Enterprise Data-warehouse (EDW) In Easy Steps

Transcription

1 Enterprise Data-warehouse (EDW) In Easy Steps Data-warehouses (DW) are centralised data repositories that integrate data from various transactional, legacy, or external systems, applications, and sources. The data-warehouse provides an environment separate from the operational systems and is designed for: Decision Support Analytical Reporting & Analysis Ad-hoc Queries Data-mining This isolation and optimisation enables queries to be performed without any impact on the systems that support the business transactional, financial and operational systems etc. Fundamentally, a data-warehouse helps solve the on-going problem of pulling data out of transactional systems quickly and efficiently and converting that data into actionable information. Additionally, the data-warehouse allows for processing of large and complex queries in a highly-efficient process. Upon successful implementation of a data-warehouse, organisations very quickly reap the many positive business gains.

2 Benefits from a successful implementation of a data warehouse include: Enhanced Business Intelligence & Analytics Increased Query and System Performance Business Intelligence from Multiple Sources Timely & Faster Access to Data Enhanced Data Quality and Consistency Historical Intelligence Empowers deeper Business Insights & informed Business Decisions Faster High Return on Investment Deeper Business Insights will be gained through improved information access empowering informed business decisions faster. Business Management teams and stakeholders will be freed from making their decisions based on limited data and their own gut instincts. Decisions that affect the Business Objectives Strategy and Operations of organisations, will be based upon credible factual evidence and actual organisational data and information. Moreover, decision makers will be better informed as they will be able to query actual data and will retrieve information based upon their personal needs. In addition, data-warehouses and related business intelligence and analytics can be applied directly across the business systems and processes. Data-warehouses are purposely designed and constructed with a focus on speed of data retrieval and analysis. A data-warehouse is designed for storing large volumes of data and being able to rapidly query the data. These analytical systems are constructed somewhat differently from operational systems which focus on creation and modification of data. In contrast, the data-warehouse is built for analysis and retrieval of data rather than efficient upkeep of individual records (i.e. transactional).

3 The data-warehouse allows for large system burdens to be taken off the operational environment and effectively distributes system load across an entire organisation s technology infrastructure. For many organisations, enterprise-wide information systems are comprised of multiple subsystems, physically separated and built on different platforms. Moreover, merging data from multiple disparate data sources is a common need when conducting business intelligence analytics. To solve this problem, the data-warehouse performs integration of existing disparate data sources and makes them accessible in one place. Consolidating enterprise-wide data into a single data repository alleviates the burden of duplicating data gathering efforts, and enables the extraction of crucial information that would otherwise be impossible. Additionally, the data-warehouse becomes the single view of truth across the whole business rather than the multiple truth versions that can come from reporting on individual sub-systems and disparate data sources and processes. The data-warehouse enables business users and decision makers to have access to data from many different sources as they need it. Additionally, business users will spend little time in the data retrieval process. Scheduled data integration routines, known as ETL Process (Extract, Transform & Load) are leveraged within a data-warehouse environment. These routines consolidate data from multiple source systems Extracts the data from the data sources; Transforms the data into the correct format and structure for querying and analysis; and Loads the data into a data-warehouse where subsequently, business users can then easily access data from one interface. Consumers of data will be able to query data directly with less information technology support. The wait time for information technology professionals to develop reports and queries is greatly diminished as the business users are given the ability to generate reports and queries on their own. The use of query and analysis tools against a consistent and consolidated data repository enables business users to spend more time performing valueadd data analysis and less time gathering data.

4 A data-warehouse implementation typically includes the conversion of data from numerous source systems and data files and transformation of the disparate data into a common format. Data from the various business units and departments is standardised and the inconsistent nature of data from the unique source systems is removed. Moreover, individual business units, departments, groups and users across the business will start to utilise the same data repository as the source system for their individual queries and reports. Thus all business groups and users will produce results that are consistent across the business providing a single view of truth and subsequently, the overall confidence in the organisation s data is substantially increased and valued. Data-warehouses can contain many years of data that can neither be stored within, nor reported from, transactional systems. Typically transactional systems satisfy most operating reporting requirements for a given time-period but without the inclusion of historical data. In contrast, the data warehouse stores large amounts of historical data and can enable advanced business intelligence analytics, including real-time analysis, trend analysis and trend predictions. The advantage of the data-warehouse is that it allows for advanced reporting and analysis of multiple time-periods. Return on investment (ROI) refers to the amount of increased revenue or decreased expenses a business will be able to realise from any project or investment of capital. Subsequently, implementations of data-warehouses and complementary business analytics solutions have enabled businesses to generate higher amounts of revenue and provide substantial cost savings. Many leading Industry Analyst Studies & Report findings all point to the financial Impact Business Analytics projects have on achieving a substantial impact on a business financial status. Additionally, business analytics implementations typically have generated a 3-5 year Return on Investment. And, regardless of your Organisational, Industry or Sector specific systems and processes, a datawarehouse build is tailored to meet your specific or unique business requirements.

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