Introduction to Data Analysis Process
Data analysis process is the process of analyzing data to identify the data patterns or business decisions. There are several techniques, process & tools involved in data analysis. Data analysis is a very vital for knowing the exiting business performance and predicting the possible patterns for the betterment of the business. data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating data visualizations through charts and graphs, applying statistical analysis, machine learning models.
Phases of Data Analysis Process
Let us define each of the phases in detail and how can we achieve it using the technology stack.
1. Business Understanding
While analyzing the data for the industry we should have clear overview and understanding of the industry what it does, what kind of decision they are going to make, for which purpose the data is being analyzed, this all data analyzing process is started with a question, lots of people think that the data can be analyzed by using the data set, availability of the data set is sufficient to analyze any kind of pattern, as per understanding there is no data set for analyzing the data all we need it the questions define the data sets itself, the only challenge, in this case, is while answering the one questions another question can be pop up bu it is ok, it more than actually a part of data analyzing process.
2. Acquire the Raw Data
This is the step where after defining the question, data is collected from the different source such as data warehouse, logs, and data set to answer those question, row data is queried to answering the questions but this is not the row data set, instead, we need to call it row data because it is not exactly in the form of where we want it to analyzing.
3. Extract the Data
This is the step where data is extracted to create a final data set. that will allow us to leads the further analyzing process this is a clean data set. SQL is used for extracting the data from the database. the database which is queried to extract the data having several rows exceed 1 Million. where database query languages like SQL enables an Analyst to analyze and transform data easily. SQL is the first thing you should learn as it enables you to work on the dataset.
4. Transform the Data
Data transformation is the process of converting the data or dataset from on state or structure to another state structure, it is the fundamental state of data integration where the data collected from different sources have been integrated into particular structured data in such manner that it can be used at a destination for analysis process this process is known as ETL(Extract Transform Load). The data transformation process refers to detecting and understanding the data in its original structured or source format. This is usually achieved with the help of algorithms which is implemented by using data analysis and profiling tool. This step helps you decide what needs to happen to the data to get it into the desired or requested format. Generally, R or Python language enables you to perform data transformation on large or complex data that is coming from the source.
5. Data Visualization
After building or creating the datasets, we need to visualize data to develop your Hypothesis or Insights to explore and evaluate the data. Tableau/saas (data visualization application) allows us to visualize large rows of columns of data in both structured and unstructured databases and easily bring insights/ meaningful patterns out of the dataset.
6. Statical Analysis
it is the important aspects of data analysis which summarize the data and it’s understanding in terms of model and graphs apart from this it also explains how the data is related to the underlying real world. the statical analysis is also used to identifying the pattern or trends for predictive analytics which helps to make the business decision, it also helps to determine the statical significance of the data set.
7. Data Model Development
industries are extremely interested to deploy model which has predictive capabilities, data model development consists of the definition of model goals, the concept of the problem and its translation into a computational model.
R/Python enables you to create a statistical model to reject any invalid or null hypothesis, the modern application plays an important role in handling the mathematical complexity. Vendors are developing software as services such as table and SAS to making the analysis process easier and easier by building models using automated predictive modeling tools designed for business analysts. analytics professionals are utilizing machine learning algorithms from open-source marketplaces or model building APIs to build a predictive application model.
8. Recommendations/Report/Story
This is the final step of the data analytics process where analysis decision is summarized and the result or consequences of the analysis process is represented in terms of story, report, recommendations and PPT, tableau and SAS application plays an important role to summarize the analysis process via a report or story building, this report includes:
- Customer/Industries centric outcomes.
- Strategy and decision tree for the industries.
- Identification of business priority.
- Identification of target audience or consumers for the products.
- business case based on measurable outcomes.
Conclusion
For most businesses, enterprises, industries and government agencies, lack of data isn’t a problem. There’s huge information available to make a clear data-driven and business-oriented decision. With so much data to use in the analytics oriented process, we need something more appropriate knowledge and information from available data: Business needs to know it is the right data for making the data-driven decision. Business needs to draw accurate conclusions from that data/information/knowledge. Business needs data that informative and useful for decision-making process.
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