DATA ANALYTICS

Data analytics converts raw data into actionable insights. It includes a range of tools, technologies, and processes used to find trends and solve problems by using data. Data analytics can shape business processes, improve decision-making, and foster business growth.

IMPORTANCE:

Data analytics helps companies gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build related digital products, optimize operations, and increase employee productivity.

BIG DATA ANALYTICS:

Big data describes large sets of diverse data—structured, unstructured, and semi-structured—that are continuously generated at high speed and in high volumes. Big data is typically measured in terabytes or petabytes. One petabyte is equal to 1,000,000 gigabytes. To put this in perspective, consider that a single HD movie contains around 4 gigabytes of data. One petabyte is the equivalent of 250,000 films. Large datasets measure anywhere from hundreds to thousands to millions of petabytes.

Big data analytics is the process of finding patterns, trends, and relationships in massive datasets. These complex analytics require specific tools and technologies, computational power, and data storage that support the scale.

BIG DATA ANALYTICS WORK:

Big data analytics follows five steps to analyze any large datasets:?

1. Data collection
2. Data storage
3. Data processing
4. Data cleansing
5. Data analysis

DATA COLLECTION:

This includes identifying data sources and collecting data from them. Data collection follows ETL or ELT processes.

ETL – Extract Transform Load

In ETL, the data generated is first transformed into a standard format and then loaded into storage.

ELT – Extract Load Transform

In ELT, the data is first loaded into storage and then transformed into the required format.

DATA STORAGE:

Based on the complexity of data, data can be moved to storage such as cloud data warehouses or data lakes. Business intelligence tools can access it when needed.

DATA PROCESSING:

When data is in place, it has to be converted and organized to obtain accurate results from analytical queries. Different data processing options exist to do this. The choice of approach depends on the computational and analytical resources available for data processing.

Centralized processing?

All processing happens on a dedicated central server that hosts all the data.

Distributed processing?

Data is distributed and stored on different servers.

Batch processing?

Pieces of data accumulate over time and are processed in batches.

Real-time processing?

Data is processed continually, with computational tasks finishing in seconds.?

DATA CLEANING:

Data cleansing involves scrubbing for any errors such as duplications, inconsistencies, redundancies, or wrong formats.? It’s also used to filter out any unwanted data for analytics.

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