Once you know what you know exactly which kind of data is brought over and exactly how the data is transformed you can absolutely automate it, furthermore, utilizing a data warehouse can help ensure that the data being analyzed is accurate and consistent.
Infrastructure, ensuring data quality, understanding the source of data and business rules, managing the metadata and extracting and transforming it to standardized formats for a data warehouse, big data will have to become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus—as long as the right policies and enablers are in place, also, appropriately defined and arranged process of benchmarking and tracking KPIs lets users to further improve and keep each quality assurance and business processes in the warehouse in a good shape.
As a function related to security, a data integrity service maintains information exactly as it was inputted, and is auditable to affirm its reliability, data scrubbing, also called data cleansing, is the process of amending or removing data in a database that is incorrect, incomplete, improperly formatted, or duplicated. In summary, the data warehouse project manager must embrace new tasks and deliverables, develop a different working relationship with the users, and work in an environment that is far less defined than with traditional operational systems.
Will have to be used mainly for capturing all the data from main transactional database in time dimension, archive data consists of older data that is still important to your organization and may be needed for future reference. As well as data that must be retained for regulatory compliance, likewise, without testing, the data warehouse could end up producing invalid results and as a result lose the trust of the end business users and decision makers.
At some point, business analysts and data warehouse architects refine the data needs, and data sources are identified, it is clearly unacceptable to wait until the end of the day (or week) to load data into a real-time data warehouse with extreme service levels for data freshness, especially, warehouse however, is used to store historical information in databases captured from diverse sources for the purpose of aiding tactical or strategic decision making.
Due to the increasing demand for real-time data-driven decisions, timeliness is the most important dimension of data quality, by ensuring that quality data is stored in your data warehouse or business intelligence application, you also ensure the quality of information for dependent applications and analytics. As well as, new technologies are making it easier to access important information about your operations, performance and most importantly, your customers.
An expectation of completeness indicates that certain attributes should be assigned values in a data set, when you query on a previous value of a dimension attribute, you loose new data, and when you query on a newer value, you loose previous data, also, like other modeling artifacts data models can be used for a variety of purposes, from high-level conceptual models to physical data models.
Master data management (MDM) is the core process used to manage, centralize, organize, categorize, localize, synchronize and enrich master data according to the business rules of the sales, marketing and operational strategies of your company, strategic decisions made on the basis of information from the data warehouse are likely to be more far-reaching in scope and consequences. In this case, instant access to application data via pre-built adapters to integrate data from various sources.
Want to check how your Data Quality Processes are performing? You don’t know what you don’t know. Find out with our Data Quality Self Assessment Toolkit: