Expertise in setting up multiple Data warehousing, Data migration, Data Integrations & Data Analysis projects.
Step 1) Extraction
Data is extracted from the source system into the staging area. Transformations if any are done in staging area so that performance of source system in not degraded. Also, if corrupted data is copied directly from the source into Data warehouse database, rollback will be a challenge. Staging area gives an opportunity to validate extracted data before it moves into the Data warehouse.
Three Data Extraction methods:
Irrespective of the method used, extraction should not affect performance and response time of the source systems. These source systems are live production databases. Any slowdown or locking could affect company’s bottom line.
Some validations are done during Extraction:
Step 2) Transformation
Data extracted from source server is raw and not usable in its original form. Therefore, it needs to be cleansed, mapped and transformed. In fact, this is the key step where ETL process adds value and changes data such that insightful BI reports can be generated.
In this step, you apply a set of functions on extracted data. Data that does not require any transformation is called as direct move or pass through data.
Validations are done during this stage
Step 3) Loading
Loading data into the target datawarehouse database is the last step of the ETL process. In a typical Data warehouse, huge volume of data needs to be loaded in a relatively short period (nights). Hence, load process should be optimized for performance.
Types of Loading:
Create and analyze sophisticated plans, budgets and forecasts based on business process with small to large data sets. Integrated scorecards and strategy management – Model metrics to measure progress toward objectives and link them dynamically to actions and forecasts.
Business Intelligence integrates reporting, modeling, analysis, dashboards, stories, and event management so you can understand your organization’s data, and make effective business decisions.
Financial Consolidation & Reporting supports the close, consolidation and reporting process with the agility and affordability of an integrated solution.It also helps finance teams deliver financial results, create informative financial and management reports and provide the Chief Financial Officer (CFO) with an enterprise view of key financial ratios and metrics.
A data warehouse, or enterprise data warehouse (EDW), is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI), and machine learning. Simplifies ongoing integration tasks and new app development. Ensure consistent master information across transactional and analytical systems. Addresses key issues such as latency and data quality feedback proactively rather than “after the fact” in the data warehouse. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data.
KPI is a quantifiable value expressing the business performance in a shorter time-frame level. They are used in different industries to track organizational processes, improve efficiency and help businesses to understand and reflect on the outcomes. When a business is measuring the effectiveness of a process, often metrics and KPIs are established to perform the evaluation and analysis.
Gain value with the most complete cloud: Comprehensively address your needs with functional breadth and depth across financial and operational planning, consolidation and close, master data management, and more.
2021 © All rights reserved by 1Direction Global.