Data Analytics

Here’s My First Article On Data Analytics

What Is Data Analytics?

Data Analytics is defined as “the scientific process of transforming data into insights for making better decisions.”

Analytics is the use of data, information technology, statistical analysis, quantitative methods and mathematical or computer-based models to help the managers gain improved insight about their business operations and make better, fact-based decisions.

Why To Use Data Analytics?

• Improved Decision Making: Data Analytics eliminates guesswork and manual tasks. Be it choosing the right content, planning marketing campaigns, or developing products. Organizations can use the insights they gain from data analytics to make informed decisions. Thus, leading to better outcomes and customer satisfaction.
• Better Customer Service: Data analytics allows you to tailor customer service according to their needs. It also provides personalization and builds stronger relationships with customers. Analyzed data can reveal information about customers’ interests, concerns, and more. It helps you give better recommendations for products and services.
• Efficient Operations: With the help of data analytics, you can streamline your processes, save money, and boost production. With an improved understanding of what your audience wants, you spend lesser time creating ads and content that aren’t in line with your audience’s interests.
• Effective Marketing: Data analytics gives you valuable insights into how your campaigns are performing. This helps in fine-tuning them for optimal outcomes. Additionally, you can also find potential customers who are most likely to interact with a campaign and convert into leads.

Analyzing big data can optimize efficiency in many different industries. Improving performance enables businesses to succeed in an progressively competitive world. Opportunity abounds for the use of analytics and big data such as:

• Determining credit risks
• Developing new medicines
• Finding more efficient ways to deliver the products and services
• Crime Prevention
• Environmental protection
• Predicting sales trends
• Healthcare

Data Analytics Vs Data Analysis: What’s The Difference?

It’s a common misconception that data analysis and data analytics are the same thing. The generally accepted distinction is:

• Data analytics is the broad field of using data and tools to make business decisions.
• Data analysis, a subset of data analytics, refers to specific actions.

Moreover to explain this confusion and attempt to clear it up we’ll look at both terms.

source — BI

But major difference is Analysis is for past data and Analytics is for predictions.

Data Analytics is a broad term that defines the concept and practice of all activities related to data.

The primary goal is for data experts, including data scientists, engineers and analysts to make it easy for the rest of the business to access and understand these findings.

Data that is raw, has no value. Instead, it’s what you do with that data that provides value. Data analytics includes all the steps you take, both human- and machine-enabled, to discover, interpret, visualize, and tell the story of patterns in your data in order to drive business strategy and outcomes.

The data analytics practice encompasses many separate processes, which comprises:

• Collecting and ingesting the data
• Categorizing the data into structured/unstructured forms, which might also define next actions
• Managing the data, usually in databases, data lakes or data warehouses
• Performing ETL (Extract, Transform, Load)
• Analyzing the data to extract patterns, trends, and insights
• Sharing the data to business users or consumers, often in a dashboard or via specific storage. — source bmc

What Is Data Analysis?

Consider data analysis one slice of the data analytics pie. Data analysis consists of cleaning, transforming, modeling, and questioning data to find useful information. — source bmc

The act of data analysis is usually limited to a single, already prepared dateset. You’ll inspect, arrange, and question the data.

When you’re done analyzing a dateset, you’ll turn to other data analytics activities to:

• Give others access to the data
• Present the data (ideally with data visualization or storytelling)
• Suggest actions to take based on the data

Classification of Data Analytics

There are major four types of classification in data analytics:

source — governanceanalytics

1. Descriptive Analytics: It is the conventional form of Business Intelligence and data analysis. Descriptive analysis or statistics can summarize raw data and convert it into a form that can be easily understood by humans.

souce — descriptive

A common example of descriptive analytics are the company reports that provide a historic review like:

• Data queries
• Reports
• Descriptive statistics
• Data Visualization
• Data dashboard

2. Diagnostic Analytics: Diagnostic analytics is form of advanced analytics which examines the data or content to answer the question “Why did it happen?”.

Diagnostic analytical tools aid an analyst to dig deeper into an issue so that they can arrive at the source problem.

3. Predictive Analytics: Predictive analytics helps to forecast the trends based on the current events. Predicting the probability of an event happening in future or estimating the accurate time it will happen can all be determined with the help of predictive analysis models. — source predictiveanalyticstoday

source — redwoodlogistics

Example:

Set of techniques that use model constructed from past data to predict the future or ascertain impact of one variable on another:

• Linear Regression
• Time series analysis and forecasting
• Data mining
• Classification

4.Prescriptive Analytics: It tells what decision to make to optimize the outcome.

The prescriptive model utilizes an understanding of what has happened, why it has happened and a variety of “what-might-happen” analysis to help the user determine the best course of action to take. Prescriptive analysis is typically not just with one individual action, but is in fact a host of other actions.

A good example of this is a traffic application that helps you choose the best route home, taking into account the distance of each route, the speed at which one can travel on each road and, crucially, the current traffic constraints.

The goal of predictive analytics it to enable:

• Quality improvements
• Service enhancements
• Cost reductions and
• Increasing productivity

Tools that can be used are: Optimization Model, Simulation and Decision analysis.

Data Analytics Process Steps

Step to understanding what data analytics is to learn how data is analysed in organizations. There are a few steps that are involved in the data analytics life cycle.

1. Understand the problem: Understanding the business problems, defining the organizational goals, and planning a lucrative solution is the first step in the analytics process. E-commerce companies often encounter issues such as predicting the return of items, giving relevant product recommendations, cancellation of orders, identifying frauds, optimizing vehicle routing, etc.
2. Data Collection: Next, you need to collect transactional business data and customer-related information from the past few years to address the problems your business is facing. The data can have information about the total units that were sold for a product, the sales, and profit that were made, and also when was the order placed. Past data plays a crucial role in shaping the future of a business.
3. Data Cleaning: Now, all the data you collect will often be disorderly, chaotic, and contain unwanted missing values. Such data is not suitable or relevant for performing data analysis. Hence, you need to clean the data to remove unwanted, redundant, and missing values to make it ready for analysis.
4. Data Exploration and Analysis: After you gather the right data, the next vital step is to execute exploratory data analysis. You can use data visualization and business intelligence tools, data mining techniques, and predictive modelling to analyse, visualize, and predict future outcomes from this data. Applying these methods can tell you the impact and relationship of a certain feature as compared to other variables.

Below are the results you can get from the analysis:

• You can identify when a customer purchases the next product.
• Can Understand how long it took to deliver the product.
• Get a better insight into the kind of items a customer looks for, product returns, etc.
• Able to predict the sales and profit for the next quarter.
• Can minimize order cancellation by dispatching only relevant products.
• Able to figure out the shortest route to deliver the product, etc.

5. Interpret the results: The final step is to interpret the results and validate if the outcomes meet your expectations. You can find out hidden patterns and future trends. This will help you gain insights that will support you with appropriate data-driven decision making. — source simplilearn.

Data Analytics Tools

There are many tools involved in data analytics they include python, R,IBM Cognos Analytics, tableau, Power BI, QlikView, Apache spark, Microsoft Excel and SaS. By the help of this tools, we can make batter data driven decision and interpret the result from the data.

Skills Required To Become A Data Analyst

• Programming skills: Knowing programming languages, such as R and Python, are imperative for any data analyst.
• Statistical skills and mathematics: Descriptive and inferential statistics, as well as experimental designs, are required skills.
• Machine learning skills
• Data wrangling skills: The ability to map raw data and convert it into another format that enables more convenient consumption of the data
• Communication and data visualization skills
• Data intuition: It is crucial for a professional to be able to think like a data analyst. — source simplilearn

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