What is Data Warehouse?
Data Warehouse Definition
Data Warehouse (The Data Warehouse) is a database of unique data structure that allows relatively quick and easy performance of complex queries over large amounts of data.
A classical, production information system is primarily adapted to input data. Since the production system requires the aforementioned property for such a system, it allows the company to be operational and run smoothly, and that means mostly data entry.
On the other hand, the data storage system in its structure allows fast and easy retrieval of large amounts of data. This makes it suitable for the construction of the so-called systems for business decision support (DSS – Decision Support System).
In addition, what is Data Warehouse?
The data stored daily in the production system must ultimately serve the administrative structure of the company. Administrative structure of the company should be able to extract useful information from large amounts of data, which she will serve for evaluation of the results achieved, planning and decision-making. For this purpose it is necessary to ensure a quick and easy access to data stored in complex structures of production systems. Data Warehouse provides just such a mode that is faster and easier access to information, to view and analyze large amounts of data, in which time measures the reach in seconds or minutes.
More information in Data Warehousing PDF.
In building the data warehouse, implementes have to meet specific problems that do not come in the construction of production (transaction-oriented) information systems. Most problems are related to the construction of a system for extracting data that is a periodically automated transfer of data from the source to the destination of the production system data warehouse. Some of the problems encountered in the construction of warehouses are:
- Integration of diverse data from multiple sources (multiple production systems) implemented on different platforms;
- Rapid detection of the changes in the source system;
- Iterative nature of model-building data warehouses and hence the iterative nature of building software for extraction.
Problems related to the construction of a data model are quite well-described in the literature and is not a problem too. On the other hand, problems related to extraction of data represent the biggest challenge, making the process of building Extraction System takes between 70 and 90 percent of the total time required to build a warehouse. When we add the problems that arise due to the iterative nature of building models and data ekstrakcijskog systems, we get a system which is very difficult to accurately determine the limits of construction. This is one of the reasons why the project of building data warehouses, as it turned out in practice, is largely subject to failure.
Useful links about DWH and what is Data Warehouse in detail:
Data Warehousing and Business Intelligence Portal
DataWarehousing Papers & Articles
DM Review Business Intelligence & Data Warehousing Enabling E-Business
TDWI Welcome to The Data Warehousing Institute
Welcome to Ralph Kimball Associates
Related Data Warehouse Posts:
- ABC reporting deployment optionsThere are 3 basic ways of report deployment in Data Warehouse. Each has benefits and risks. Here is the list of facts to be aware before choosing how to deploy reports.
- Business Intelligence Example of Reporting Errors - Duplication of FiguresData Warehouse is not imune to changes on source systems, actually it is very vulnarable to them. If source systems have duplicate figures, Data Warehouse will also report duplicates. Here is an example from practice where guys made patch on CRM system to handle "urgent" marketing product without making any kind of communication toward reporting - DWH and Business Intelligence. Duplicated figures were in official reports for three months and caused significant deviations of core Key Performance Indicators. Unfortunatelly, reporting mistake was presented to the board members as Data Warehouse error.
- Data changes problemsEvery change in source system directly related with reporting systems, specially Business Performance Management Software can cause official external and internal reporting collapse. What seams small change in only one table in source system like change in hierarchy of one larger customer could cause distorted or no data at all if the change is not predicted in company information system (IS). Finding business segments of offices beneath residential customer. Obviously this is a mistake and needs to be cleaned.
- Data ForensicsIf company insists on investigating every aspect of business intelligence reporting errors then we are talking about data forensic. It is mandatory to be aware about reporting mistakes that happened but too big focus on every detail of generated error (who made mistake, who signed, who defined process, was everything under procedure, what is the error impact and etc.)
- Data Mart in Data WarehousingConsider Data Mart as a small specialised Data Warehouse or independant unit (financial data mart, customer data mart, sales relation, customer support, technical data and similar) in Data Warehouse scheme.
- Data Model LimitsWhenever strategic management decision making process needs new data from existing company information systems it should be implemented quickly and information provided easily. But is it really so?Problem is that every new requirement for information details might face limitations in data models in production systems or data warehousing.
- Data Warehouse and Business Intelligence Reporting Users
- Data Warehouse ownershipIf the ownerships has to belong to only one department it is a little bit risky decision. For example if IT is owner of DWH, they are just service providers without in deep knowledge what to do exactly with content. Perhaps better variant is to give ownership to finance, to be more precise to controlling. Reason for this decision is general view over definitions, external and management reporting. Controlling knows best what kind of products will be made upon DWH. Perhaps best solution would be to establish committee of experts from all business areas with lead from controlling and to give dwh ownership to this body.
- Data Warehouse ProblemsFrom time to time Data Warehouse might be in the center of user anger despite the fact it does not cause directly problems but it is in the middle. It is visual interface for users and therefore first in the line to suffer users anger. Data Warehouse is on the line of fire...
Here are some issues that could be raised by data warehouse users:There is no mapping process of new products and services.
There is no clear definition of methodological responsibilities.
Who defines performance indicators or products?
- Data Warehouse Process Shock Absorber
- Defining Key Performance IndicatorsMarketing Analyst: What is the correct figure asks marketing data warehousing team?
IT developer: Both figures are ok.
Marketing Analyst: We know that both figures are ok but which one is correct for our KPI?
- Difference between data integration and unificationDifference between data integration and unification is best to consider through Data Warehouse (DWH). It is widely used technology and it quite often being treated to help company with both crucial data quality issues. Data unification is something what Data Warehouse (DWH) does with ease since it is constructed to gather information from many sources with purpose to speed reports and to store data. All data are in same storage place customized for reporting processes.
- ERP reporting and DWH/BIIs it reasonable to include ERP reports in Data Warehouse and Business Intelligence or not? It is a thin line between wrong and good decision. Here are few thoughts what is about data transfers.
- General ConceptIf each marketing product is going to be implemented without general concept and idea of future business logic of products and without cost and revenue details for thorough profitability analysis then Information Systems are going to be caught in trap of impossible functionality and business logic.
- Goofy Capital Investment Process
- Google Effect vs. Knowledge IntegrationGoogle Effect is attention deficit problem where facts can be easily found but not understood. Our brains are physically changing due to data accessibilty. Companies have similar problem due to information hyper production.
- Information System Paradox
- Information Systems as Show StoppersHow can own Information Systems become show stoppers for new products? Own IS as main enemy of development.It is more than simple.
- Information Systems Delivery ProblemsReports are being produced, users have started to test them. Everything seems idealistic... But reports have errors and IT is concentrated on report quantity not quality. Users report errors but IT does not have time to fix them, because reporting quantity needs to be caught.Reports are top of the ice bergs, with their testing many errors will appear that are not made by reporting platform. Their error source could be deeper in production systems or even in the processes.
- Knowledge Management and Business Intelligence PictureCombination of all mentioned exports form information systems to knowledge management base with corporate documentation, processes and innovations is essential Decision Intelligence, basis for decision support system. Only adequately stuffed Decision Intelligence can create Competitive Intelligence.
- Mapping issue, DWH and MIS
- Process problems in productionWith introduction of DWH and BI lot of dirt below carpet is found, meaning many process errors in production systems are found. Not only dirt is found but also dead mice are found below carpet. When reporting errors are being communicated to production systems owners first reaction is treating the bad messenger as enemy. We do not have errors, why are you attacking us, you have made mistake and similar can be heard from respective owners and this is logical human reaction. After first negative shock they will start to cooperate on cleaning process errors, data quality and data flow improvement but first expect emotional fight.
- Replacing Reports from Source Systems
- Spoiling DWH quality
- Thin line between legacy reports and DWH/BI reportingOne case showed significant issue about where to deploy and use reports. Dillema was to place all the reports into Data Warehouse or to leave part of them in legacy system and to export more complicated once to DWH/BI. Actually, there was no dillema at all for user (department). Department and director wanted to transfer all the reports into DWH/BI and to leave loyalty system without any reporting.
- Valuable Internal Business Intelligence ResourcesMany companies do not appreciate own resources: experts, know how, internal knowledge, internal work. It somehow much easier to hire vendor to do job instead to engage own teams.
- What Do You Have in DWH?What do you have in DWH? This is political question, because everything can be set up to be available through DWH but others allocate resources and schedule priorities. This is not question for DWH but for management and most of all for CMO, CFO and CSO who are main pushers toward DWH resources.
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