Understanding Data Warehousing In Data Science

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A data warehouse system is also called a decision support system, analytic application, business intelligence system, decision support system, and executive information system. It provides new designs which reduce response time and enhance query performance for analytics and reports.

How it works

Data warehousing is more of a central repository that receives information from one or more sources, such as transactional systems and other databases. The data received may be structured, semi-structured, or unstructured. It is then processed and transformed to make it possible for users to access it using business intelligence tools, spreadsheets, and others. In short, warehousing merges information from the various sources into a single comprehensive database. With the information available in one place, it is easier for an organization to analyze customers easily and holistically. It makes data mining possible and ensures all information available is considered before any action is taken.

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Data warehousing types

Enterprise data warehousing –

it is centralized warehousing that offers decision support services across an enterprise using a unified approach of organizing and presenting data. This type of warehousing also makes data classification according to subject possible and provides access according to the same divisions.

Operational data store –

in the operational data store, the warehouse refreshes in real-time, making it very effective for routine activities like storing employee records. The data store saves the day when data warehouse and OLTP systems do not support the reporting needs of an organization.

Data mart –

this is a data warehousing subset designed for specific lines of business like sales or finance. A data mart can be independent, meaning it collects the data directly from the targeted sources.

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Data warehousing components

Data warehousing is made up of four components, as highlighted below.

1. Load manager –

It is the front component that performs all operations associated with data extraction and loading into the warehouse.

2. Warehouse manager –

it manages the data coming in and handles operations like data analyzing to ensure consistency, generation of aggregations, views and indexes creation, transformation and merging of the data sources, data back-up, and archiving.

3. Query manager –

it is the backend component that manages user queries. This component basically arranges the direct queries into appropriate tables and schedules their execution.

4. End-user access –

this component handles access tools for the end users, which include query tools, data reporting, EIS tools, application development, data mining, and OLAP tools.

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Who uses data warehousing?

Date warehousing is beneficial to users who rely on large amounts of data to make decisions and those who use customized processes to get information from multiple sources. The warehousing also benefits people looking for a systematic approach to making decisions and simple technology for accessing data. It is used in various industries to streamline data handling process, and the most common industries using the systems include:

Airlines –

for analyzing route profitability, crew assignments, and frequent flyer programs.

Banking –

for managing resources, market research, and performance analysis of the operations and products.

Healthcare –

for strategizing and predicting outcomes, generating treatment reports for patients, and sharing data with insurance companies.

Public sector –

for gathering intelligence which helps government agencies maintain and analyze individual tax and health policy records.

Telecommunications –

for making sales and distribution decisions and handling product promotions.

Retail chains –

for marketing and distribution of products, item tracking, promotions, customer buying pattern, and pricing policy determination.

Hospitality –

for estimating and designing promotion and advertisement campaigns based on client travel patterns and feedback.

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Data warehousing advantages and disadvantages

The good

• It allows business users to access critical data quickly and from multiple sources in one place.

• It offers consistent information on cross-functional activities and also supports ad-hoc queries and reporting.

• It integrates multiple data sources, thus reducing stress on the available production system.

• Data warehousing also helps in the reduction of total turnaround time for data analysis and reporting.

• It makes it easier to use analyses and reports through proper restructuring and integration.

• It stores huge amounts of data, thus helping users to make analyses from different periods and trends for almost perfect future predictions.

The bad

• Data warehousing is not as ideal for unstructured data and can be outdated relatively fast

• It is difficult to make any changes needed in data ranges and types, indexes, queries, and data source

• It can be a complex affair for average users

• Project scopes always increase no matter the amount of effort put into project management

• Users sometimes develop different rules for business

• It requires training for proper implementation, and this calls for adequate resources from an organization

Data warehousing best practices

The advantages of data warehousing outweigh the disadvantages, and hence many organizations will always gravitate towards the process. If you decide that warehousing is what your organization needs, you must give your best for it to work well for you. To make data warehousing work for you: