It goes without saying, it is in the best interest of every organization to stay competitive.
That being the case, while most organizations are aware of the power of data and analytics in the 21st century, not every organization is capable of utilizing it fully.
The gathering of data from various business units and its analysis to derive business insights are vital components in business intelligence systems. Unless the enterprise data is gathered appropriately, it is impossible to extract “meaningful” information.
This requirement is typically facilitated by an enterprise data warehouse, or EDW, which is a centralized repository of data from multiple sources. All of which is curated with the ability to scale easily under ideal conditions. The EDW must also provide both current and historical data in a single data platform to solve the challenges often observed in traditional data warehouses. This means that the business users across the enterprise are empowered to create what is needed with the increased availability of data.
BENEFITS
- Historical data analysis
- Data consolidation from varied data structures
- Enhanced data consistency and quality
- Faster and more informed decision making
- Boost data availability and efficiency using self-service BI capabilities
- Monitor business performance
- Data security and privacy
MODERN DATA WAREHOUSE
The emergence of new technologies like data lakes, data streaming, Lambda & MPP architectures over the last few years have paved the way for new methodologies. At the same time, previous industry buzzwords like big data, cloud computing and advanced analytics have all played major roles in the development of the modern data warehouse.
For companies that have begun to use cloud-based solutions, upon migration to the cloud, they’re able to notice a huge shift in achieving cost-effective scalability. This is done as the volume of data increases, requiring more efficient methods of utilizing data and resources.
At Scalefree, we believe the success of a modern data warehouse also depends on the ability to also rethink the overall data strategy. This includes everything encompassing data architecture, data modeling, data ingestion, data quality or governance.
Aided by our broad expertise in the BI field, we’re here to help you understand the structure of your data.
By leveraging your chosen data platform, our consultants deliver business value by adopting industry best practices like data warehouse automation and data virtualization to provide business data within your new data warehouse architecture.
ROLE OF DATA VAULT 2.0 in EDW
Most traditional data warehouse architectures were unsuitable for fast-changing business conditions. Data Vault then emerged as a modern hybrid solution to address both the traditional and the modern data challenges of enterprises. Within it, were the necessary governance and structure to capture personally identifiable information, thus ensuring compliance with regulations.
Interested in data warehouse automation? Check out our exclusive page on Automation.
Data Vault sits right at the heart of the modern enterprise data warehouse, offering four key functionalities in its toolbox:
- A flexible yet unique data modeling technique that decouples raw data from cleansed business data
- A scalable data architecture to ingest high-volume, high velocity data along with capabilities to processing semi-structured and unstructured data
- A project execution methodology based on agile principles, CMMI, TQM, and Six-Sigma that supports incremental DevOps
- An implementation framework that supports automated generation of repeatable, pattern based data load processes
Not fully taking advantage of your implementation of Data Vault 2.0?
Scalefree is happy to support your project with our expert Consultants who are Certified Data Vault 2.0 Practitioners.
DATA WAREHOUSE AUTOMATION
Data warehouse automation is a game changer in modern data warehousing especially for those using Data Vault 2.0.
Data Warehouse Automation provides an agile platform to enable organizations to increase their business agility and time-to-market by automating the whole life cycle of data. Not only does it accelerate the data warehouse development cycle but also supports data quality and consistency, efficient metadata management and documentation of the source, EDW model, and ELT/ETL processes.
RECOMMENDATION
Is your enterprise data model agile?
How long does your team spend to compute a new KPI?
Is your data warehouse model compatible with big data?
Do you spend too much on just ingesting raw data from a new source into your data warehouse?
If any of these questions resonate with you, then you must reconsider the design of your enterprise data warehouse. At Scalefree, we provide you real industry expertise to address these questions and much more!