It is becoming increasingly difficult to scale and copy data from multiple data sources in multiple organizations in multiple locations as the trends of globalization sets in. The biggest challenges companies are facing is the management, retrieval and security of this data. Minor data mismanagement: such as data leaks or even unavailability of data for critical decision making in real time can cost a company billions. The only answer to all such problems is just 2 words: ‘Data warehouse’, which is the best traditional solution for data integration.
What is a Data warehouse (DW)?
It is a centralized data repository that integrates data from various transactional, legacy, or external systems, applications, and sources. The data warehouse provides an environment separate from the operational systems and is completely designed for decision-support, analytical-reporting, ad-hoc queries, and data mining. This isolation and optimization enables queries to be performed without any impact on the systems that support the business’ primary transactions (i.e transactional and operational systems).
A data warehouse is best defined by the type of data it stores, and the people who use it. It is typically read-only, with the data organized according to business requirements, rather than by computer processes. The data warehouse classifies information by subjects of interest to business analysts and managers — for example, customers, products and accounts. The data is inserted (or loaded) into the warehouse, then made available for querying by business users. Redundant data is often included in a data warehouse in order to provide users with multiple views of information that present it in logical, easily understood groupings.
How companies use data warehouses?
The data warehouse supports online analytical processing (OLAP), which enables high-level end users to gain insight into business operations through interactive and iterative access to the stored data. This enables business executives to improve corporate strategies and operational decision making by querying the data warehouse to examine business processes, performance and trends.
There are many benefits to deploying and effectively using a data warehouse for example, a data warehouse can be used to perform the following tasks:
- Track, manage and improve corporate performance.
- Monitor and modify a marketing campaign.
- Review and optimize logistics and operations.
- Increase the efficiency and effectiveness of product management and development.
- Query, join and access disparate information culled from multiple sources.
- Manage and enhance customer relationships.
- Forecast future growth, needs and deliverables.
- Cleanse and improve the quality of your organization’s data.
Benefits of Data Warehouses
- Improved Business ProcessesA data warehouse platform can deliver a practical way to view the past without affecting the daily operations of the business. By querying and analyzing data in the data warehouse, organizations can improve operations and enable more efficient business processes, thereby increasing revenue and raising profits. Additionally, the data warehouse allows for processing of large and complex queries in a highly-efficient manner. Upon successful implementation of a data warehouse or data mart, business will realize numerous improvements and positive gains.
- Business IntelligenceImproved information access allows deeper insights. This allow management to make informed well thought decisions rather trusting their instincts. Decisions that affect the strategy and operations of organizations will be based upon credible facts and will be backed up with evidence and actual organizational data. Customized and well arranged data will allow better decisions as it gives the opportunity to query actual data retrieving information based upon the personal data needs.
- Increased System PerformanceData warehouses are intelligently designed to allow thorough data drilling, analytics, data retrieval based on complex algorithms and high speed of data retrieval and analysis. They are designed to store large and complex volumes of data and are able to rapidly respond to data requirements. These analytical systems are constructed differently from operational systems which focus on creation and modification of data that allows for a large system burden to be taken off the operational environment and effectively distributes system load across an entire organization’s technology infrastructure.
- Data Extraction from Multiple ResourcesGlobalization has diminished the physical boundaries of the world. Organization working in America might have its support call center based in Africa, where its manufacturing might be happening in Pakistan. For such organizations, enterprise information systems are comprised of multiple subsystems, physically separated and built on different platforms. The biggest challenge such companies face is to merge the data from multiple platforms to conduct the analytics for business intelligence. To solve this problem, the data warehouse performs integration of existing disparate data sources and makes them accessible in one place.
The data warehouse stores large amounts of historical data and can enable advanced business intelligence including time-period analysis, trend analysis, and trend prediction. The advantage of the data warehouse is that it allows for advanced reporting and study of large scale of multiple time periods.
Companies who want to create a data warehouse can start by figuring out what their business objectives are and how having a data warehouse will contribute to these goals. Next, they should choose a database management system for their data warehouse. Then, they need to construct a data model and plan the data transformation. Afterwards, businesses need to begin testing the plan to see what needs to be tweaked or changed.
Companies must invest in knowledgeable programs on Data warehousing to learn how a data warehouse fits into the overall strategy of complex enterprises, how to develop data models useful for business intelligence, and how to combine data from disparate sources into a single database that comprises the core of their data warehouse.