Data warehouses integrate, aggregate, provide business intelligence, and serve as a data repository. By adopting innovative strategies that leverage cloud architecture, teams can maximize the value of their data warehouse.
With the emergence of cloud computing and other technological advancement for data integration, storage and management, the nature and scope of the data warehousing has changed dramatically. While some companies consider using data warehouses to store all their data, they are still in their element, providing faster access and a more organized structure for day-to-day analytics. In addition, companies can improve their data warehouse strategy by introducing new tools for data preparation, workflow analysis, and master data management. These five strategies will enable companies to take full advantage of the growing cloud data warehouse market.
Companies that first identify the business problem with data and then work towards a solution are more likely to succeed. The main reasons for setting up a data warehouse are
Data standardization: it is easier for managers to analyze the data stored in the data warehouse consistently and extract the necessary information. Standardizing data collected from different sources reduces the risk of error and improves overall accuracy.
Better decision-making: successful companies develop plans and strategies based on data, while many can only make decisions by properly evaluating the data and its conclusions. By providing fast and efficient access to data, the data warehouse gives managers a competitive advantage and enables the development of data-driven plans.
Cost reduction: Data warehouses allow managers to analyze historical data further and evaluate the effectiveness of previous plans. They then identify whether they need to change their strategy to reduce costs.
Which is better, cloud or on-premises data warehouse? A data warehouse is a single data warehouse that combines local and cloud data to facilitate analysis and decision-making. More and more companies are moving from on-premises data warehouses to cloud-based solutions. Local data warehouses allow complete technology control, but all hardware and software must be purchased, installed, and managed in-house. Compliance and control are increased as all data is managed in-house.
Modern cloud storage is scalable on demand, cost-effective (no hardware, server space, IT staff, or operational costs), and includes analytics, authentication, and access control functions. The cloud provider provides security, so start-up costs are relatively low. Another advantage of cloud storage is availability and longer uptime. By outsourcing the maintenance and management of your data warehouse to one provider, you can free up time and resources to focus on analytics and other strategic projects.
The process of importing data from sources, transforming it, and storing it in a data warehouse is called ETL (Extract, Load, and Transform). ETL allows companies to collect and analyze data faster by moving the transformation step to the end of the process. Cloud-based data warehouses are suitable for ETL applications because they provide scalable computing resources for transforming data once it is loaded. There are additional advantages when comparing cloud data warehouses and on-premises data warehouses.
ETL loads the target system before transforming the data, so organizations need data modeling tools to prepare the data for analysis. Subject matter experts can advise engineers to build and manage the data warehouse and provide data in a format that meets the end-user's needs.
Determining the frequency of data uploads is essential in deciding how an organization uses data. Batch processing is a technique that allows large amounts of data to be processed simultaneously by recording multiple transactions over a period of time. Batch processing reduces business costs by not requiring special hardware support for data entry. Real-time processing is another method that requires continuous data input, processing and output. When time is of the essence for a business, real-time processing provides real-time analysis so you can act quickly. Batch processing is efficient for most businesses, but real-time processing has specific applications in business.
Data warehouses collect data from many sources. Analyzing the security requirements of each data source is an essential task for many businesses. Businesses of different sizes should prioritize data security and compliance with legislation. This is why it is important to develop a data security strategy.
The five essential elements that determine the success of the data warehouse project have been discussed in detail in the previous section. These practices include adopting an agile approach, modifying data collection methods, and considering data warehouse and ETL issues.
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