* Data Quality
* Data Quality Domain
* Data Quality Rule
* Data Profiling
* Data Quality Profiling
* Database Profiling

Data Quality is a measure of the value of data in relation to
potential data problems:

* missing data records or data values
* incorrect or out-dated data values
* inconsistent abbreviations and spelling
* inconsistent data formats and units of measure
* inconsistent data organization
* duplicate data
* violations of data-rules or business-rules

Data quality can be analyzed in relation to a particular
Data Quality Domain, making it possible to determine the
importance of different data problems.

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A Data Quality Domain is an application or use of data that
imposes a set of Data Quality Rules, each of which is associated
with a degree-of-importance for the domain.

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A Data Quality Rule is a specification of one or more data
quality problems which should not exist in a set of data.

For example, a data quality rule might specify that in the
EMPLOYEE table, EMPLOYEE_NAME must be set and
that it must contain only letters and spaces. Another rule
might specify that EMPLOYEE_NAME should not contain
multiple consecutive spaces. These two rules might be specified
separately so that, in a particular Data Quality Domain,
they can be assigned different degrees-of-importance.

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Data Profiling can refer to: Data Quality Profiling or Database Profiling.

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Data Quality Profiling is the process of analyzing a database
in relation to a Data Quality Domain, to identify and prioritize
data quality problems. The results can include:

* Summaries (with counts and percentages) describing…
o completeness of datasets and data records
o problems organized by importance
o the distribution of problems in a dataset
* Details – lists of…
o missing data records
o data problems in existing records

Data quality profiling can be useful when planning and managing
data cleanup projects.

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Database Profiling is the process of analyzing a database to determine
its structure and internal relationships:

* the tables used, their keys and number of rows
* the columns used and the number of rows with a value
* relationships between tables
* columns copied or derived from other columns

Database Profiling can also include analysis of:

* tables and columns used by different applications
* how tables and columns are populated and changed
* the importance of different tables and columns
Database profiling can be useful when planning and managing
data conversion and data cleanup projects.

Database profiling can be an initial step in defining a
Data Quality Domain, which is used in Data Quality Profiling.

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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.

Sometimes zip codes are missing or in one case we found data was separated using incorrect SQL functions resulting in erroneous data. Data scrubbing / cleansing a dirty data is always different in different companies.

We have a successfully accomplished various complicated tasks using various tools and SQL language.

Translating this to Data architecture helps defining the role of the data architect as the one responsible for developing and maintaining a formal description of the data and data structures – this can include data definitions, data models, data flow diagrams, etc. (in short metadata). Data architecture includes topics such as metadata management, business semantics, data modeling and metadata workflow management.

Source: WikiPedia

2-     Operational data bases are highly normalized in relational database format to facilitate data update. Business intelligence databases are highly demoralized for fast retrieval and analyses of large amount of data.

3-     In operational databases most of the data is raw and stored without any calculations, calculations are usually done when required through queries. In business intelligence large amount of data is pre-calculated or derived to save time when running queries.

The above are few general difference between Business intelligence and operational databases, we are keep researching and will update more.

We are working on this database and adding more information on different angles. However at present there are about nearly 10,000 contacts are added and are searchable.

Source: Data mining concepts and techniques by Jiawei Han and Micheline Kamber

Financial management through general ledger, payables, receivables, inventory, sales process, purchasing, analytical accounting, fixed assets, cash flows, collections and bank reconciliations.

 Business Intelligence and reporting  That brings strategic insight into your business processes with sophisticated reporting, analysis and budgeting solutions to help you make an informed decision making. Manage budgets reports and look for trends and relationship in any functional area of your business. Browser based access to graphical key performance indicators (KPI’s) via Business portal.

Human Resource Manage your human resources, from skills mapping, recruitment and employee registration, to skills development and help in meeting government reporting requirements accurately and timely.

Project Management Management your resources, forecast your costs and budgets, track time and expenses, and manage contract and billings.

Custom Relationship management Manage your customer groups, create and launch marketing campaigns, track customers sales and after sales. Accessible from Microsoft Outlook and web.

Manufacturing Manage your entire manufacturing process from product configuration, supply and capacity planning, to scheduling and shop floor.

Supply Chain Management Improve inventory management, manage single or multi-site warehouses and handle order prcessing, demand planning.

Collaborative Workspace Simple to sophisticated, Business to Business or Business to Consumer, and from buy side to sell side. enable customers and suppliers do business with you through websites or by connecting their systems directly to yours.

Configuration and Development Customization capabilities enable system administrators and programmers to add new functionality and modify existing functionality quickly and with limited coding.

Our experiance; We have worked on few projects relating to Microsoft Dynamics involving customized reporting in Crystal reports as the canned reports were not meeting the management’s information needs. That project was related to Accounting part of Microsoft Dynamics. Customized is always required in most of softwares.