In today’s data driven era, businesses and organizations are highly rely on data analytics to gain useful insights and make smarter informed decision. Taking you to the real world application of predicting customer behaviour to optimizing processes, data analytics has successfully become the cornerstone of smarter decision making.
According to the fact, almost 90% of the world’s data was created in the last two years and that happened only because of it. This created a boom in a digital world of IT industries and due to that companies are demanding high skilled working professionals.
In this blog, we delve into data analytics, covering its types: descriptive, diagnostic, predictive, and prescriptive analytics. We’ll explore key methods, the importance of analytics in decision-making, and the growing career opportunities in this dynamic field.
What is Data Analytics?
Data analytics is a highly demanding field among students and working professional in the field of computer science.
It is a detailed process that involves cleaning, transforming and interpreting data to discover patterns and insights. The goal is to make smarter decisions, helps organizations or individual to make better choices.
There are different steps involved to understand this concept in detailed manner:
Data Collection in Data Analytics
The first step of data analytics is to gather the right data. Data having different sources, whether you can for internal source or external source.
- Internal Source: Internal source includes company’s data which handles employee’s information, financial records etc.
- External Source : External source includes data generated from social media, public data, surveys, customer feedbacks etc.
Data Cleaning and Preprocessing
Once you gather the data, it’s rarely perfect . It consists of errors, missing values, duplicate values, and lot of irrelevant information. Data cleaning is the next process after data gathering for clearing these issues, so the data is good to go for the analysis.
key tasks for the data cleaning includes : removing duplicates, handling missing values, correcting errors and standardizing data.
Exploratory Data Analysis (EDA)
After cleaning the data , next step is to explore it to understand it structure and uncover initial patterns. This is done through EDA where :
a) Descriptive Statistics which includes mean, median, mode as well as standard deviation to get a view of data description.
b) Data Visualization tools are used which includes scatter plots, box plots and histograms to identify patterns and maintains relationship between variables.
Data Modeling in Data Analytics
At this step, you have more statistical and mathematical model that helps to analyze data more deeply so that classification and prediction becomes easier. In data modeling analysts prepare data for analysis in the organizations that is really helpful in making strategic decision insights.
Data Modeling not only helps in reducing errors but also improves database performance.
Data Interpretation and Insights
The next part is data interpretation, once a model is generated, it is very important to cross check the result with the great insights. Checking if the model’s prediction aligns with the real world observation. If the predictions are not accurate, adjustments may needed.
Decision Making and Action
The final step in the data analytics process is to use the insights derived from data to make decisions. These decisions can be at a strategic or operational level and often lead to action such as:
Adjusting marketing strategies: For example, if data shows a specific products is popular during a season , a company may focus marketing efforts on that product at the right time.
Types of Data Analytics
Data Analytics plays a critical role in today’s data-driven world. It helps organizations uncover insights, predict trends, and make informed decision. Below are the four main types of data analytics, explained in detail with examples:
Diagnostic Analytics
Objective : To investigate the reason behind a particular outcome or trend.
Key Question : Why did it happen ?
Features : a). Diagnostic Analytics involves techniques like drill-down, data discovery and correlation analysis.
b). Uses statistical analysis to identify trends, anomalies and relationship between variables.
Applications : a). In E-Commerce : Understanding why a specific product experienced a sudden spikes in a sales to increase the sales projection.
b). Finance : Understanding why there is a decrement in a sales for a specific product or some other finance factors.
Tools : There are different programming languages also we are using includes Python, R , SAS and SQL.
Example : In telecom industry, suddenly a company faces a dip in customer retention. Diagnostic analytics helps in revealing that customer were dissatisfied with recent services outage.
Descriptive Analytics
Objective : To summarize and interpret historical data to understand what has happened.
Key Question : What happened?
Features : a). It involves basic statistical techniques like averages, percentages as well as inaccuracies.
b). Utilize data visualization tools like dashboards, graphs and reports.
Applications:
- Business : Analyzing sales report to determine quarterly performance.
- Healthcare: Tracking patients admissions rates over time.
Tools : There are different tools we can use for the descriptive analytics includes SQL, Power BI, Tableau, Excel.
Example : A retail company uses descriptive analytics to determine their overall sales projection over the past years. They project that their sales report gets increased by 15%, primarily due to occasional promotions.
Predictive Analytics
Objective : In Predictive analytics, future insights or predictions can be done using past or historical data.
Key Question : What is likely to happen ?
Features : a). Predictive analytics uses statistical models, machine learning and data mining techniques.
b). Highly relying on algorithms to predict patterns and insights.
Applications :
- Heathcare : Predicting patients readmission rates in the hospital.
- Retail : Forecasting demand for products using occasional season.
Tools : There are different tools we will be covering in predictive analytics includes Python, R , SPSS and TensorFlow.
Prescriptive Analytics
Objective : With data predictions and insights, we will recommended actions.
Key Questions : What should we choose ?
Features : a). It combine insights from all other three includes diagnostic, predictive and descriptive analytics.
b) For suggesting action they are using optimization and simulation algorithms.
c). It often integrates artificial intelligence and machine learning for automated decision making and workflows.
Applications:
a) Logistics : Optimizing delivery routes for faster shipping.
b). Marketing : It enhances customer experience , increase sales projections and maximizing profitability.
Tools : There are several tools in prescriptive analytics which includes Python, MATLAB, Tableau, Gurobi, RapidMiner etc.
Why Data Analytics is Important ?
Data Analytics is important across industries because it provides the tools and techniques to optimize decision making, improve processes, and gain a competitive edge in the market. Below is a detailed explanation of its importance:
Improved Decision Making
Traditional decision making relies on incomplete or irrelevant information, leading to inaccuracies. Data Analytics offers a systematic approach to analyze huge amount of well structured and unstructured data. Through identification of trends and patterns, industries can make informed and accurate decisions.
Improves Operational Efficiency
Analytics identifies inefficiencies in processing and helps to design strategies for improving productivity. For Example : Supply chain analytics can minimizes delay and reduce costs by optimizing logistics.
Predicting and Understanding Customer Behaviour
Organizations uses analytics to understand their customer base and experiences. Tools like CRM ( Customer Relationship Management ) helps analyze their purchase history, feedback and preferences, enabling businesses to improve customer satisfaction and loyalty.
Predictive and Prescriptive Insights
Predictive analytics uses historical data to predict the future insights such as market demand or customer churn. Prescriptive analytics takes it a step further by providing actionable recommendations to achieve specific goals. For Example : In healthcare industry, predictive analytics helps to determine the disease outbreak whereas prescriptive analytics helps to suggest the treatment plans to it.
Risk Management and Cost Reduction
Analytics can easily identifies potential risks by analyzing historical data and identifies unusual patterns. This is really helpful in finance industries where the risk of finance threatens the data loss so by identifying the real time fraud detection, we can prevent the financial fraud.
Data Analytics helps organizations identifying where the resources are getting wasted. For instance, manufacturing companies use it to optimize energy consumption or reduce material waste during production.
Improved Marketing Strategies
Marketing leverages analytics to measure campaign effectiveness, understand customer demographics, and optimize ad spend. Tools like Google Analytics and social media insights provide real time data that helps in improving marketing strategies.
Jobs in Data Analytics
Data Analytics offers diverse job roles across industries, each requiring a unique blend of skills in data analysis, handling and interpretation. Here are common roles in the field of data analytics:
Data Analyst
Role : Data Analyst helps in collecting , cleaning and interpreting data to generate insights.
Tools Required : SQL, Python / R, Tableau / PowerBI, Advanced Excel
Industries : Healthcare , Finance , Marketing, IT , E-Commerce
Business Analyst
Role : Business analysts helps in bridging gap between business needs and technical solutions by analyzing data to recommend strategies.
Tools Required : SQL, Data Modeling, Requirement Gathering, Stakeholder Communication, SDLC
Industries: IT, Banking , Finance , Insurance , Consulting
Data Scientist
Role : Applying statistical techniques, machine learning, and predictive modeling to analyze complex data.
Tools Required : Python/R, machine learning algorithms, big data tools, data engineering.
Industries : Tech, Healthcare, Finance, Research.
Data Engineer
Role : Building and maintaining data pipelines, databases, and infrastructure for analytics teams.
Tools Required : SQL, Python, ETL tools, cloud platforms (AWS, Azure), big data tools (Hadoop, Spark).
Industries : Technology, E-commerce , Telecommunications.
In similar manner, there are different jobs in the field of data analytics like Marketing Analyst, Operational Analyst and so on….
