This Guide is aimed at Beginners and Newcomers and is a great starting point to help understand data analytics. I’ve split the guide into 2 Parts. The First Part, i.e, this one, Focuses on the basic concepts and the theory, and the second part focuses on a practical walkthrough example for these concepts.
What is Data Analytics?
Data analytics is the process of Collecting, processing, modeling, and Analyzing data to discover useful information and come up with solutions, to support decision-making.
Data Analytics Scope and Applications
Data Analysis helps in converting complex, raw, and unstructured data into useful information that can be used to -
- Measure Performance.
- Predict outcomes.
- Find Causes Effect Relationships.
- Track Progress.
- Generate useful suggestions.
There are several ways to implement Data analytics. The process is implemented differently, based on the requirements and use cases.
For Example, A Manufacturing company may collect daily production data to help identify inefficiencies in the production process. This information helps the company reduce costs both in terms of time and money.
Meanwhile, an E-commerce Shop may collect customer data. This can help them get a better understanding of the purchase behavior and Market Trends, which are used to make predictions.
Importance of Data Analytics
There are several ways in which data analytics can help a business. But, Business Intelligence is only one of many countless applications of data analytics.
A great example is the Covid-19 Pandemic and how it was/is being successfully countered. Governments and Organizations constantly collected covid data to Measure its spread, Causes, and Effects. This helped generate predictions at the right time. These predictions and correlations.
This information was crucial for the effective distribution of Vaccinations, Medicines and, the effective implementation of lockdowns and containment zones. Data Plays a huge role in our modern world and with data analytics we can solve almost any problem.
Analysis vs Data analytics
“Analysis” and Data Analytics are different. An analysis is a part of the data analytics process. An analysis involves careful and critical examination of something, to gain a better understanding of it.
Ultimately, it is a very important step in the process of getting answers from data to identify patterns and make predictions.
Data Analytics on the other hand is not just a single step or activity. It is a whole process that consists of a sequence of activities that are carried out consistently.
Data Analysis Process.
Data Analytics includes five major steps. These Steps are carried out in sequence and most cases are cyclical ie. The Process is carried out continuously or repeated even after the first run.
For example, Financial Data Analytics processes have Monthly, Quarterly, and Yearly Cycles in order to generate respective reports.
The Upcoming section takes you through each step of the process, and the concepts and theories related to these steps. I’ve tried my best to explain all the foundational concepts in the simplest way possible.
The Process of data analytics starts with
1. Problem Identification and Definition
The First step involves finding Pain points and Opportunities which require a solution. This is done by, first, asking questions that relate to a problem. Before moving on to the next step, it is important to understand the problem to start formulating the steps toward solving it.
If we do not understand and define the problem, It is difficult to come up with an effective solution.
2. Data Collection
In this step, the Sources and Methodologies for collecting the data are determined. There are several sources, tools, and methods available. The type, source, and methodology of data collection are based on the Problem defined in the previous step.
For example, if we are looking for general information regarding the performance of a new product, we would conduct a survey. A survey can help generate Quantitative data.
If we want details on the reasons people like or dislike a certain product, we would go for an interview or an observational study. This gives us Qualitative Data.
Secondary data, i.e., Existing data, can also be re-collected based on the requirement. Once we define the Collection methodology, we proceed to collect the data.
3. Data Processing
The next task involves processing the collected data. Data processing involves tasks carried out to transform raw data into meaningful information. Data can be processed to reveal knowledge, interpret the behavior of data, or evaluate the effectiveness of the collected data.
A basic example is — In the previous step, if we conduct surveys, then responses may not be consistent. For example, the format of the date input by each respondent would most likely be different. Respondents will also skip several questions. Through data processing, we remove inconsistencies and ensure that it is properly formatted, stored, and structured.
4. Analysis and Interpretation.
This is the most important step and it plays a central role in the process. In this step, data collected and formatted in the previous steps are used to generate insights and recommendations.
Analysts first try to identify patterns and trends in the data with the help of Analytical techniques like Exploratory analytics.
- For example, Analysts may run descriptive analytics to describe, aggregate, and summarize the data.
- Exploratory analytics also involve Visualizing the data in the most meaningful way help to identify patterns, trends, and correlations.
Further In-depth analysis may be conducted with Advanced Statistical and Analytical tools like Machine Learning algorithms for developing predictive models like Regression, and, Classification models like decision trees.
5. Communication
Even if we can generate useful information from our analysis. if it is not communicated properly to the concerned stakeholders then all our analytical efforts would end up useless. This final step involves translating all the technical and statistical insights into easy-to-understand Reports and Dashboards, which should be presented clearly and concisely. The communication stage is completed with successful delivery and communication of results to all the end users and stakeholders.
How to master data analytics?
The Analytics process doesn’t end here. In most real-world cases, analytics is usually carried out consistently. A major part of the Analytics process, which I haven’t mentioned here involves the maintenance, security, infrastructure, and Automation of Data Analytics. These tasks are usually performed by data engineers and act as the backbone for the whole process, especially when it comes to BigData.
This Guide gives you a detailed summary of each of the steps. But it barely scratches the surface when it comes to the tasks and activities that are performed within each step of the process. Each step in the data analytics process is a diverse topic in itself.
If you’re just starting to learn data science, Start learning from Data Collection and cover the topics sequentially. This helps you understand better.
But before that, I’d recommend you to refer part two of this guide, which provides a very quick practical walkthrough of the data analytics process with Microsoft Excel.
Stay tuned for more guides and resources.
