Information as an asset is still in the early adoption phase, making it a major competitive advantage for leading companies as they focus on digital transformations. In turn, data and analytics have become strategic priorities. Today, not more than 50% of organization strategies mention data and analytics as core values targeted to deliver increased value.

Many enterprises still struggle under the weight of traditional business models and analog business processes. Other business recognize their potential but cannot make the cultural shift or commit to information management.

With data collection, ‘the sooner the better’ is always the best answer.

Marissa Mayer

Here we try to enlighten on the technical know-how of utilizing company data and making it work in favor of your processes and targets. We will highlight the methods of gathering data, building an inventory of that data (data warehouse), and steps to achieve this linearly. Before diving into details first, we need to understand the terminologies and various types of data in an organization.

What is Data Warehouse (DWH)?

To make the data "Stable, Scalable, and Maintainable" from the Business analysis and Decision-making process perspective, we need to transform the raw data into a structured data model called the data warehouse (DWH). In other words, A data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data. The terms used for the definition of the data warehouse is described below.

Subject-Oriented: It implies that the data is organized around various subjects like product categories, items, Consumers, Customers, Professionals. Data can be further broken down to the lowest grain called Dimensions.

Integrated: It implies that the data is integrated from various sources irrespective of its format. Example - DBMS/RDBMS, Legacy systems, Cloud, Excel, Flat files.

Time-Variant: The data stored in the DWH is the trend of the overall period (excluding the real-time data), which varies depending on the timeframe selected (i.e., Month, Quarter, Year).

Non-Volatile: It implies that the data will not change w.r.t the facts captured in the form of data (read-only data). However, there may be some dimensions like Age, Address, Salary, Assets which may change over time. Nevertheless, these changes will not directly impact the specific instance/transaction when the data is captured.

A Data warehouse schema involves arranging data into two categories Measures (these data is stored in Fact tables) & Dimensions (these data is stored in Dimension tables).

Why create the DWH?

• Helps organization who wants to centralize their reporting projects and get insights for decision making.
• Integrate data from multiple sources
• Merge historical data with current data (maintaining historical data)
• Improve the quality of data
• Speeding up response times to help quickly analyzing large volumes of data
• Faster and flexible reporting

This comprehensive guide covers the types of data and elements of the data warehouse that can be utilized. Now we dive into how the process building such a data model works. This will start from the raw data source to the final model having the structured data that can help us get insights into one's business. The widely used process to achieve this is the ETL (Extract, Transform & Load) method.

What is ETL (Extract, Transform and Load)?

ETL is the method/process used to implement the data warehouse concept and bring it into real-world Business practice. Extracting raw data from source to the staging area (STG), transforming and maintaining the history of data in the operational data store (ODS) & finally loading data in the Dimension and Facts table in the data warehouse (EDW).

Why ETL?

In today's world, all companies have data at their disposal in one form or another. To derive meaningful business insights from that data, the concept of Data warehousing has evolved. ETL is the stepping stone for implementing the concept of data warehousing.

• Using ETL, we can pull the data from multiple sources from disparate platforms to a common platform. Thus, the companies can use the data even from their legacy systems, which they may have ignored due to technical limitations or the additional cost burden.
• ETL can handle a large volume of data and provides flexibility to integrate & re-organize data as per the business requirements.
• Like reading data from disparate systems is essential, similarly loading finished data into disparate systems for reporting and analysis is equally important. Hence ETL provides flexibility in loading data across disparate systems, thereby providing easy access to finished data for reporting, data visualization, and data science.
• Repetitive processes can be automated and hence do not require any manual intervention. Process automation helps to minimize human error, save on cost, and optimize resource utilization.

How to implement ETL?

As the name suggests, The ETL process is divided into three steps.

Step – 1: STG - Extracting Raw Data to Staging Area

Extraction means fetching data from sources. Sources can be documents, spreadsheets, CSV files, Flat files, SQL Server, My SQL, Azure, Oracle, and so on. Below are a few examples.

There are two main types of extraction – full and partial.
• Full extraction is used for initial extractions or if the source having a low volume of data
• Partial extraction is used when there is no need to extract all the data or if the source having a high volume of data makes a full extract impossible. In partial extraction, only updated or new data will be extracted.

Step – 2: Operational Data Store (ODS) – Transforming data