Ensuring managers have easier and faster access to information which supports the decision-making process is the key to achieving success. Unfortunately, in many organizations, systems aren’t integrated, but rather data is distributed, which makes access and proper analysis extremely problematic.
That is why it is necessary to create a data warehouse that acquires the data from the source system and transforms it in a way that it can be used in the business decision-making process.
The data warehouse gives you information that reflects the tendencies and changes in your organization, and you will have access to a system that supports managers and management in strategic and tactical decision-making as well.
A data warehouse allows you to conduct analyses that include data even from a few years back. Moreover, the collected data is organized, archived and stored in a central database, which increases control of access to data and improves security.
Warehouses are not a ready product that can be purchased from the supplier. The creation of a data warehouse is a complex process that should be carried out by Business Intelligence specialists with the experience and skills to work with dedicated tools.
Stages Of Data Warehouse Implementation
- Analysis and design
The collection of business requirements describing the way the warehouse will operate in the future; the review of source data, the specification of data formats and the selection of tools used to design a warehouse, which should be the result of the analysis.
- Creation of the data warehouse
Creating a physical data warehouse structure that will store integrated data from various sources (transaction systems database – e.g. ERP, CRM, SCM; files; various types of services); documentation is also prepared during the data warehouse creation process.
- Supplying the data warehouse
Implementing the ETL process and setting the automatic refreshment of the warehouse in accordance with the timetable.
- Warehouse testing
The verification of data.
- ImplementationTraining end users and administrators.
In addition, the warehouse is separated from operational systems, so the system does not get slowed down. The data warehouse also makes it possible to add and read multiple records in a unified format, despite the fact that the data originates from various sources.
There are various ways to analyze the data contained in the warehouse, which are chosen depending on current needs.
- cyclical, standard reporting with a consistent structure, whereby the user can change the filter criteria,
- user-defined reports and queries using SQL technology or by means of an automatic report generator,
- statistical analysis allowing for the determination of trends, forecasting and much more,
- multidimensional On-line Analytical Processing (OLAP),
- data mining, which facilitates the projection of company and market behavior,
- business modeling, the prediction of business effects.
Data warehousing specialists from JCommerce have implemented various types of data warehouses, which is why we know how helpful it is for users to have organized and archived information. Over time, information needs increase, which is the natural effect of the growth of the company, so warehouse end-users should stay in touch with BI specialists to regularly verify their business needs.
Data Warehouse – OLAP
OLAP is a multidimensional analysis, made possible by browsing a large amount of data in a fast and interactive way. It provides ready-made reports or analytical cross-sections in a short time, reflecting the multidimensional structure of the company.
Multidimensional modeling and multidimensional databases are the basis of the analytical processing system because they enable the discovery of data contained in the warehouse from different perspectives, thanks to which the results are presented in a clearly comprehensible manner. Furthermore, OLAP tools provide convenient navigation through the database structure and the creation of cross-sections of selected dimensions, moving them in the report using pivot tables. The user also has the opportunity to create queries, presentations, and to process the results in the form of diagrams or charts.
Providing the highest quality of information is made possible through:
- Data mining
Provides more detailed data about the examined features, going from general to specific.
Allows users to analyze data from a wider perspective by aggregating data.
Limiting the studied phenomenon according to the specific features.
The ranking of attributes.
- Arithmetic, statistical, and econometric operations and selecting the smallest, largest values, sorting and generating indicators.
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Data Warehouse – Data Marts
Data marts are the subsets of data warehouses or independent databases which reap data from the warehouse. Data marts have a narrowed subject matter and operate in a selected business area, e.g. the marketing or accounting department. Data marts should be consistent with the main warehouse, making the warehouse a reliable source for the data marts. ETL processes play an important role here, enabling the integration of data from various sources, as well as transformation and loading into the target database.
The separation of subject matters in the warehouse allows the user to:
- speed up operations without placing an additional burden on the main data warehouse,
- analyze data from specific sources which is important from the point of view of various departments,
- store more detailed data and ready aggregates to facilitate and speed up work,
- increase the efficiency of data downloading,
- help employees in business positions understand the structure of data better; they don’t have to spend too much time getting to know the intricate data structures.
Business Intelligence Specialists From JCommerce
Business Intelligence specialists from JCommerce have the necessary knowledge of technology in the scope of the creation of data warehouses, and have vast business experience gained during business analytics projects for international clients. Our specialists are certified and have many successfully implemented data warehouses to show for their efforts.
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