The process of performing a variety of tests on a system to explore functionality or to identify problems. System testing is usually required before and after a system is put in place. A series of systematic procedures are referred to while testing is being performed. These procedures tell the tester how the system should perform and where common mistakes may be found. Testers usually try to “break the system” by entering data that may cause the system to malfunction or return incorrect information. For example, a tester may put in a city in a search engine designed to only accept states, to see how the system will respond to the incorrect input.

Source; BusinessDictionary.com

Any number of techniques and tools can be employed to handle these kinds of situations. Specialized structured query language constructs such as the T-SQL LIKE and CONTAINS clauses can be used for basic wildcard searches. But LIKE queries are limited in their ability to handle misspellings, and CONTAINS queries are used in conjunction with SQL Server Full Text indexing.
Fuzzy search databases can be amassed that compile common misspellings (or variants) of specific words which can then be substituted during the cleansing process. This technique works better for applications that check one word at a time, like Microsoft® Word, which employs a similar technique for making spelling corrections on the fly.

Phonetic matching algorithms, implemented in SQL Server as SOUNDEX queries, also detect similarities between single words by matching prominent phonetic characteristics that are then scored numerically for comparison. Key drawbacks to SOUNDEX are that the input string must be contiguous with no spaces, and if the first character of the input string is not correct, the probability of a match being made drops down to zero.

Basically mashups combine similar types of media and information from multiple sources into a single representation. An example is AlertMap, which combines data from over 200 sources related to severe weather conditions, biohazard threats, and seismic information, and displays them on a map of the world; another example of a data mashup is Chicago Crime Map, which indicates the crime rate and location of crime in Chicago.

Source: Wikipedia.org

The data was first exported data from customer_name column to text file along with prime keys, then re-imported into access database using space delimiter which separated first name and last name into 2 columns.

Search through rows of data, realizing patterns. Build new reports that answer unusual end-user questions and modifying existing canned reports. Modify reports to correct longstanding problems. Research and document data requirements and listen intently to a manager, translating what He/she says into data fields.

Design queries for different legacy databases using multiple BI packages. Present reports to customers that elicit good questions and no doubts

Measures and attributers come directly from fields in OLTP or calculated fields based on those fields. Dimensions and hierarchies must be represented in the data and relationships contained in OLTP data. In case the piece of information is not available in the OLTP data source, we need to determine if it is present in another data source. Also analyses must be done to see if different data sources can be joined during population of data source.[1]

If the required data is unavailable then step should be taken to gather the required data in coordination with decision makers.

Source:

1- Delivering Buisness Intelligence with Sql Server 2008 by Brian Larson

We tried our best to elaborate some terms used in Business intelligence, Data Warehousing and Data Mart.
If readers have some more to add / please feel free to submit in comments so it can help other readers and correct ourselves.

Data Mart Structure

The data we used for business intelligence can be divided into four
categories: measures, dimensions, attributes, hierarchies.The four type of data help us to define the structure of data mart.[1]

Measures

Measure is numeric quantity expressing some aspect organization’s
performance. Measures are the facts and also known as fact table.[1]

Dimension

Dimension is categorization used to spread out aggregated measure to reveal its constituent parts.[1] dimensions include time (or date), customer, product, geography, lab type, campus, patient, promotions, gender (and other demographics), and so forth. Each dimension is associated with the facts / measures to which it relates via the linkages / joins between the table(s) housing the dimension (the dimension table) and the fact table.[2]

Attributes
For example a member of the Patient dimension, within the Analysis Services implementation for a healthcare provider, might contain information such as patient name, patient ID, gender, age group, race, and other attributes. Some of these attributes might relate to each other hierarchically, and, as we shall see in subsequent articles of this subseries (as well as within other of my articles), multiple hierarchies of this sort are common in real-world dimensions.[2]
Hierarchies

These are created from the attributes of dimesions. For example In Dimension Geography there are attributes named as Country-State-City-Postal Code.[3]

Source

1- Delivering Business Intelligence with Sql Server 2008 by brian Larson
2- Database Journal by Internet.com
3- Sql Server Analysis Services 2008 with MDX

As per Oracle.com;

The purpose of the data mart is to provide access to data that is specific to a particular department or functional area. The data should be at a meaningful level of detail for the kind of analysis that the end users want to perform, and should be presented in the business terms that they understand.

The expectation is that the analysis of the data in a data mart will lead to more informed business decisions. Therefore, you need to understand how the business person makes decisions—what questions the users ask in the decision-making process, and what data is necessary to answer those questions. The best way to understand the business processes is through interviews with the business people. The requirements identified as a result of these interviews comprise the business requirements for your data mart.

As you gather requirements for the data mart, you should focus your efforts on the information needs of a single subject area. Remember that no requirements document will be complete enough to get all information at the outset, and you need to design the data mart to accommodate changing needs. However, if you introduce new subjects or deviate from your primary theme, you will lose your focus and schedule.

Even though the data mart addresses one subject area, it usually has many business users, each with different requirements and expectations. Try to identify at least one representative from each area of the business for your interviews. For example, if you are building a marketing data mart, interview several people involved in various aspects of the marketing function (such as a marketing analyst, channel specialist, direct marketing manager, and promotion manager).

Use a consistent set of questions or an interview template for each interview. The questions should focus on the users’ information requirements, such as content, frequency of update, priorities, and level of detail. Finally, set a definite time limit on the interview and requirements gathering phase; otherwise, it could continue indefinitely as you try to refine each requirement.

You cannot collect all requirements in this time frame, but you will get enough to create a road map. Needs will change during the implementation period, and you will need a way to evaluate and accommodate requests for changes or to reject and consider them for a future phase.

The objective of this paper is to demonstrate the capability of DTS as a database solution for
automatic data transfer and update in solving business problem. This DTS package is developed for the sales of variety of plants and eventually expanded into commercial supply and landscaping business.
Please Read more