DATA WAREHOUSE IMPLEMENTATION

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DATA WAREHOUSE IMPLEMENTATION 

WHAT?

• Data warehouse implementation is a series of activities that are essential to create a fully functioning data warehouse after classifying, analyzing and designing the data warehouse with respect to the requirements provided by the client. 
• Various phases of data warehouse implementation are: - 

(i) Planning 

(ii) Data Gathering 

(iii) Data Analysis

(iv) Business Actions

• Data warehouses need few important components to defined while designing implementation of system, such as data marts, OLTP/OLAP, ETL, meta data etc.

WHY? 

• To identify & store the data in effective manner.
• It can help in making decision based on strong data analysis.
• It stores data from various sources with different formats & with help of ETL tools convert this data into standard format.

HOW? 

• The processes are as follow: -

Planning: - 

• It is one of most important steps of process.
• It helps in getting a pathway or the road map that we have to follow achieve our described goals & objectives.

Data Gathering: -

• It is process that involves the collection of data from various source that can be used for data analysis and reporting.
• It involves a wide-range of steps & it is a time- consuming process.
• It needs to identify data that is going to be helpful for organization.

Data Analysis: -

• Once data collected, next step which comes into the picture is data analysis.
• The process of generating & getting meaningful insights out of the day together is known as data analysis.

Business Action: -

• From data analysis further used for making decisions for the organization.
• Higher-level would be efficiency of the business decisions and these decisions are going to decide the future of organizations. 

Components of Data warehouse implementation: - 

1. Data Marts
2. OLTP
3. OLAP
4. Meta Data
5. ETL

1. Data Marts: -

• It is important components of data warehouse.
• It can be the subset of a data warehouse that is focused on particular business line.
• LIKE: - SALES, MARKETING, HUMAN RESOURCES.

2. OLTP: -

• Stands for ONLINE TRANSACTIONAL PROCESSING.
• It deals with processing of transactional data which is frequently changing in nature.

3. OLAP: - 

• It stands for ONLINE ANALYTICAL PROCESS.
• It helps in processing and analyzing the data stored in database.
• It deals with master data which is not frequently changing in nature.

4. Meta Data: -

• The data of data is known as meta data.
• It helps in getting the information about the data.
• For example: - If we have country data, then state data, city data & the area level can be called the meta data of data. 

ADVANTAGES: -

1. Better data management and delivery.
2. Better decision making
3. Cost Reduction
4. Competitive advantages