This will save time and reduce the likelihood of errors

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Example: For e-commerce, it is important to increase the average check and reduce customer churn. Data analysis can suggest which products to recommend to customers.
Big Data
Step 2: Collecting data
Use all available data sources:
Internal: CRM, ERP, analytics systems.
External: social networks, open data.
Streaming: real-time data from IoT devices or log files.
Recommendation:
Automate data collection with ETL (Extract, Transform, Load) tools.
Big Data
Step 3: Preparing and storing data
Data cleaning sweden bulk sms service Make sure the data is correct and does not contain duplicates.
Structuring: Use data organization platforms such as Hadoop or cloud storage (AWS, Google Cloud).
Ensure security. Implement encryption and access control.
Big Data
Step 4: Data Analysis
For analysis, use advanced tools and approaches:
BI platforms (Tableau, Power BI): data visualization for easy understanding.
Machine learning (Scikit-learn, TensorFlow): building predictive models.
Stream processing tools (Apache Spark, Kafka): real-time data analysis.
Helpful tip:
Start with simple metrics and models to get the first results quickly. Gradually move on to more complex tasks.
Big Data
Step 5: Integrate the results into the business ⚙
Apply the findings of the analysis to: