A data warehouse is home for all your data.
Combine data from all your important information systems into one place where it's easy to access and utilize. Using a data warehouse makes business analytics, forecasting and planning easier and more efficient.
Building an efficiently working data warehouse is routine for QuickBI experts. With more than 300 integrations, we combine all the data you need from your company's various systems to your data warehouse and your BI system.
Agile BI reporting and data analytics
By integrating all of your data into a data warehouse, you can automate parts of your reporting process, and you also get a great tool for testing your data and creating and quickly verifying new data models and analyzes.
Centralized data is easy to utilize
We gather all the data sources needed for your reports and analytics into a data warehouse. The data is formatted to be easy to use with the BI tools you choose, and system integrations are configured to update everything automatically.
Much more than just a data warehouse
Data warehousing is an integral part of fast, reliable and comprehensive reporting. QuickBI can build a seamless, efficient reporting and analytics system for your business, where all data flows effortlessly and securely from source systems to the data warehouse and further to the BI tool of your choice.
Deployment and maintenance of the data warehouse
When you buy your data connectors from QuickBI you will have Google BigQuery included in your plan.We take care of maintenance of the data warehouse.
BigQuery, part of Google's cloud services, is a highly scalable data warehouse for businesses of all sizes. It is not too robust a tool to take advantage of relatively small amounts of data, but it also works well with considerable amounts of data.
BigQuery is a scalable and cost-effective solution also in terms of pricing, as it is based on the use of the product and services. The price depends on the number of analytics processes and the storage space required.
BigQuery is an excellent basis for reporting and machine learning models. The data in the data warehouse can be processed and analyzed with the desired BI tool. As a service provided by Google, BigQuery is also a good option if the company wants to keep its cloud data within the borders of Finland.
What is a data warehouse?
A data warehouse (eng. data warehouse, DW) is an information system in which large amounts of different types of data can be collected and stored for the organization's needs. A data warehouse usually consists of databases in which data is stored according to a specific data model. The data is organized and processed in such a way that it can be easily and effectively used for business monitoring and analysis.
In the data warehouse, data from several different source systems of the company can be combined, which enables more complex analyzes and, for example, the detection of trends. The data warehouse offers users a uniform, comprehensive and up-to-date view of the company's data. It can be effectively utilized by using various reporting programs and BI tools.
Benefits of data warehouse
The information collected from different systems and apps offers the opportunity to analyze the business more comprehensively, to deal with problem areas and to make better predictions about the future.
The use of a data warehouse reduces the costs associated with data processing and management, because the data is centrally available from one place and no time is spent collecting fragmented data from several different sources.
Advantages of cloud storage
The latest technologies, for example machine learning, can be integrated into the cloud data warehouse, which makes its use easier and more efficient. Cloud data warehouses using in-memory technology can offer extremely fast data processing speeds.
Cloud data warehouse service providers follow strict, up-to-date information security practices. They also take care of data encryption and data decentralization and backup, which minimizes the risk of data loss.
Why join data while it is in the data warehouse?
More information available and a reduced amount of manual work needed
- More accurate cashflow forecasts
- Being able to forecast financial key figures by utilizing data collected from f. ex. sales and / or production related information systems / software
- Planning a budget
- Monitoring the planned budget
- Compiling billing material
- Automatic salary data collection
- Automatic accounting voucher data collection
Surely by now you are convinced that a data warehouse is by far the most efficient way to manage data? Contact us and we'll talk more.
The components of data warehousing
The architecture of the data warehouse
The operational layer, which contains raw data from the organization's source systems. The data in this layer is stored as is without any changes.
The transformation layer, where data is cleaned, transformed and combined with data from other source systems. This layer also contains metadata that helps to understand the structure and meaning of the data.
An analysis layer where the data is organized in an easy-to-understand format according to the users' needs. This layer contains various tools that users can use to view and analyze data.
Terms related to data warehousing
Big data refers to very large, diverse and rapidly growing amounts of data that cannot be processed using traditional methods. The material can be structured or unstructured data; spreadsheets, images, audio, text or any other files. The information obtained from Big Data can be used, for example, for forecasting, customer behavior analysis, business optimization and research work.
The data model defines the structure and content of the data warehouse, i.e. the types of data to be stored in the data warehouse and their relationships to each other. The data model ensures that the information in the data warehouse is consistent so that it can be used correctly and efficiently for reporting and analytics.
A database is a data structure designed for the systematic storage and management of data. When data from several different source systems is typically integrated into the database, data from only one specific area is usually stored in the database. The database is located on a server that serves as a storage space and allows several different users to access and manage the database information. Commonly used database servers are e.g. Microsoft SQL Server, Oracle Database, MySQL and PostgreSQL.
A data lake is a broader and more versatile concept than a data warehouse, and it can include, for example, data warehouses, cloud storage solutions, Big Data systems, NoSQL databases, etc. While a data warehouse usually stores mostly structural and transactional data from business systems, a data lake often stores diverse data from different sources, in many different file formats. The purpose of a data lake is to provide users with a wider selection of data sources that can be used with multiple analytics tools, while a data warehouse is usually designed for a specific group of users for specific types of analysis. A data lake offers a flexible way to process data in real time.
Data marts are smaller data warehouses, parts of the data warehouse, designed to serve a specific business area or unit (for example, production, marketing or sales). Data marts can be used to offer data warehouse users customized and easy-to-understand reports and views. Data marts can be used to reduce the load on the data warehouse and speed up users' access to information
A sandbox is a test or development environment where users can experiment and process data in a data warehouse without affecting the production environment. In the sandbox, new information systems, reports, visualizations or other analytics tools can be tried out before they are published for production use. The sandbox helps reduce risks and errors that could cause disruptions in the production environment when new systems or processes are implemented.