Want to learn the basics of Data Analytics? This is a Data analytics tutorial for beginners.

Data Analytics Basics

What is Data Analytics?

Data analytics is the process that helps a business make sense of data and make important decisions. The process comprises of variety of tools and techniques based on the problem to be solved.

Every organization is generating data through its sales, marketing, operations, and other functions. Data Analytics (DA) is the enabler of translating raw data into actionable insights that drive advancement in a wide range of sectors and industries.

Organizations may gain a competitive advantage, streamline operations, improve customer experiences, and manage complicated challenges by analyzing massive amounts of data.

This article discusses the fundamentals of Data Analytics, including its key steps, different types, the tools used, why it’s important, and what future trends we can expect.

Data Analytics process steps

Analyzing data and drawing conclusions involves several steps. These steps are critical to the success of the initiative. Let’s have a look at the key steps of the Data Analytics lifecycle.

Step 1: Define the problem

The first step is to define the problem, which needs to be solved. Without a clear objective, the data analytics initiative is not going to be useful.

Defining the problem also includes identifying the data needed to address the problem.

For example, an e-commerce wants to identify the problems with the sales numbers. The sales volume is stagnant or very erratic.

Another example,

An organization would like to predict the demand for products so that it can ensure enough supply to meet the demands.

Step 2: Data Collection

The next step is to organize and collect the data for the project. The data has already been identified in the first step. The data can be collected from existing data sources (like orders, customers, etc.) or can be collected through surveys, interviews, market research, or observations.

Step 3: Data Cleaning

The next step is to process and clean up all of the data collected. Data cleaning includes correcting the errors in data, and removing duplicates and inconsistencies.

For example, See the table below. The date format in the second row is incorrect. This needs to be identified and corrected as shown in the “Corrected data” column.

The processed or cleaned data is then migrated to a location 

Step 4: Data analysis

Once the data is cleaned, it’s time for analysis. Analysis may involve a variety of techniques.  Using statistical or mathematical techniques to discover patterns, relationships, or trends are data analysis techniques. Software applications/platforms like R, Python, and Excel are used for data analysis.

Step 5: Interpreting and visualizing the data

This is the step that helps us understand – What does the data tell us? Once we (Business and Data Analysts) interpret the results, it’s important to create visual representations that are easy to understand.

Please note that stating the interpretations as text is not as powerful as showing it visually.

“A picture is worth a thousand words.”

Step 6: Data storytelling

The next step is to communicate the findings and insights to the stakeholders. Stakeholders could be non-technical or may not be able to understand the technical jargon or terms. Presenting the data in such a form that all the stakeholders can understand the make decisions.

Step 7: Measuring effectiveness and improvement

The final step is to validate the effectiveness of the solution. The measures are put in place so that actual data can be measured against the expectations. In case of a “not meeting the expectations” scenario, a root cause analysis can be conducted to find the problems, that need to be solved. This cycle will be continued till the expectations are met.

We have a detailed blog article on Data Analytics lifecycle phases.

Check out our Data Analytics Courses

We have created several courses for you to get into the exciting world of Data Analytics. The courses are designed to suit your learning and upskilling needed:

• Data Analytics Certification Training
• IIBA Data Analytics CBDA Prep Training
• Power BI Certification Training
• Data Analytics Fundamentals Training
• Data Visualization Training in Tableau

Types of Data Analytics

There are four types of Data Analytics. Each type is characterized by a goal, it serves in the data analytics process.

Here are the four types of analytics:

Descriptive Analytics

Descriptive analytics is used to find the answer to the question – “What has happened?”. This type of analytics uses historical data to understand what has happened in the past.

For example,  An organization targets to achieve sales growth of 10% every quarter.

Descriptive analytics uses sales transactions (recording every order), summarizes these, and makes calculations to check if the “10% quarterly growth” is achieved or not. In descriptive analytics, we use summaries,  charts, and pattern analysis to interpret the results.

Descriptive analytics can be used to answer questions like these:

• What were our sales figures last quarter?
• How has our website traffic changed over the past year?
• What is our average customer satisfaction level?

Diagnostic Analytics

Diagnostic analytics goes beyond descriptive analytics to explore the reasons why something happened. It does this by examining diverse datasets and looking for patterns and correlations.

Examining various datasets to acquire a complete picture of what transpired is common in diagnostic analytics. A retailer, for example, can examine sales data, customer feedback, and marketing campaign data to determine why sales fell in a specific month.

Diagnostic analytics involves formulating hypotheses about the root causes of events. These hypotheses can then be tested using further analysis or experimentation.

Predictive Analytics

Using past data, predictive analytics makes future projections. Finding patterns and trends in data is done by applying machine learning techniques and statistical algorithms. Many different factors, like consumer behaviour, sales information, and market trends, can be predicted by using predictive analytics techniques.

Based on historical data, it can predict future events such as customer turnover, demand for new products, or the chance of a natural disaster.

Prescriptive Analytics

Using the information from predictive analytics, prescriptive analytics makes recommendations that can be put into practice.

Prescriptive analytics not only forecasts future events but also makes recommendations for counteractions. This has the potential to assist businesses in making astute decisions regarding the allocation of resources, refining marketing strategies, and enhancing customer support.

Prescriptive analytics has diverse applications, providing recommendations in various fields, such as:

• Marketing: Which products should we promote? Which channels should we use?
• Sales: How should we allocate our sales force? How should we price our products?
• Operations: How should we schedule our production? How should we manage our inventory?

Tools Used  in Data Analytics

The foundation of data analytics is made up of these tools and technologies, which enable experts to gather, examine, and visualize data to make deft decisions. Whether you’re a data scientist, analyst, or business analyst, these tools satisfy a broad range of analytics requirements and guarantee accuracy and efficiency in deriving useful insights from data.

R and Python

Python is a general-purpose programming language and has become the language of choice for Data Engineers, scientists, and analysts. It has extensive libraries for data analysis and visualization.

R is also a powerful language suited for statistical computing. It provides extensive support for all types of statistical analysis.

Key Features:

• Extensive libraries for statistical analysis and machine learning.
• Versatility in handling and manipulating data.

Microsoft Excel

A ubiquitous spreadsheet engine with robust data analysis capabilities.

Key Features:

• User-friendly interface for data manipulation and visualization.
• Fundamental for basic analytics tasks and reporting.

SQL

SQL (Structured Query Language) is the de-facto language for retrieving, manipulating, and managing data. Data Analytics initiatives need extensive use of SQL at various stages.

Key Features:

• Highly versatile language that is easy to learn.
• Standard SQL is supported by most of the databases. So you can learn it once and use it everywhere.

Tableau

Tableau is an advanced data visualization software for interactive and intuitive insights.

Key Features:

• Seamless integration with various data sources.
• Rich visualization options for effective communication.

Power BI

Microsoft’s Power BI for creating dynamic and interactive reports.

Key Features:

• Real-time data analysis and sharing.
• Integration with other Microsoft products. 

SAP Business Objects

Business intelligence suite offering a range of reporting and analysis tools.

Key Features:

• Comprehensive tools for business intelligence and performance management.
• Integration capabilities with various data sources. 

Why Data Analytics is important?

Data Analytics has applications across industries and functions. It can be used to improve performances, optimize processes, provide insights, and predict forward-looking insights.

Here are some examples of how Data Analytics has helped organizations.

Credit Scores

The credit score is used by banks and financial institutions to decide whether the loan can be extended to the customer.  It is a statistical analysis performed by lenders and financial institutions based on historical data of the customer.

It has become the most widely used score for checking the creditworthiness of a customer.

Typically the credit score (FICO score) is in the range of 300 to 850 (It’s 300 to 900 in India). The FICO score is calculated by taking into consideration – Payment history, Credit Exposure, Credit type and duration, and other factors.

The readmission rate was reduced by 40%

Let us take the example of UnityPoint Health. At UnityPoint Health, predictive analytics helped in predicting the readmission risk for each patient.

The hospital scored every patient for readmission risks.  Using these results, the hospital was able to predict and prevent a patient’s readmission within thirty days through the early treatment of the symptoms. In less than two years, this hospital was able to readmission by 40%.

Ref: How three hospitals reduced readmissions (https://www.managedhealthcareexecutive.com/view/how-three-hospitals-use-predictive-analytics-reduce-readmissions) 

Fraud Detection

Transaction fraud is a challenging problem for Banks and Financial Institutions. Fraud detection represents a set of proactive measures undertaken to identify and prevent fraudulent activities and financial losses.

Fraud detection involves the use of statistical analysis and Artificial Intelligence. Any fraud detection system also faces a challenge because of constantly changing fraud patterns and fraudsters’ tactics.

Reference: Fraud Management in  Banks

Optimizing Inventory

Inventory Optimization is achieved through prescriptive analytics. If an organization is using multiple distribution channels for selling its products, it’s extremely difficult and complex to determine the optimal inventory strategy. The solution lies in inventory optimization using prescriptive analytics.

Ref: Analytics in Supply Chain Management (https://www.linkedin.com/pulse/power-data-analytics-supply-chain-management-rafael-a-vela-/) 

Future Trends in Data Analytics

Synthetic Data

Synthetic data is data that’s artificially generated using algorithms and machine learning techniques. It’s meant to mimic real-world data, but it doesn’t contain any personally identifiable information (PII) or sensitive data. Instead, it’s created by analyzing a sample of real-world data and then using that analysis to create new synthetic data that are statistically similar to the original data.

Synthetic data can be created using a small sample of real-world data, which means there’s no need to store large amounts of sensitive data. This can reduce the cost of data storage and management.

Analysis: Synthetic data can be used to create large datasets for analysis. This can help organizations identify patterns and trends that might not be visible in smaller datasets.

Edge Analytics

By processing data closer to the source, edge analytics lowers latency and enhances decision-making in real-time. Edge analytics will be essential for interpreting data collected at the edge devices as the Internet of Things (IoT) grows. This trend ensures quicker insights, particularly in applications requiring immediate responses.

Internet of Things (IoT)

An industry-changing development is the combination of data analytics and the Internet of Things (IoT). IoT devices generate huge amounts of data, and analytics will be essential for obtaining actionable insights. From optimizing operational processes to enhancing user experiences, the synergy between IoT and data analytics will drive innovation across sectors.

Real-Time Data/Insights

The demand for real-time data processing is escalating as businesses seek instantaneous insights for timely decision-making. Advanced analytics tools capable of processing and analyzing data in real time will become increasingly crucial. This trend is especially vital in sectors such as finance, healthcare, and manufacturing where immediate responses are essential.

Augmented Analytics

Augmented Analytics is a concept of data analytics. It uses Natural Language Processing, Machine Learning, and Artificial Intelligence to automate and enhance data analytics, data sharing, business intelligence, and insight discovery.

It is expected to be one of the most talked about trends in the coming years. It will make it easier for people to interact with data.

Conclusion 

Data analytics serves as a transformative force reshaping global businesses and industries, driving innovation, and optimizing processes. In the era of unprecedented technological advancements, embracing data analytics is imperative for success in a data-driven landscape.