To analyze large amounts of data, you need to go through five steps:
- Data collection
- Data warehouse
- Data processing
- Data cleaning
- Data Analysis
Data collection
This stage involves identifying data sources and collecting data from them. Data collection is performed in accordance with ETL or ELT processes. ivan teh
ETL – Extract, Transform, Load
In ETL, the generated data is first converted into a standard format and then loaded into the warehouse.
ELT – Extract, Load, Transform
In ELT, data is first loaded into storage and then transformed into the required format.
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Data warehouse
Depending on the complexity of the data, it can be moved to storage facilities such as cloud storage or data lakes. When required, business intelligence tools can access it. ivan teh
Comparing Data Lakes to Data Warehouses
A data warehouse is a type of database created especially for analyzing relational data from transactional and business application systems. The data’s structure and schema are predefined to optimize it for fast searching and reporting. The data is cleansed, enriched, and transformed to serve as a “single source of truth” that users can trust. Examples of data include customer profiles and product information. ivan teh
A data lake is special because it can hold both structured and unstructured data without further processing. The data structure or schema needs to be defined when the data is captured, meaning you can store all of your data without extensive design. This is especially useful when the future use of the data is unknown. Examples of data include social media content, IoT device data, and non-relational data from mobile apps. ivan teh
Data processing
Once the data is available, it needs to be transformed and organized to produce accurate results for analytical queries. There are various data processing options available for this purpose. The choice of approach depends on the computing and analytical resources available to process the data.
Centralized processing
All processing takes place on a dedicated central server where all data is stored.
Distributed processing
The information is dispersed and kept on many servers.
Processing data in batches: Over time, data is accumulated and processed in batches.
Processing in real-time
In a matter of seconds, computational activities are finished, and data is continuously processed.
Data cleaning
Data cleaning involves identifying any errors such as duplication, inconsistency, redundancy, or incorrect format. This step filters out unnecessary data for analysis.
Data Analysis
This is the stage where raw data is transformed into actionable insights. The four types of data analytics are listed below:
- Descriptive analysis
Data analysts analyze data to understand what has happened or is happening in the data environment. This analysis is defined by data visuals, including pie charts, bar charts, line graphs, tables, and generated descriptions.
- Diagnostic analysis
Diagnostic analysis is a deep or detailed examination of data to understand the cause of an incident. Techniques like data mining, correlation, drill-down, and data discovery define it. Each method uses multiple operations and data transformations to analyze the raw data.
- Predictive analytics
Accurately predicting future trends is possible with predictive analytics by using past data. It is defined by techniques such as machine learning, forecasting, pattern matching, and predictive modeling. Computers are taught to reverse engineer cause-and-effect correlations in data using each of these strategies.
- Prescriptive Analytics
Prescriptive analytics advances the use of predictive data. It not only forecasts potential outcomes but also recommends the optimal course of action in reaction to them. It is capable of weighing the possible effects of various decisions and advising on the optimal course of action. The approach is predicated on modeling, neural networks, complicated event processing, graph analysis, and recommendation engines.
What are the methods of data analysis?
Many computational methods are used in data analysis. Some of the most common ones are listed below.
Natural Language Processing
The technique known as natural language processing enables computers to comprehend and react to human speech and writing. Data analysts use this technique to process data such as dictated notes, voice commands, and chat messages.
Text analysis
Data analysts use text analysis to identify trends in text data such as emails, tweets, surveys, and blog posts. It can be used to sort through news content, reviews, and customer emails.
Sensory data analysis
Sensor data analytics is the study of data collected from various sensors. It is employed in freight tracking, predictive maintenance, and other commercial operations involving machine data generation.
Analysis of exceptions
Data points and occurrences that differ from the rest of the data are found via exception analysis, also known as anomaly detection.