Data warehouse
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
High quality data
The data warehouse enables uniform storage and processing of data, so the data is transparent, traceable and valid.
Faster and easier reporting
The data stored in the data warehouse is already optimized for reporting and analysis.
More versatile analytics
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.
Agile testing and development
New perspectives can be tried out more easily and new reports can be created more nimbly.
User-specific customization and sharing
Reports can be created for different users and user groups that collect all the information they need from different systems.
Better decision making
When using a data warehouse, the company gets a better overall picture of its business, which helps to make better decisions.
Cost savings
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
Flexibility and scalability
The data warehouse located in the cloud can be expanded, its performance improved and the structure changed more nimbly than a traditional on-premise server.
Cost savings
Total usage costs are usually lower when using a cloud data warehouse, as the company only pays for the resources it needs at any given time.
Fast onboarding
The cloud data warehouse can be deployed quickly, because there is no need to set up an infrastructure.
The latest technology
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.
User friendliness
Cloud data warehouses offer versatile tools and functions for making analyses. Integrating new applications and data sources is easier.
Automated maintenance
The service provider takes care of the maintenance of the cloud database, updates and the correction of possible defects.
Data security and backup
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
More diversified reporting through utilization of several data pipelines
- Monitoring sales profitability
- Activity reporting
- Monitoring efficiency of salespeople
- Sales pipeline reporting and forecasting
- Calculation of customer life cycle value
- Related expense tracking
- Reclamation reporting
- Customer satisfaction
Data from different systems and software to be used for marketing purposes
- Customer segmentation based on purchasing behavior
- Identification of established and profitable customer segments
- Calculating the cost for customer acquisition between different marketing channels
- Customer purchasing behavior reporting based on where or through which channel the lead was acquired from
- Timing marketing campaigns based on seasonal variation
It is possible to make the work of human resources easier by collecting data from different sources. For example:
- Organizing available human resources according to a production plan
- Monitoring the live implementation of a working hours budget. For example, a frequently updatable view of a working hours actualized vs budget by month.
- Monitoring the ratio of billable work in relation to total working time
- An automatically updated "presence" calendar along with calculation of daily resources, taking into account holidays, sick leave, trips, training days, etc.
Comprehensive reporting enables improving the efficiency of production
- Reducing production material losses with better visibility towards ensuring that the materials are used before they go bad / turn sour
- Comparison of component inventory levels to an existing production plan. Monitoring the inventory levels of critical components
- Monitoring work efficiency, for example between different production lines and / or products
- Material loss monitoring and analysis
- Comparison of planned and actual profit margins
- Calculation of activity based costs. For example, linking the costs of logistics, management and sourcing on existing products
A better understanding of customer service can be gained by combining data from multiple sources
- Tracking the ratio of deferred / cancelled customer appointments by customer service representative
- Proper allocation of resources according to level of busyness and time of day
- Response time monitoring
- Monitoring service ticket queue status / situation
- Customer satisfaction
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
- Source systems from which data is collected, e.g. databases, applications, websites, cloud services.
- The ELT process (Extract, Load, Transform), where data from source systems is copied, loaded into the data warehouse and transformed to suit its purpose. The more old-fashioned way is ETL (Extract, Transform, Load), where the data is transformed before loading it into the data warehouse.
- A database containing collected and edited data. The data warehouse database is designed to support fast queries and analytics.
- Metadata - information about the context, content and structure of the data. It helps in using and interpreting the data.
- BI tools to view, analyze and report data in the data warehouse.
- Data security, which includes e.g. access rights management, encryption methods and other security measures that help protect data in the data warehouse.
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.
Data warehousing - best practices
- Design the data warehouse carefully, taking into account the needs and goals of the entire organization and its various users and teams.
- Make sure that the tools and applications chosen to use the data warehouse are compatible and easy to use.
- Ensure data quality – the data stored in the data warehouse must be clean and consistent.
- Use standardized metadata. It is a prerequisite for efficient management and maintenance of the data warehouse.
- Create information security policies and ensure that they are followed.
- Train users so that they have a sufficient understanding of using the data warehouse, as well as its benefits and possibilities.
- Make a maintenance and service plan to ensure that the data warehouse remains functional and efficient. Maintenance includes, for example, backups, technology updates, monitoring data integrity, managing access rights, responding to feedback, and servicing and repairing equipment.
Terms related to data warehousing
Big data
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.
Data model
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.
Database
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.
Data lake
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 mart
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
Sandbox
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.