust in your models and ensuring they are aligned with ethical data usage principles.

Stage 4. Data Analysis and Interpretation

You have explored your data, unearthed hidden patterns, and built powerful models. Now comes the crucial step which is transforming these insights into actionable knowledge that can inform decisions and drive business value. In this stage of the life cycle of data analytics, we will delve into techniques for analysing and interpreting your data effectively, along with strategies for communicating your findings to a wider audience.

Hypothesis Testing: Beyond Textbooks, Real-World Applications

Statistical hypothesis testing is a cornerstone of data analysis, but textbooks often present it in a sterile, theoretical way. In the real world, hypothesis testing plays a vital role in answering critical business questions. Let us explore practical applications:

• Marketing Campaign Effectiveness: You might run an A/B test with two versions of a marketing email to see which one generates a higher click-through rate. Hypothesis testing can help you determine if the observed difference is statistically significant or simply due to random chance.
• Product Feature Optimisation: You might analyse user behaviour data to see if a new feature on your website is leading to increased engagement. Hypothesis testing can help you decide whether the observed change is a true effect of the feature or just a random fluctuation.

By understanding how to apply hypothesis testing in real-world scenarios, you can draw statistically sound conclusions from your data and make data-driven recommendations with confidence.

Data Visualisation: The Art of Storytelling with Data

Data visualisation is not just about creating charts and graphs, it is about storytelling. Advanced techniques like interactive dashboards allow users to explore your data dynamically, uncovering hidden trends and relationships on their own. Storytelling principles like using clear visuals, concise text, and a logical flow can help you effectively communicate complex insights to stakeholders, even those without a strong data background.

A/B Testing & Causal Inference: Going Beyond Correlation

While data analysis can identify correlations, it cannot always prove causation. A/B testing, as mentioned earlier, is a powerful tool for establishing causality in controlled settings. But what about observational data, where you cannot manipulate variables? Causal inference techniques can help you draw more robust conclusions from observational data by accounting for confounding factors that might be influencing the relationships you observe.

Stage 5. Communication and Action (Final Stage)

You have navigated the data science life cycle, transforming raw data into a treasure trove of actionable insights. But the journey does not end here. This final chapter of the data analytics lifecycle focuses on putting your insights to work, fostering a data-driven culture within your organisation, and ensuring responsible data usage practices.

Data Democratisation: Empowering Everyone with Data

Data should not be confined to the realm of data analysts. Data democratisation is the process of making data accessible and understandable to everyone in your organisation. Here are some strategies to empower non-technical stakeholders:

• Develop data literacy programs: Equip employees with the basic skills to understand data concepts, interpret visualisations, and ask data-driven questions.
• Create user-friendly data dashboards: Design interactive dashboards that allow users to explore data independently and discover insights relevant to their roles.
• Encourage data storytelling: Foster a culture where people translate data insights into clear, concise narratives that resonate with different audiences.

By empowering everyone with data, you unlock the collective intelligence of your organisation and unlock the full potential of your data analysis efforts.

Building a Data-Driven Culture: Decisions Rooted in Evidence

Data should not just be used for reactive problem-solving, it should be ingrained in your organisation’s DNA. Here is how to cultivate a data-driven culture:

• Lead by example: Senior leadership needs to champion data-driven decision-making and actively seek data insights to inform strategic choices.
• Integrate data into workflows: Embed data analysis practices into existing workflows across departments, ensuring data informs decisions at every level.
• Celebrate data-driven success stories: Recognise and reward instances where data analysis has led to positive outcomes, reinforcing the value of data-driven decision-making.

By fostering a data-driven culture, you create an environment where data is valued, trusted, and actively used to achieve organisational goals.

Ethical Considerations and Potential Biases: Responsible Data Usage

The power of data comes with a responsibility to use it ethically. Here are some key considerations:

• Data privacy: Ensure compliance with data privacy regulations and implement robust data security practices to protect user information.
• Algorithmic bias: Be aware of potential biases that might creep into data collection, analysis, or model development. Implement strategies to mitigate bias and ensure fair and equitable outcomes.
• Transparency and explainability: Strive for transparency in your data analysis process and build models that are interpretable. This fosters trust and allows stakeholders to understand the rationale behind data-driven decisions.

By prioritising ethical considerations and responsible data usage, you ensure your data analytics efforts not only drive business value but also operate within a framework of trust and fairness. You can enrol in a solid data analytics course so that you can learn all of the steps in more detail.

Wrapping Up

The data analytics lifecycle is not a linear process, but rather a continuous loop of exploration, refinement, and communication. As you gain experience with each stage, you will develop a keen eye for identifying hidden opportunities within your data and translating them into actionable insights.

Remember, data is a powerful asset, but its true value lies in its ability to drive informed decision-making. By mastering the data analytics lifecycle and embracing the hidden gems of insights within each phase, you can transform data into a competitive advantage for your organisation.

So, start your data exploration journey with Imarticus Learning’s Postgraduate Program in Data Science and Analytics so that you can unlock the power of data to achieve remarkable results. This data analytics course will teach you everything you need to know to become an expert data scientist or analyst.

Frequently Asked Questions

• I have a lot of data, but I do not know where to start. What is the first step in the data analytics lifecycle?

The first step is defining your goals. What questions are you trying to answer with your data? Once you know your objectives, you can determine the type of data you need to collect.

• Data cleaning sounds complicated. How important is it?

Data cleaning is crucial. Dirty data leads to inaccurate insights. Techniques like identifying and correcting errors, removing duplicates, and formatting inconsistencies ensure the quality of your analysis.

• What are some common data analysis techniques?

There are many, but some popular ones include descriptive analytics (summarising data), predictive analytics (forecasting future trends), and prescriptive analytics (recommending actions based on insights). The best technique depends on your specific goals.

• How can I turn data insights into actionable steps for my business?

Communication is key. Present your findings in a clear and concise way, highlighting actionable recommendations. Collaborate with stakeholders to translate insights into concrete strategies and implement changes to drive business value.

As competition increases in various industry sectors, more and more companies flock to use newer systems and technologies to gain a competitive advantage. One such technological solution which is proving itself is business analytics.

The scope of business analytics is wide. This article will discuss the different ways business analytics is helping transform different industries.

What is Business Analytics?

A primary question that comes to mind is what is business analytics? Business analytics is the process of using data, statistical analysis, and various methods to better understand business performance and make choices. It involves collecting, processing, and analysing massive amounts of data in order to derive relevant insights. These insights enable businesses to better their operations, strategy, and overall performance. To learn more about this topic, business analytics courses help a lot.

Key Steps in Business Analytics

With respect to the scope of business analytics in improving industries includes extracting meaningful insights from raw data. Business analytics includes these steps:

• Data Collection: Collecting information from a variety of sources, including sales records, customer interactions, social media, and more. This data can be either structured (like databases) or unstructured (like social media posts).
• Transforming Data: Sometimes the format of the raw data is of no use in business analytics. The format of the data needs to be changed in this scenario to better suit the analysis process. This can include finding out the summaries, sums, and averages or changing into entirely different data types.
• Data Processing: This step involves cleaning and organising collected data to ensure that it is accurate and useful. This may include deleting duplicates, correcting errors, and transforming data to a standard format.
• Statistical Analysis: Using statistical tools to examine data and uncover trends, patterns, and relationships. This may include techniques such as regression analysis, hypothesis testing, and clustering.

Types of Business Analytics

There are mainly 4 types of business analytics used in modern industry. They are namely predictive, prescriptive, descriptive and diagnostic. Here is an in-depth look at each of them.

• Descriptive Analytics: In this type of business analytics, data is processed to figure out patterns, trends and relationships.

• Predictive Analytics: In this type of analytics, past data is processed and transformed to predict future events. 

• Prescriptive Analytics: This type of business analytics deals with processing past data to find trends and find out how future problems can be subverted.
• Diagnostic Analytics: This involves processing data to figure out why an event took place in the past.

To learn more about business analytics, one can pursue a Senior Management in Business Analytics course among many other business analytics courses.

Scope of Business Analytics in Different Industries

The scope of business analytics is immense, it is changing the way companies operate and make choices in today’s data-driven world. Business analytics covers a wide range of industries, including finance, healthcare, retail, and manufacturing. Businesses gain insights by leveraging data and advanced analytical tools, which improve efficiency, optimise operations, improve customer experiences, and drive innovation. Here are some of the ways business analytics is helping various industries in 2024.

Manufacturing

The manufacturing industry was one of the first to see practical use cases of business analytics. The scope of business analytics in manufacturing includes increasing operational efficiency, cutting costs, and improving customer satisfaction. Let’s look at some of the key ways business analytics can be used in manufacturing.

• Supply Chain Optimisation: Business analytics analyse data from all stages of the supply chain, including suppliers, vendors, and customers. This information can be utilised to detect trends and patterns. as well as discover potential cost reductions and process improvements.
• Quality Control: Business analytics allows you to examine production process data and identify opportunities for improvement. This can assist companies enhance their product quality. This drives higher customer satisfaction and hence retention.
• Energy Management: Manufacturers also use business analytics to optimise energy use and expenses. Companies can develop more energy-efficient operations by assessing their energy consumption patterns and production schedules. This not only decreases the environmental impact but also cuts energy expenditures, which can be significant in manufacturing processes.
  

Healthcare

Business analytics in healthcare can help predict patient outcomes, personalise treatments, and improve care delivery by studying historical and real-time data.

The healthcare industry is integrating business analytics to improve save costs, and boost overall operational efficiencies. Here are some of the scope of business analytics in the healthcare industry:

• Improving Patient Outcomes: By studying patterns in patient data, doctors can anticipate unfavourable outcomes using business analytics, resulting in the quicker implementation of therapy which is often life-saving. This approach reduces probable health risks and enhances the quality of care.
• Improving Operational Efficiency: Hospitals and clinics use business analytics to optimise their day-to-day operations. Healthcare professionals and scheduling to inventory management can be improved using this approach. By studying usage patterns and patient movement, healthcare facilities can not only better allocate resources, but also make sure that they meet patient demands while also operating efficiently.
• Cost Reduction and Management: By analysing data on drug usage, treatment efficacy, and patient recovery rates, hospitals may optimise their spending and invest in treatments and equipment that provide the most value for money.
  

Education

The scope of business analytics is not limited to just the corporate industry. It has also extended itself to the education sector. 

Here are some of the use cases of business analytics in the education sector:

• Learning Management Systems (LMS) Analytics: LMS analytics is crucial to modern education, and business analytics plays an important role in optimising efficiency. LMS monitors student engagement, evaluates the efficacy of online content, and provides insights into how digital learning tools are used, resulting in more successful LMS systems.
• Curriculum Development: Business analytics can also be used for curriculum development. Educational institutions can identify areas of the curriculum that require improvement by analysing data on student performance across multiple topics, subjects and programmes. This results in more dynamic and relevant educational offers, better-preparing students for the demands of the modern industry.
• Predictive analytics for student success: Institutions can forecast student outcomes using past data, allowing for early intervention for students who are not showing signs of passing a class. This approach can greatly improve graduation rates and academic success.
  

Finance

Business analytics is critical, especially in the banking industry. This industry requires precision, foresight, and compliance. Financial institutions may make informed decisions about risk management, operational efficiency, and client engagement. These informed decisions are formed by leveraging massive amounts of data from transactions, market movements, consumer behaviour, and economic indicators. 

Here are some of the ways business analytics has helped the finance industry:

• Risk Management: One of the most important uses of business analytics in finance is risk management. Financial institutions use sophisticated analytical models to assess and reduce the risks associated with loans, investments, and financial markets. These models can forecast probable defaults, market swings, and other financial hazards by assessing historical data and current market trends, allowing businesses to take proactive steps.
• Fraud Detection: Another important application is fraud detection. Business analytics technologies, using machine learning algorithms and pattern recognition, can detect odd transactions that may suggest fraudulent activity. This allows for quicker reactions and decreases the financial losses associated with fraud.
  

E-Commerce

The global e-commerce market is estimated to be valued at $6.3 trillion by 2024, up from $5.8 trillion in 2023. E-commerce is one of the most competitive businesses due to its continual growth. 

Thus, E-commerce businesses are continuously trying to outperform each other using new business analytics trends. Here are some of the ways business analytics is helping the E-commerce industry:

• Personalisation of Customer Experiences: The use of business analytics in e-commerce enables the development of highly personalised purchasing experiences. Companies can personalise product recommendations, marketing messaging, and promotional offers by evaluating customer behaviour, purchase history, and preferences. This level of customisation not only improves the consumer experience but also increases sales and loyalty.
• Innovative Marketing Campaign: The application of business analytics is critical to the success of e-commerce marketing campaigns. Businesses may optimise their advertising expenditure, focus on high-performing channels, and increase the overall ROI of their marketing efforts by reviewing the effectiveness of previous campaigns across several channels.
  

Real Estate

The scope of business analytics in the real estate industry is wide. Real estate brokers leverage data analysis for tasks like precise property valuation and uncovering current market trends. By analysing past and present patterns, skilled brokers can forecast future market conditions, ultimately empowering them to provide exceptional service to their clients.

Here is an in-depth look at some ways business analytics helps the real estate industry:

• Lead Generation and Conversion: Real estate companies use business analytics to improve their lead generation and conversion strategies. Companies can better understand what draws customers by examining data from multiple sources such as websites, social media and online adverts.
• More accurate projection of property values: A fair price is an immensely important affair when it comes to both buying and selling properties in real estate. Sellers rely on their broker to sell their property for the greatest possible price in the shortest amount of time. The days of guesswork in real estate pricing are gone. Today, brokers have a wealth of comparable data at their fingertips. This allows them to analyse past sales of similar properties, considering factors like age, condition, amenities and accessibility. This data-driven approach leads to more realistic listing prices, benefiting both sellers and buyers.
  

Energy and Utility Production

Business analytics is very important in the energy and utility production industry. It ranges from using predictive models to predict demand. It is also used to forecast weather so that greener energy production alternatives can be used. 

Here is a closer look at the points. 

• Forecasting Energy Demand: Energy production companies can very accurately predict future energy demand by examining previous consumption data, weather patterns and economic indicators. This forecasting aids in planning generation and distribution to avoid shortages or overproduction, which is especially important in regions that rely heavily on renewable resources such as solar and wind. This also helps in cost-cutting.
• Renewable Energy Integration: Analytics play a huge role in the renewable energy sector. Because renewable sources depend on the weather, it is most important to weather forecast to figure out how much electricity can be produced.
  

Transportation

The scope of business analytics is not utilised fully in the transportation industry. First-world country governments use business analytics to understand people’s needs, transform cities into smart cities using IoT, control traffic, optimise route planning and intelligent transportation systems, and manage congestion. Here are some ways business analytics is helping the transportation industry:

• Route Optimisation: One of the most common applications of business analytics in transportation is route optimisation. Analytics software can calculate the most efficient delivery and transportation routes by assessing traffic data, weather conditions, vehicle conditions, and real-time updates. This minimises fuel use, saves time, and improves overall transportation efficiency.
• Managing Vehicle Health: Business analytics is essential for managing a group of cars. It can get very difficult to check up on individual cars personally so business analytics is used to monitor vehicle health, track maintenance schedules, and predict breakdowns before they happen.
  

Insurance

Many insurance companies provide a wide range of insurance products, including coverage for cars, property, health, and long-term care. Each has a unique risk profile. Regardless of the products they offer, many insurance businesses aim to decrease costs, enhance profits, and foster long-term customer relationships. 

Many rely on data-driven predictive analysis to make effective business decisions that support these objectives. Here are some of the ways business analytics helps this industry: 

• Mitigate Fraud: Business analytics make it easier to identify trends, whereas advanced analytics and predictive modelling use data like past claims and externally gained data like credit ratings to flag claims that are likely to be fraudulent.
  

Organic Lead Generation: Business analytics provides marketers and insurers with a comprehensive knowledge of their Customer Acquisition Cost (CAC), which shows how much is spent on organic lead generation and third-party suppliers. The final result is that it provides a clear picture of where companies should focus their resources.

Entertainment and Media

The global pandemic has changed how people consume media and entertainment. More and more people are turning to OTT platforms for rich media content. Business analytics has one very effective use case in this industry: 

• Effective Strategy Development: Business analytics helps decision-making and strategy development in the entertainment industry. Data-driven insights allow analysts to recognise emerging trends, estimate market demand, and make informed decisions about content creation, distribution, and investment. Data analytics is also useful for optimising advertising campaigns, identifying target audiences, and assessing the effectiveness of promotional activities. 

In Conclusion

The business analytics market is growing at an exponential rate. With more and more industry applications of business analytics, the demand for business analysts is greater than ever. The scope of business analytics is only going to get wider from here. This will create more jobs in this field. 

If you want to get into this field and become a business analyst yourself, you can pursue a Senior Management in Business Analytics course by Imarticus. Completing a certified course will surely clear your concepts. This course will provide you with all the skills you will need to work in this sector. 

• What are some surprising ways business analytics is impacting industries?

Business analytics has gone beyond traditional number-crunching. Industries like healthcare are using data to predict patient outcomes, manufacturers are using it to optimise production lines and there are many more examples such as these.

• How can I upskill in business analytics?

You can go for specific courses or certifications that can help you develop your business analytics skills. Imarticus Learning offers many online courses and certification programs to get you started.

• Is business analytics relevant to my industry?

Business analytics is becoming increasingly relevant across almost all industries. Even if your specific role isn’t directly involved in data analysis, understanding how data can be used can give you a significant edge.

• Are these changes mostly happening in tech companies, or are they relevant to more traditional businesses too?

Business analytics is revolutionising both tech and traditional businesses. While some applications might be more cutting-edge in tech, the core principles of using data to make informed decisions are valuable for any organisation. The report likely explores how traditional industries like retail or manufacturing are leveraging data analytics to improve efficiency, understand customer behaviour, and gain a competitive edge.

Whenever looking for a job, one of the first thoughts that strikes an individual is “What will be my pay scale?” As a business analyst, there are a lot of opportunities available to an individual as of 2024 because this job role is quite in demand.

As per a report published by the US Bureau of Labor Statistics projects, business analytics salary will experience a steep growth of somewhere around 10% between the years 2022 and 2032. This will also make the profession of business analytics, grow faster and steeper than any other profession for that matter. Business analysts are in demand already, and it is expected to experience more growth in the coming years.

The COVID-19 pandemic has acted as a catalyst for the digital revolution and has acted as a mean factor for the demand of business analysts. However, there is a lack of analytically-driven individuals, and hence, business analytics job salary have scaled greater heights. it acts as an incentive to draw competent and skilled personnel towards a company.

Factors Affecting Business Analyst Salary

Business analysts have to perform various and diverse functions within an organisation. Business analytics salary depends upon various factors. The following are the determinant factors of business analytics job salary:

• Location: business analytics careers are a wide domain and their salaries highly depend upon the job location one chooses to work in. For example, a person who is a business analyst in Bangalore city will have a higher pay scale than a person working in the same job role in Thiruvananthapuram. It is decided by the cost of living index and the popularity of the job role in a particular city.
• Specific role: business analytics salary also differs based on the job title or job role of an individual. A person who is a business analyst in finance, IT or marketing will have a higher pay scale compared to a business analyst in the sales or technical department.
• Skillset: Skillset makes a huge difference in the salaries of individuals for a job role. A business analyst having varied and diverse skills with effective performance will be paid a higher salary. On the other hand, business analysts possessing limited skills will be paid considerably less.
• Experience: One of the most important determining factors of business analytics salary is the amount of experience an individual possesses. An inexperienced person will earn relatively less than a person who has a considerable amount of experience in this field.
• Industry/Sector: another significant factor is the sector in which an individual is working. A business analyst working in the IT sector will have a higher pay scale than a business analyst working in the healthcare sector. The rise in technology has prioritised the technological sector, enabling the business analyst of this industry to be more in demand than those of the sales, and consultancy sectors.
• Certifications/Credentials: Accredited certifications in business analysis are a powerful way to stand out in a competitive field. These programs not only enhance your knowledge and skillset but also demonstrate your commitment to the profession, potentially leading to higher earning potential and faster career advancement compared to uncertified peers.

Business Analytics Salary in 2024

Business analysts are critical for organisations of all sizes, making them a sought-after skillset worldwide. Their dynamic roles and the impact they bring command lucrative salaries. However, business analytics salary vary depending on various factors such as skills, certifications, location and experience.

Business Analyst Salary: Based on Experience

One major parameter affecting a business analyst’s salary is their experience. Candidates with more experience are offered a higher salary. Given below is the salary structure of business analysts, which varies based on their experience.

• Beginner

The average junior business analytics salary in India is INR 5 lakhs per annum. If someone wishes to upskill themselves in this domain, they can enrol for the Senior Management Programme in Business Analytics, IIM Calcutta by Imarticus and explore the advantages of this course.

• Intermediate

The average salary of an intermediate-level business analyst in India with almost 5 years of experience is INR 12.7 lakhs annually.

• Advanced

The average salary of a senior business analyst with an immense amount of experience for more than a decade in India is INR 18.3 lakhs annually.

Business Analytics Salary: Based on Location

Another main factor that affects business analytics jobs salary is the location of the job. Here is a comparison of the salaries of business analysts based in different countries and cities.

• India

Business analytics salary in India vary based on which city or state in India one is working in. In the table given below, the salaries of business analytics professionals working in different cities in India are mentioned:

City	Average Salary
New Delhi	INR 4.5 lakhs per annum
Mumbai	INR 4.3 lakhs per annum
Pune	INR 4.7 lakhs per annum
Kolkata	INR 4 lakhs per annum
Hyderabad	INR 4.2 lakhs per annum
Noida	INR 5.3 lakhs per annum

• USA

Different cities in the United States have varied salaries for business analytics careers. The most popular cities with this job role are stated as follows:

City	Average Salary
Plato	USD 94,715 per annum
Tampa	USD 93,967 per annum
Chicago	USD 86,593 per annum
Phoenix	USD 85,908 per annum
Dallas	USD 79,289 per annum
New York	USD 99,444 per annum

• United Kingdom

Here are the salaries in the United Kingdom depending on the cities:

City	Average Salary
London	GBP 53,941 per annum
Westminster	GBP 45,570 per annum
Richmond upon Thames	GBP 36,822 per annum
Uxbridge	GBP 42,460 per annum

• Australia

The business analytics salary is based on which city in Australia they work in. The table shows the salaries of the top cities:

City	Average Salary
Melbourne	AUD 1,05,260 per annum
Sydney	AUD 1,04,626 per annum
Brisbane	AUD 1,06,850 per annum

• Canada

Different cities in Canada have different salaries for business analytics experts. The salaries of different cities have been elucidated below in the mentioned:

City	Average Salary
Montreal	CAD 82,544 per annum
Toronto	CAD 74,648 per annum
Ottawa	CAD 77,535 per annum

• Singapore

AWS security experts are paid differently in different cities in Singapore. The following table shows the salaries of the top cities:

City	Average Salary
Woodlands	SGD 82,358 per annum
Bedok	SGD 75,049 per annum
Outram	SGD 80,561 per annum

Business Analyst Salary: Based on Industry

Business analytics careers differ in job roles based on industries. Hence, business analytics salary are also different in several sectors. The sector which is booming offers higher packages to its employees when compared to the industries facing stagnant and low returns.

Salaries of business analysts based on various industries are given in the table:

Industry	Average Salary
IT services and consulting	INR 9.5 lakhs per annum
financial services	INR 10.6 lakhs per annum
hardware and networking	INR 9.1 lakhs per annum
engineering and construction	INR 10.1 lakhs per annum
fashion and textile	INR 11 lakhs per annum
investment banking, venture capital, private equity	INR 12.3 lakhs per annum
oil, gas, petrochemicals	INR 23 lakhs per annum
marketing and advertising	INR 7.5 lakhs per annum
sports and recreation	INR 10.1 lakhs per annum
banking, insurance, accounting	INR 11.6 lakhs per annum

Business Analyst Salary: Based on Job Title

A business analyst has to perform various operations, and it is not possible for one single person to be equipped with the skills that are required in every sector. That is why various designations or job titles have been assigned to the individuals working as business analysts. 

Business analytics salary varies based on the job title as well. Summary of salaries on the basis of job titles have been enumerated as follows:

Job Title	Average Salary
business analyst in data science and analytics	INR 9.7 lakhs per annum
business analyst in project and program management	INR 10.7 lakhs per annum
business analyst in product management	INR 11.1 lakhs per annum
legal and regulatory business analyst	INR 7 lakhs per annum
media production, and entertainment business analyst	INR 4.2 lakhs per annum
business analyst in IT and information security	INR 10.5 lakhs per annum
procurement and supply chain business analyst	INR 9 lakhs per annum
business analyst in content, editorial and journalism	INR 8.6 lakhs per annum
business analyst in UX design and architecture	INR 6.3 lakhs per annum
business analyst in customer success, service, and operations	INR 9.9 lakhs per annum

What Can You Do To Increase Your Salary?

Business analysts are responsible for managing dynamic tasks, which require various types of hard and soft skills. The ever-evolving digital era makes the role of business analysts even more challenging as they need to match the industry demands now and then.

However, one can enrol for business analytics courses in order to improve their skills, which will ultimately result in a salary hike. 

Individuals can consider the following tips which will help them to elevate their annual salary packages:

• Gain advanced degrees such as master’s and PhD and earn a specialisation in a particular field.
• Take up effective certification courses that are beneficial for improving one’s skill set.
• Seek an opportunity where one can take up managerial and leadership positions that can validate their expertise and result in increased income.
• One may explore the booming industries in order to secure a high salary package.

Future Trend

The LinkedIn 2021 Report for Jobs On The Rise reflected that there has been a great demand for job positions involving digital skills, and there is no sign for them to go down. The report shows that there will be more than 150 million job opportunities in the tech field in the world in the next five years. The field of business analytics is predicted to grow at the rate of 5.9% in the next decade as stated in the report published by Emni Burning Glass.

And the market for business analysts is expected to grow enormously in the coming years. It is the best opportunity for the younger generation to take up business analytics as a profession. This job will provide great exposure to the individuals and will also fetch them lucrative salaries. 

The market for business analytics will increase and so does the investment in the sector. It is the ideal time for individuals to market themselves to employers and to secure a decent salary package.

Conclusion

In the rapidly evolving technological era, business analytics is considered to be a very important job role and every organisation needs efficient business analytics professionals in each department. Hence, this is a booming job role offering lucrative packages to individuals.

If you have a knack for the tech industry and have considerable knowledge about business operations, then this can be the right job role for you. To enhance your knowledge and skills, you can enrol on various business analytics courses. You may sign up for the Senior Management Programme in Business Analytics, IIM Calcutta by Imarticus. This will help you to gain hands-on experience, and you can thrive as a business analyst in the competitive corporate world.

Frequently Asked Questions

• What is the salary of a business analyst in future?

Business analytics salary will only grow in the near future. At present, it shows an estimate of INR 10.5 lakhs annual pay in 2024, in India.

• What is the salary of a business analyst with four years of experience in India?

An intermediate-level business analyst who has an experience of four years can earn up to 9.5 lakhs to 10 lakhs in a year.

• What is the highest salary of a business analyst in India?

The salary of a business analyst ranges from 6 lakhs to 15 lakhs per year in India. However, an experienced business analyst may earn more than 15 lakhs annually.

• Is business analyst a high-paying job?

Business analysts are paid a higher salary than professionals in other business roles. It is a high-paying job and is expected to grow more in future.

The latest developments and trends in the field of big data technologies, focuses on gathering and analysing data for decision-making, and an increased dependence on machine learning (ML) are propelling the global market for Data Science platforms. As technology progresses and data becomes more available, the future prospects of the data science field appear promising. 

According to the Analytics Insight report, by mid-2025, India will experience a massive employment growth in the realm of data science. It is predicted that there would be around 1,37,630 data science jobs in India at that time. In the first half of 2021, the Indian job market saw a surge in demand for data scientists. Data science jobs for freshers in this industry can be extremely rewarding, which can provide an intellectually challenging and stimulating environment.

So, with a focus on providing applicants with an integrated learning experience, Imarticus Learning, the leading professional education provider for new-age leaders, is offering a postgraduate program in Data Science and Data Analytics. With its distinctive career support services, it continues to assist students as they take a new approach towards data science jobs. You may have access to over 500 elite placement partners and uncover numerous possibilities for yourself.

What are the Latest Trends in Data Science?

Professionals in this sector must remain up to date on the newest trends because it is an ever-changing field. Some of the prominent trends are:

• Automated Machine Learning tools 
• Artificial Intelligence (AI) and Machine Learning (ML) integration
• Natural Language Processing or NLP
• Explainable AI and interpretability
• Ethical considerations in data science
• IoT analytics and edge computing 
• Cloud-based data science platforms
• Big data processing and analytics

Most Aspiring and Lucrative Jobs in the Data Science Domain

To find the right job, you must target your job title and check the job descriptions before applying, so that the job profile aligns with your goals. Here is an assorted list of the most qualified job profiles in the Data Science field:

• Data Analyst 
• Data Scientist
• ML Engineer
• ML Scientist
• Data Engineer
• AI Engineer
• Marketing Data Analyst
• Quantitative Analyst
• Database Manager
• Database Administrator
• Data Architect
• Data Warehouse Manager
• Database Developers
• Applications Architect
• Infrastructure Architect
• Enterprise Architect 
• Statistician
• Business Intelligence Analyst

Strategy to Secure a Job in Data Science

Ensuring a secure position in data science requires an excellent foundation of education, skill development, and hands-on experience. Here are a few ways to get started:

• Getting a degree in respective discipline such as data science, statistics or computer science.
• Learning the concepts of statistics, maths machine learning, coding, databases and big data in depth. 
• Acquiring proficiency in important programming languages such as Python and R.
• Practising data analysis and visualisation using tools such as Excel, SQL and Tableau.
• Creating a portfolio of data science projects to demonstrate the expertise.
• Pursuing certifications or advanced degrees to enhance the knowledge and credibility in the field.
• Staying updated with fast-paced industry trends and continuously learning new techniques.
• Engaging with the community and meeting fellow data scientists.

Final Words

A data science and analytics course will not only teach participants the skills they need to become modern-day data analysts, but it will also help land data science jobs for freshers as a Data Scientist, Data Engineer, Business Analyst, Machine Learning Engineer, Tableau Developer, Business Intelligence Specialist and other positions.

This six-month Data Science course from Imarticus offers an innovative educational curriculum that covers both fundamental and advanced Data Science and Analytics disciplines. Aspirants may learn how to use the practical applications of Data Science, Data Analytics, Power BI, Python, SQL and Tableau while developing competence in these areas.

Data analytics is constantly evolving, almost all manual repetitive tasks are automated, and some are complex. If you are in the profession of big data, a data scientist, or from the field of machine learning, understanding the functions of these algorithms would be of great advantage.

Continuing the earlier blog, below are a few popular data analytics algorithms commonly used by data scientists and machine learning enthusiasts. The headings might differ slightly in terms of the terminology of the algorithms, but here we have tried to capture the essence of the model and technique. To excel in the field of data analytics, one can consider enroling into a data analytics course that can equip individuals with in-demand skills and enhance their career prospects.

Linear Regression

Imagine you have many logs to stack together from the lightest to the heaviest, however, you cannot weigh each log, you need to do this based on the appearance, height, and circumference of the log. Only using the parameters of the visual analysis should you arrange them. In other words, Linear Regression establishes a relationship between independent and dependent variables by arranging them into a line. Another example would be modelling the BMI of individuals using weight. You should use linear regression if there is a possible relationship or some sort of association between variables, if not, then applying this data analytics algorithm will not provide a useful model. 

Logistic Regression

Like any other regression, logistic regression is a technique to find an association between a definite set of input variables and an output variable. But in this case, the output variable would be a binary outcome, i.e., 0/1, Yes/No, e.g., if you want to assess whether there will be traffic at Colaba, the output will be a specific Yes or No. The probability of traffic jams in Colaba will be dependent on time, day, week, season, etc., through this technique, you can find the best fitting model that will help you understand the relationship between independent attributes and traffic jams, incidence rates, and the likelihood of an actual jam.

Clustering

This is an unsupervised learning data analytics algorithm where a data set is clustered into unique groups. So, if you have a database of 100 customers, you can internally group them into different clusters or segments based on variables. Suppose it’s a customer database that you are working on. In that case, you can cluster them based on gender, demographics, purchasing behaviour, etc. This is unsupervised, as the outcome is unknown to the analyst. The algorithm decides the outcome, and an analyst does not train the algorithm on any past input. There is no right or wrong solution in this technique, business usability decides the best solution. There are two types of clustering techniques, Hierarchical, and Partitional. Some also refer to clustering as Unsupervised Classification.

Decision Trees

As the name suggests, decision trees represent a tree-shaped visual, which one can use to reach a desired or a particular decision by simply laying down all possible routes and their consequences or occurrences. Like a flow chart for every action, one can interpret the reaction to selecting the option.

K-Nearest Neighbors

This data analytics algorithm is used to solve classification problems, although it can also be used to solve regression problems. This algorithm is very simple. It stores all available cases and then classifies any new cases by taking a vote from its K-neighbours. The new case is assigned to the class with the most common attributes. An analogy to understand this would be the background checks performed on individuals to gather relevant information.

PCA

The main objective of the Principal Component Analysis is to analyse the data to identify patterns and find patterns, to reduce the dimensions of the dataset with minimal loss of information. The aim is to detect the correlation between variables. This linear transformation technique is common and used in numerous applications, like in stock market predictions. 

Random Forest

In the random forest, there is a collection of decision trees, hence the term ‘Forest’. Here, to classify a new object based on attributes, each tree gives a classification, and that tree votes for that class. And overall the forest chooses the classification having the most votes, so in the true sense, every tree votes for a classification.

Time Series / Sequencing

Time series is a data analytics algorithm that provides regression algorithms that are further optimized for forecasting continuous values, like for example, the product sales report, over time. This model can predict trends based on the original dataset used to create the model. To add new data to the model, you must make a prediction and automatically integrate the new data into the trend analysis.

Text Mining

The objective of the text mining data analytics algorithm is to derive high-quality information from the text. It is a broad term covering various techniques to extract information from unstructured data. Many text mining algorithms are available to choose from based on the requirements. For example, first is the Named Entity Recognition, where you have the Rule-Based Approach and the Statistical Learning Approach. Second is the Relation Extraction, which has the Feature Based Classification, Kernel Method.

ANOVA

One-Way-Analysis of Variance is used to analyse if the mean of more than two dataset groups is significantly different from each other. For example, suppose a marketing campaign is rolled out in 5 different groups where an equal number of customers are present within the same group. In that case, the campaign manager needs to know how differently the customer sets are responding so that they can make amends and optimise the intervention by creating the right campaign. The Analysis Of Variance works by analysing the variance between the group to variance within the group.

Optimise your knowledge by understanding these data science algorithms intensely to flourish in data science by applying for a dedicated data science course.

Frequently Asked Questions

What problem do data scientists solve?

Data scientists are crucial in addressing real-world challenges across diverse sectors and industries. In healthcare, their expertise is harnessed to create tailored medical solutions, enhance patient results, and cut healthcare expenses. This illustrates just one facet of how data science is applied to solve practical problems and make a positive impact.

What do data scientists work on?

Data scientists employ statistical methods to gather and structure data, showcasing their adeptness in problem-solving. Their responsibilities extend to devising solutions for challenges arising in data collection, cleaning, and the development of statistical models and data science algorithms. This underscores the importance of problem-solving skills in their multifaceted roles.

What do I need to know to become a data scientist?

To begin as a data scientist, one must acquire skills in data wrangling, become proficient in organizing and structuring data, grasp essential concepts such as predictive modelling, and master a programming language. Additionally, developing a working familiarity with diverse tools and datasets is crucial. Ultimately, the goal is to extract actionable insights from the information. One can acquire these skills with an expert-led data science course at top institutes like Imarticus Learning.

AI in Business 

Artificial Intelligence (AI), is a revolutionary technology that is designed to simulate human intelligence and is changing lives both in our personal spaces as well as in the business world. AI in business is prevalent in a lot of applications and software and it plays a very big role in revolutionising business practices, workflows, and entire sectors by modifying the way we work, analyse data as well as access information. 

The use of AI in Business is spread to almost every sector and department; from advertising and marketing to product innovation, customer experiences, maintenance, and more. In current times, Artificial Intelligence undoubtedly holds a lot of influence on how we do business and will continue to have an impact in the future as well.

Keep on reading to get an insight into the benefits of AI in business as well as AI in business examples. 

Use of AI in Business

The use of AI in business is spread to various business areas and offers increased productivity and efficiency while also promising scalability, insights, and growth. The various departments and applications where the use of AI in business is making an impact are as follows:

• Accounting: Various accounting tools use AI to categorise transactions or create cash flow projections, with applications for payroll, tax, and financial forecasting.
• Content Generation: Generative AI is gaining a lot of traction as a tool that can assist in writing, researching, and editing as well as creating graphics, videos, and other media. 
• Customer Service and Support: Callbots and Chatbots offer an easy and convenient way to extend their current customer service capabilities and increase the volume of customer inquiries that their team can respond to. 
• Cybersecurity: Cybersecurity tools that are AI-powered can monitor systems activity against cyber attacks and can help by detecting risks and areas of vulnerability. 
• Information Technology (IT) Operations: AI can be used to streamline data and maintain IT infrastructure, automating tasks like performance monitoring, workload scheduling, and data backups. 

Benefits of AI in Business

The benefits of AI in business are varied and this technology has the potential to deliver significant advantages across different departments and business functions. Some of the major benefits of AI in business are: 

• Reduce Costs: The use of AI as well as machine learning in a pricing system can help in increasing revenue and if companies take up an integrated approach, it can lead to a definite growth in their revenue as well.
• Reduce Human Error: Making mistakes is human and even the most skilled, experienced, and professional employee can make a mistake. This is where artificial intelligence can assist and help in avoiding these mistakes. Autonomous artificial intelligence is AI that can manage operations independently.
• Avoid Fraud: In AI self-learning neural networks help examine and analyse user behaviour, identify suspicious transactions, and create algorithms to prevent financial losses. This helps the system become less vulnerable, which is crucial to establish customer trust. 

AI in Business Examples

Some of the world’s biggest tech giants are in a race to become the leaders in Artificial Intelligence and they truly are the trailblazers who have embraced AI to give the world some of the most revolutionary products and services. The top AI in business examples are: 

• Alphabet –Google: Alphabet is the parent company of Google and Waymo which is the company’s self-driving technology division, and wants to bring self-driving technology to the world not only to move people around but to reduce the number of crashes as well. Autonomous vehicles by Waymo are currently shuttling riders around California in self-driving taxis. 
• Apple: One of the biggest and most respected technology companies globally, Apple uses artificial intelligence (AI) as well as machine learning in its products like the iPhone which enables the FaceID feature, or the HomePod smart speakers, which enables the smart assistant Siri. Apple is also expanding its services through the use of AI by helping you find your photo in iCloud or to recommend songs on Apple Music.
• Alibaba: World’s largest e-commerce platform, Alibaba sells more than eBay and Amazon combined and AI is a crucial part of its daily operations, predicting what customers might want to buy. Alibaba also uses AI in its City Brain project to create smart cities and uses AI algorithms to help reduce traffic jams by monitoring every vehicle in the city. 
• Amazon: Amazon is at the top of its AI game with its digital voice assistant Alexa, however, there are various other aspects of its business where artificial intelligence is a key player. Amazon offers a new convenience store concept called Amazon Go where no checkout is required. The store uses AI technology that tracks what items you pick up and then automatically charges you for those items through the Amazon Go app on your phone. 

Conclusion

The future of AI in business depends on its capabilities as opposed to technologies. AI plays a key supporting role in business in three major categories: automating processes, analysing data to gain insight, and customer and employee engagement, companies that can strategically use AI can gain in terms of efficiency, productivity, and potential savings and growth. 

The career prospects in the field of AI are bright and with the right training, background, and education, there may be potential job opportunities in the field of artificial intelligence. The Executive Program in AI for Business in collaboration with IIM Lucknow, offered by Imarticus Learning provides the best possible learning outcomes for individuals aspiring to become AI and Machine Learning leaders. This AI and ML course is aimed at professionals working in the field of Artificial Intelligence and can help them climb the corporate ladder in the most successful manner possible.