| The focus can be enterprise-wide, within a department, or on a specific business activity. Using business analysis experience and leading Business Intelligence (BI) technologies we help our clients to: |
- Identify and understand business information needs
- Evaluate and select appropriate BI technology
- Improve business processes by streamlining information flow
- Design, code, and deploy effective, customized applications that deliver action-enabling information.
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Our concept-to-rollout development services result in complete solutions. We are experts in leading OLAP (On Line Analytical Processing) technologies and other analysis and planning applications for integrating, delivering and presenting key business performance information to the right people at the right time.
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| Data Mart |
| A data mart (DM) is a specialized version of a data warehouse (DW). Like data warehouses, data marts contain a snapshot of operational data that helps business people to strategize based on analyses of past trends and experiences. The key difference is that the creation of a data mart is predicated on a specific, predefined need for a certain grouping and configuration of select data. A data mart configuration emphasizes easy access to relevant information. |
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| There can be multiple data marts inside a single corporation; each one relevant to one or more business units for which it was designed. DMs may or may not be dependent or related to other data marts in a single corporation. If the data marts are designed using conformed facts and dimensions, then they will be related. In some deployments, each department or business unit is considered the “owner” of their data mart which includes all the “hardware, software and data.”[1] This enables each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc. |
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| Reasons for Creating a Data Mart |
- Eases access to frequently needed data
- Creates collective view by a group of users
- Improves end-user response time
- Ease of creation
- Lower cost than implementing a full Data warehouse
- Potential users are more clearly defined than in a full Data warehouse
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| Data Quality |
| Companies often cannot rely on the information that serves as the very foundation of their primary business applications. Inaccurate or inconsistent data can hinder your company's ability to understand its current – and future – business problems. This leads to poor decisions that can cause a host of negative results, including lost profits, operational delays, customer dissatisfaction and much more. |
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| An effective data quality strategy can help you better understand your business environment, allowing you to maximize profitability and reduce costly operational inefficiencies. |
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| The goal of data management is to provide the infrastructure to transform raw data into consistent, accurate and reliable corporate information. Its foundation consists of the five building blocks of data management technology: |
- Data Profiling – Inspect data for errors, inconsistencies, redundancies and incomplete information
- Data Quality – Correct, standardize and verify data
- Data Integration – Match, merge or link data from a variety of disparate sources
- Data Augmentation – Enhance data using information from internal and external data sources
- Data Monitoring – Check and control data integrity over time
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| Data Warehouse |
Data Warehouse is the main repository of the organization's historical data, its corporate memory. For example, an organization would use the information that's stored in its data warehouse to find out what day of the week they sold the most widgets in May 1992, or how employee sick leave the week before Christmas differed between California and Quebec from 2001-2005. In other words, the data warehouse contains the raw material for management's decision support system. The critical factor leading to the use of a data warehouse is that a data analyst can perform complex queries and analysis (such as data mining) on the information without slowing down the operational systems.
While operational systems are optimized for simplicity and speed of modification (online transaction processing, or OLTP) through heavy use of database normalization and an entity-relationship model, the data warehouse is optimized for reporting and analysis (on line analytical processing, or OLAP). Frequently data in data warehouses is heavily denormalised, summarized and/or stored in a dimension-based model but this is not always required to achieve acceptable query response times.
CRESCO Global can make it easier to get historical data to your Data Warehousing application, and get answers to your specific questions. We can handle large volumes of data through optimized queries. We have developed Data Warehouse and data mart solutions for many of our clients. |
| Advantages of using data warehouse |
- There are many advantages to using a data warehouse, some of them are:
- Enhances end-user access to a wide variety of data.
- Business decision makers can obtain various kinds of trend reports e.g. the item with the most sales in a particular area / country for the last two years.
- A data warehouse can be a significant enabler of commercial business applications, most notably customer relationship management (CRM).
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| Concerns in using data warehouses |
- Extracting, cleaning and loading data is time consuming.
- Data warehousing project scope must be actively managed to deliver a release of defined content and value.
- Compatibility problems with systems already in place.
- Security could develop into a serious issue, especially if the data warehouse is web accessible.
- Data Storage design controversy warrants careful consideration and perhaps prototyping of the data warehouse solution for each project's environments.
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| Extract, transform, and load (ETL) |
| Extract, transform, and load (ETL) is a process in data warehousing that involves extracting data from outside sources, transforming it to fit business needs, and ultimately loading it into the data warehouse. ETL is important, as it is the way data actually gets loaded into the warehouse. This article assumes that data is always loaded into a data warehouse, whereas the term ETL can in fact refer to a process that loads any database. |
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