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With the increased need for data, organizations are looking for advanced solutions. An Enterprise Data Warehouse is a known term across organizations. EDW can be referred to as the centralized representative for data, which supports any organization’s decision-making process. It provides a multitude of benefits like analyzing data, maintaining data consistency, faster access to data, data-driven insight, and ensuring data security. However, when it is about developing a robust Enterprise Data Warehouse from the very scratch, it is a challenging and complex task. It requires design, planning and proper implementation.
In this article, we will mainly focus on the best practices and the major steps that you need to take to develop an effective and efficient EDW.
Step 1: Analyzing the current data and creating a solid objective
The very first step to developing EDW is analyzing the data situation of the organization and then creating a solid objective for the respective project. The step is very important to make sure that the technology remains aligned with the business vision strategy which will help to further address the gaps and loopholes. In order to do this, the following aspects are needed.
- You need to identify data types, formats, quality, and sources of the existing data, as it will help you to clearly understand the data issues and landscape that require resolution.
- You must access the data requirements for the users and stakeholders, as it will help you gain valuable insight regarding the data use cases and expectations it will support.
- You also need to determine the key performance indicators and metrics that the technologies support, as they will help you measure the impact and success of the EDW project.
- You must create a solid business value as it will help justify the resources and investments needed for the project.
Step 2: Master data management
The second most important step is Master Data Management or MDM. This is known to be a crucial step for building an Enterprise Data Warehouse with the support, business analytics and intelligence that the organization requires. It generally involves the following tasks.
- There is a need to define master data attributes, relationships and domains. It means to identify core data entities in the organization shared across different business systems and processes like supplies, products, customers, etc. It also requires defining the association and properties.
- Implementation of data standards, policies, and rules, which will require organizations to establish and apply methods and criteria to ensure the completeness, consistency, accuracy, validity, and timelines of master data.
- Creating and maintaining a data catalog and dictionary means managing and documenting master data like sources, definitions, formats, descriptions, transformations, and data element usage. It also provides an understanding and vocabulary of master data.
- Developing and enforcing data responsibilities and roles means assigning and empowering people who are accountable for the maintenance, governance, and creation of master data. Data will be the people responsible for ensuring integrity, security and quality of data along with resolving conflicts and issues.
Step 3: Creating a data integration architecture
Data Integration Architecture is the most crucial step while developing EDW. It supports data reporting and analysis for the organization. It involves the following tasks.
- There is a need to choose the right data integration, tools, technologies and platforms. Selecting appropriate hardware and software components is necessary for enabling the ETL process. The choice of technologies, tools and platforms will depend on several factors like variety, velocity and volume of data along with the frequency and complexity.
- Designing, data, integration, workflow, pipelines and processes. It means defining the sequence and logic of ETL operation operations that extract data from source systems, transform data as per the business rules and quality standards, and load data into EDW. The workflow design, pipelines and processes must be considered according to the data constraints and dependencies.
- Selecting data, integration methods, techniques, and patterns means there is a need to choose the best approaches and practices for implementing the ETL process. The selection of techniques, methods and patterns generally depends on the objectives and characteristics of data integration projects.
Step 4: Properly set up the integration and ETL process
ETL process and integration is known to be a valuable step while developing EDW, which supports data driven decision-making of any organization. It involves the following tasks.
- Extracting data from the source system by making use of different adapters and connectors. It means assessing and retrieving data from variable data sources like web services, files, databases, APIs, and much more by making use of tools and technologies for connecting and communicating with source systems.
- Transforming data as per the business, logic, and rule means applying necessary functions and operations to extract data and make it suitable for EDW. The transformation might include data validation, data cleansing, data enrichment, data standardization, data deduplication, data aggregation, data encryption, data masking, and much more.
- Loading data into EDW using different methods and modes. It means inserting and updating data into targeted schemes and tables of EDW. It will require different methods and modes based on the requirements and constraints of the data integration project.
- Monitoring and troubleshooting integration and ETL process means properly tracking and measuring the status and performance of the process and also identifying and resolving issues and errors that might occur. The troubleshooting and monitoring might use different techniques and tools like dashboards, logs, alert notifications, etc.
Step 5: Integration of Data Visualization Tools
Data Visualization Tool is known to be the final step of the enterprise data warehouse development process. It involves the following steps.
- Choosing the right data visualization tools, technologies, and platforms means selecting the right hardware and software components, which will help users interact with data in EDW. The choice of tools, platforms and technologies is completely dependent on different factors like the format, type and data size.
- Designing reports, charts and dashboards of data visualization. It means clearly defining the structure, layout and style of data visualization displaying data. The design of reports, charts and dashboards must consider data analysis, objectives, best practices and user expectations and preferences.
- Configuring data visualization permissions, access and security, which means managing and setting up authorization, authentication and encryption mechanisms for protecting visualization and data in EDW. The configuration must consider data confidentiality and sensitivity, user responsibility and roles and data compliance and governance.
- Training users on how to use data visualization tools means providing necessary knowledge and support resources to help users efficiently and effectively use the data visualization tool.
Conclusion:
Developing an Enterprise Data Warehouse requires valuable and strategic initiative to help organizations leverage the potential of data-driven information. However, the systematic approach requires choosing the right enterprise with comprehensive developing knowledge. Choosing Hexaview Technologies can be beneficial as they have some of the best professionals to handle your requirements and help you fulfill your objectives.