Data warehousing refers to the technique of construction with the aid of a data warehouse. It is constructed by the integration of different data from several heterogeneous sources which bestow support to ad hoc queries, structured queries, analytical reporting. Data warehousing involves the integration of data, cleaning of data, and consolidation of data.

Steps for the implementation of data warehousing

Here is a list of the steps for the implementation of data warehousing

Determining the objectives of the business

The business is the stage of rapid growth and you will be requiring the right combination of support, production, sales, and administrative personnel. The integral decision-makers need to gain information if the rise in the overhead staff offers any value to the business.

As the business increases the sales force as well as employ various sales modes, the leaders should gain information about whether such models are efficient. The forces of the external market are changing the right balance between the regional and national focus and the leaders should understand the effect of this change on the business.

While working with the management team, it is a prerequisite to gain an understanding of different quantitative measurements of the activities of the business which are used by the decision-makers. Such measurements are recognized to be the key performance indicators for measuring the activities of the business.

Collection and analysis of information

The best way for collecting the performance information is by asking a specific question. The leaders have the right source of information which helps in making specific decisions.

You need to begin with the data sources. It is possible to get the reports from the customer relationship management application, accounting package, time reporting system, to name a few. You will require copies of different reports and their sources.

The summary and analytical reports, created by supervisors, analysts, and administrative assistants may include specific details, which were overlooked with the existing information and software, which was stored in memos and spreadsheets.

A key part of the collection and analysis stage is gaining an understanding of how to collect and process the information. The data warehouse may automate different reporting tasks. However, it is not possible to automate anything which is not understood or identified. The process demands extensive interaction between different individuals. You require listening and repeating everything you have heard. It is a prerequisite to gain a clear understanding of the process and the causes of existence.

Identification of the core processes of the business

Now, you already have a clear and specific idea about different processes of the business. You are already aware of the integral performance indicators like units produced, unit sales, and gross revenue. After this, you require identifying various entities which are related to the creation of vital performance indicators.

The data warehouse contributes to being the collection of the different interrelated structure of data.

Every structure is responsible for the storage of integral performance indicator for a certain business process and interlinking the indicators, to the factors, generating them. For designing a specific structure for tracking the process of the business, it is essential to identify the entities which work in a combined way for the development of key performance indicator.

Development of conceptual data model

Once the different processes of a business are identified, it is possible to develop a conceptual model of data. Thus, you will be capable of determining the specific subjects which will be expressed in the form of dimensions and fact tables, related to the factors.

You require identifying the integral performance indicators for every process of business and finding the specific format for the storage of the facts. Though the procedure may appear to be simple, it is not so. The facts are combined for the formation of OLAP cubes and the data should be an inconsistent unit of measure.

It is essential to interlink the dimensions of integral performance indicators. The interaction of specific entities leads to the generation of every row in the fact table. For adding the fact, you require populating different dimensions and correlation of the activities. A bunch of data systems may possess incomplete data. Once the rectifications are done, it is possible to construct the fact and dimension tables.

Locating data sources and planning the transformation of data

Now that you are clear about your requirements, you have to achieve them. You require identifying the source of the crucial information and the options for the movement of the data in the structure of the data warehouse.

It is a prerequisite to moving the data in the consolidated and constant data structure. A difficult task involves the correlation of information between the time reporting database and in-house CRM.

The system should not be sharing any details like customer number, employee number or project number. In this specific phase, you require making plans for the reconciliation of data in the various databases for correlating the information as it is copied in the tables of the data warehouse.

Setting the tracking duration and implementation of the plan

The structures of the data warehouse consume a huge volume of stored data. Hence, it is necessary to determine the way for archiving of the data, with the passing of data. Data warehouse helps in retaining data at different levels of granularity or details. It is a prerequisite to ensure the consistency of granularity via a single data structure. With the aging of data, you require summarizing or storing the data with fewer details in the other structure. The data can be stored for two years in day grain after which it should be moved to the other structure.

Now that the plan is developed, the effective strategy involves planning the whole warehouse and implements a specific part as the data mart for demonstrating the capabilities of the system.

There are a variety of decision support technologies which assist in the utilization of the data, which is present in the data warehouse. Such technologies help in using the warehouse efficiently and quickly. They collect the data, analyze the same, and take the right decisions, following the information which is available in the warehouse. The information, collected from the warehouse are beneficial in customer analysis, tuning the strategies of production, operations analysis.