Oracle vs SQL Server : High Availability, Licensing, Performance and security

High Availability
SQL Server 2008:
Database-mirroring :
Use a rolling upgrade process to upgrade database instances in a database-mirroring session.
Take advantage of write-ahead functionality on the incoming log stream on the mirror server.
Use page read-ahead capability during the undo phase to further improve performance.
Provide reporting capabilities with a database snapshot as a source for reports on the mirror server.

Failover clustering:
Enable failover support by sharing access among nodes and restarting SQL Server on a working node.
Increase scalability with support of up to 16 nodes in a single failover cluster.
Support a rolling upgrade process for servers participating in a failover-clustering configuration.

Peer-to-peer replication:
Replicate changes in near real time, while all databases also handle their primary responsibilities.
Boost scalability, availability, and processing capacity by configuring applications to use peers and to fail over to another peer.
Protect against accidental conflicts with built-in conflict detection.
Increase availability by dynamically adding a new node to an existing topology.

Log shipping:
Provide database redundancy by using standby servers to automatically back up transaction logs.
Increase availability by providing multiple failover sites.
Reduce the load on the primary server by using a secondary server for read-only query processing.

Oracle 11g:

Oracle Provides following features for high availability.

Real Application Clusters
Clusterware
Data Guard
GoldenGate
Streams
Secure Backup
Recovery Manager (RMAN)
Flashback Technologies
VM
Cloud Computing
Cloud Storage
Cross-Platform Transportable Tablespace
Edition-Based Redefinition
Online Reorganization

License cost

Oracle 11g license cost

- Per Processor = $17,500
- Support (22%) = $3,850
- Total (Per Processor) = $21,350
- Total (4 Processors) = $85,400


license cost of SQL Server

- Per Processor = $5,999
- Total (4 Processors) = $23,996

Security:

SQL Server features role-based security for server, database and application profiles; integrated tools for security auditing, tracking 18 different security events and additional sub-events; plus support for sophisticated file and network encryption, including SSL, Kerberos and delegation.

Oracles provides powerful security features such as database activity monitoring and blocking, privileged user and multi-factor access control, data classification, transparent data encryption, consolidated auditing and reporting, secure configuration management, and data masking, customers can deploy reliable data security solutions that do not require any changes to existing applications, saving time and money.

ORACLE DATABASE SECURITY PRODUCTS:
Oracle Advanced Security
Oracle Audit Vault
Oracle Label Security
Oracle Configuration Management
Oracle Secure Backup
Oracle Database Firewall
Oracle Database Vault
Oracle Data Masking
Oracle Total Recall

Performance:

In SQL Server, the DBA has no "real" control over sorting and cache memory allocation. The memory allocation is decided only globally in the server properties memory folder, and that applies for ALL memory and not CACHING, SORTING, etc.


Following Oracle features do not exist in SQL Server.
There are no bitmap indexes
There are no reverse key indexes in SQL Server.
There are no function-based indexes in SQL Server.

SQL - For beginners

SQL is a special-purpose language designed for the creation and maintenance of data in relational databases.

Three languages-within-a-language offer everything you need to create, modify, maintain, and provide security for a relational database:

1. The Data Definition Language (DDL): The part of SQL that you use to create (completely define) a database, modify its structure, and destroy it when you no longer need it.

2. Data Manipulation Language (DML): DML Performs database maintenance.Using this powerful tool, you can specify what you want to do with the data in your database — enter it, change it, or extract it.


3. Data Control Language (DCL): DCL Protects your database from becoming corrupted. Used correctly, the DCL provides security for your database; the amount of protection depends on the implementation. If your implementation doesn’t provide sufficient protection, you must add that protection to your application program.

Creating tables
A database table is a two-dimensional array made up of rows and columns.
You can create a table by using the SQL CREATE TABLE command. Within the command, you specify the name and data type of each column.After you create a table, you can start loading it with data. (Loading data is a DML, not a DDL, function.) If requirements change, you can change a table’s structure by using the ALTER TABLE command. If a table outlives its usefulness or becomes obsolete, you can eliminate it with the DROP command. The various forms of the CREATE and ALTER commands, together with the DROP command, make up SQL’s DDL.

e.g.
CREATE TABLE CUSTOMER (
CustomerID INTEGER NOT NULL,
FirstName CHARACTER (15),
LastName CHARACTER (20) NOT NULL,
Street CHARACTER (25),
City CHARACTER (20),
State CHARACTER (2),
Zipcode INTEGER,
Phone CHARACTER (13) ) ;

The simplest use of the SELECT statement is to retrieve all the data in all the
rows of a specified table.

To do so, use the following syntax:

SELECT * FROM CUSTOMER ;

The asterisk (*) is a wildcard character that means everything. In this context,
the asterisk is a shorthand substitute for a listing of all the column names of the
CUSTOMER table. As a result of this statement, all the data in all the rows and
columns of the CUSTOMER table appear on-screen.

SELECT statements can be much more complicated than the statement in this
example. In fact, some SELECT statements can be so complicated that they’re
virtually indecipherable. This potential complexity is a result of the fact that
you can tack multiple modifying clauses onto the basic statement.

WHERE clause,

It is the most commonly used method to restrict the rows that a
SELECT statement returns.
A SELECT statement with a WHERE clause has the following general form:

SELECT column_list FROM table_name
WHERE condition ;

The following example shows a compound condition inside a SELECT
statement:

SELECT FirstName, LastName, Phone FROM CUSTOMER
WHERE State = ‘NH’
AND Status = ‘Active’ ;

This statement returns the names and phone numbers of all active customers
living in New Hampshire. The AND keyword means that for a row to qualify for
retrieval, that row must meet both conditions: State = ‘NH’ and Status =
‘Active’.

Insert Statement

You can insert a row for the new object without filling in the data in all the columns. If you want the table in first normal form, you must insert enough data to distinguish the new row from all the other rows in the table. Inserting the new row’s primary key is sufficient for this purpose. In addition to the primary key, insert any other data that you have about the object. Columns in which you enter no data contain nulls.
The following example shows such a partial row entry:

INSERT INTO CUSTOMER (CustomerID, FirstName, LastName)
VALUES (:vcustid, ‘Tyson’, ‘Taylor’) ;

Another way to copy data from one table in a database to another is to nest a
SELECT statement within an INSERT statement. This method (a subselect)
doesn’t create a virtual table but instead duplicates the selected data. You can
take all the rows from the CUSTOMER table, for example, and insert those rows
into the PROSPECT table. Of course, this only works if the structures of the
CUSTOMER and PROSPECT tables are identical. Later, if you want to isolate
those customers who live in Maine, a simple SELECT with one condition in
the WHERE clause does the trick, as shown in the following example:

INSERT INTO PROSPECT
SELECT * FROM CUSTOMER
WHERE State = ‘ME’ ;

Updating Existing Data
You can count on one thing in this world — change. If you don’t like the
current state of affairs, just wait a while. Before long, things will be different.
Because the world is constantly changing, the databases used to model
aspects of the world also need to change. A customer may change her address.
The quantity of a product in stock may change (because, you hope, someone
buys one now and then). A basketball player’s season performance statistics
change each time he plays in another game. These are the kinds of events that
require you to update a database.
SQL provides the UPDATE statement for changing data in a table. By using a
single UPDATE statement, you can change one, some, or all the rows in a
table. The UPDATE statement uses the following syntax:
UPDATE table_name
SET column_1 = expression_1, column_2 = expression_2,
..., column_n = expression_n
[WHERE predicates] ;

Customer lists change occasionally — as people move, change their phone
numbers, and so on. Suppose that Abe Abelson moves from Springfield to
Kankakee. You can update his record in the table by using the following
UPDATE statement:
UPDATE CUSTOMER
SET City = ‘Kankakee’, Telephone = ‘666-6666’
WHERE Name = ‘Abe Abelson’ ;

You can use a similar statement to update multiple rows. Assume that Philo
is experiencing explosive population growth and now requires its own area
code. You can change all rows for customers who live in Philo by using a
single UPDATE statement, as follows:

UPDATE CUSTOMER
SET AreaCode = ‘(619)’
WHERE City = ‘Philo’ ;

Updating all the rows of a table is even easier than updating only some of
the rows. You don’t need to use a WHERE clause to restrict the statement.
Imagine that the city of Rantoul has acquired major political clout and has
now annexed not only Kankakee, Decatur, and Philo, but also all the cities
and towns in the database.
You can update all the rows by using a single statement, as follows:

UPDATE CUSTOMER
SET City = ‘Rantoul’ ;

Delete statement

As time passes, data can get old and lose its usefulness. You may want to
remove this outdated data from its table. Unneeded data in a table slows performance,
consumes memory, and can confuse users. You may want to transfer
older data to an archive table and then take the archive offline. That way,
in the unlikely event that you ever need to look at that data again, you can
recover it. In the meantime, it doesn’t slow down your everyday processing.
Whether you decide that obsolete data is worth archiving or not, you eventually
come to the point where you want to delete that data. SQL provides for
the removal of rows from database tables by use of the DELETE statement.
You can delete all the rows in a table by using an unqualified DELETE statement,
or you can restrict the deletion to only selected rows by adding a WHERE
clause. The syntax is similar to the syntax of a SELECT statement, except that
you use no specification of columns. If you delete a table row, you remove all
the data in that row’s columns.
For example, suppose that your customer, David Taylor, just moved to Tahiti
and isn’t going to buy anything from you anymore.
You can remove him from
your CUSTOMER table by using the following statement:

DELETE FROM CUSTOMER
WHERE FirstName = ‘David’ AND LastName = ‘Taylor’ ;

Assuming that you have only one customer named David Taylor, this statement
makes the intended deletion. If any chance exists that you have two
customers who share the name David Taylor.


Set functions

Sometimes, the information that you want to extract from a table doesn’t relate
to individual rows but rather to sets of rows. These functions are COUNT,
MAX, MIN, SUM, and AVG. Each function performs an action that draws data
from a set of rows rather than from a single row.

COUNT
The COUNT function returns the number of rows in the specified table. To
count the number of precocious seniors in my example high school database,
use the following statement:
SELECT COUNT (*)
FROM STUDENT
WHERE Grade = 12 AND Age < 14 ;

MAX
Use the MAX function to return the maximum value that occurs in the specified
column. Say that you want to find the oldest student enrolled in your
school. The following statement returns the appropriate row:
SELECT FirstName, LastName, Age
FROM STUDENT
WHERE Age = (SELECT MAX(Age) FROM STUDENT);
This statement returns all students whose ages are equal to the maximum
age. That is, if the age of the oldest student is 23, this statement returns the
first and last names and the age of all students who are 23 years old.
This query uses a subquery. The subquery SELECT MAX(Age) FROM
STUDENT is embedded within the main query. I talk about subqueries (also
called nested queries)

MIN
The MIN function works just like MAX except that MIN looks for the minimum
value in the specified column rather than the maximum. To find the youngest
student enrolled, you can use the following query:
SELECT FirstName, LastName, Age
FROM STUDENT
WHERE Age = (SELECT MIN(Age) FROM STUDENT);
This query returns all students whose age is equal to the age of the youngest
student.

SUM
The SUM function adds up the values in a specified column. The column must
be one of the numeric data types, and the value of the sum must be within the
range of that type. Thus, if the column is of type SMALLINT, the sum must be
no larger than the upper limit of the SMALLINT data type. In the retail database
, the INVOICE table contains a record of all sales.
To find the total dollar value of all sales recorded in the database, use the SUM
function as follows:

SELECT SUM(TotalSale) FROM INVOICE;

AVG
The AVG function returns the average of all the values in the specified
column. As does the SUM function, AVG applies only to columns with a
numeric data type. To find the value of the average sale, considering all transactions
in the database, use the AVG function like this:
SELECT AVG(TotalSale) FROM INVOICE
Nulls have no value, so if any of the rows in the TotalSale column contain null
values, those rows are ignored in the computation of the value of the average
sale.

Zeroing In on the Data You Want

The modifying clauses available in SQL are FROM, WHERE, HAVING, GROUP BY,
and ORDER BY. The FROM clause tells the database engine which table or tables
to operate on. The WHERE and HAVING clauses specify a data characteristic that
determines whether or not to include a particular row in the current operation.
The GROUP BY and ORDER BY clauses specify how to display the retrieved
rows.

SELECT * FROM SALES ;

This statement returns all the data in all the rows of every column in the
SALES table. You can, however, specify more than one table in a FROM clause.
Consider the following example:
SELECT *
FROM CUSTOMER, SALES ;

IN and NOT IN
The IN and NOT IN predicates deal with whether specified values (such as
OR, WA, and ID) are contained within a particular set of values You may, for example, have a table that lists suppliers of a commodity that your company purchases on a regular basis. You want to know the phone numbers of those suppliers located in the Pacific Northwest. You can find these numbers by using comparison predicates,
such as those shown in the following example:

SELECT Company, Phone
FROM SUPPLIER
WHERE State = ‘OR’ OR State = ‘WA’ OR State = ‘ID’ ;

You can also use the IN predicate to perform the same task, as follows:
SELECT Company, Phone
FROM SUPPLIER
WHERE State IN (‘OR’, ‘WA’, ‘ID’) ;
This formulation is a more compact than the one using comparison predicates
and logical OR.
The NOT IN version of this predicate works the same way. Say that you have
locations in California, Arizona, and New Mexico, and to avoid paying sales
tax, you want to consider using suppliers located anywhere except in those
states. Use the following construction:

SELECT Company, Phone
FROM SUPPLIER
WHERE State NOT IN (‘CA’, ‘AZ’, ‘NM’) ;

GROUP BY clause
with one of the aggregate functions (also called set functions) to get a quantitative
picture of sales performance. For example, you can see which salesperson is selling more of the profitable high-ticket items by using the average (AVG) function as follows:

SELECT Salesperson, AVG(TotalSale)
FROM SALES
GROUP BY Salesperson;


You can analyze the grouped data further by using the HAVING clause. The
HAVING clause is a filter that acts similar to a WHERE clause, but on groups of
rows rather than on individual rows. To illustrate the function of the HAVING
clause, suppose the sales manager considers Ferguson to be in a class by
himself. His performance distorts the overall data for the other salespeople.
You can exclude Ferguson’s sales from the grouped data by using a HAVING
clause as follows:

SELECT Salesperson, SUM(TotalSale)
FROM SALES
GROUP BY Salesperson
HAVING Salesperson <> ‘Ferguson’;

another implementation, the order may be that of the most recent updates. The order can also change unexpectedly if anyone physically reorganizes the database. Usually, you want to specify the order in which you want the rows. You may, for example, want to see the rows in order by the SaleDate, as follows:

SELECT * FROM SALES ORDER BY SaleDate ;


JOIN
JOINs are powerful relational operators that combine data from multiple tables
into a single result table. The source tables may have little (or even nothing)
in common with each other.

Cross join

SELECT *
FROM EMPLOYEE, COMPENSATION ;

The result table, which is the Cartesian product of the EMPLOYEE and COMPENSATION tables, contains considerable redundancy.

An equi-join is a basic join with a WHERE clause containing a condition specifying
that the value in one column in the first table must be equal to the value
of a corresponding column in the second table. Applying an equi-join to the
example tables from the previous section brings a more meaningful result:

SELECT *
FROM EMPLOYEE, COMPENSATION
WHERE EMPLOYEE.EmpID = COMPENSATION.Employ ;

The natural join is a special case of an equi-join. In the WHERE clause of an equijoin,a column from one source table is compared with a column of a second source table for equality. The two columns must be the same type and length and must have the same name. In fact, in a natural join, all columns in one table that have the same names, types, and lengths as corresponding columns
in the second table are compared for equality.

Imagine that the COMPENSATION table from the preceding example has
columns EmpID, Salary, and Bonus rather than Employ, Salary, and Bonus.
In that case, you can perform a natural join of the COMPENSATION table with
the EMPLOYEE table. The traditional JOIN syntax would look like this:

SELECT E.*, C.Salary, C.Bonus
FROM EMPLOYEE E, COMPENSATION C
WHERE E.EmpID = C.EmpID ;

The inner join is so named to distinguish it from the outer join. An inner join
discards all rows from the result table that don’t have corresponding rows in
both source tables. An outer join preserves unmatched rows. That’s the difference.

Outer join
When you’re joining two tables, the first one (call it the one on the left) may
have rows that don’t have matching counterparts in the second table (the one
on the right). Conversely, the table on the right may have rows that don’t have
matching counterparts in the table on the left. If you perform an inner join on
those tables, all the unmatched rows are excluded from the output. Outer joins,
however, don’t exclude the unmatched rows. Outer joins come in three types:
the left outer join, the right outer join, and the full outer join.

Left outer join
In a query that includes a join, the left table is the one that precedes the keyword
JOIN, and the right table is the one that follows it. The left outer join preserves
unmatched rows from the left table but discards unmatched rows
from the right table.

SELECT *
FROM LOCATION L LEFT OUTER JOIN DEPT D
ON (L.LocationID = D.LocationID)
LEFT OUTER JOIN EMPLOYEE E
ON (D.DeptID = E.DeptID);


This join pulls data from three tables. First, the LOCATION table is joined to the DEPT table. The resulting table is then joined to the EMPLOYEE table. Rows from the table on the left of the LEFT OUTER JOIN operator that have no corresponding row in the table on the right are included in the result. Thus, in the first join, all locations are included, even if no department associated with them exists. In the second join, all departments are included, even if no employee associated with them exists.

Right outer join
I bet you figured out how the right outer join behaves. Right! The right outer
join preserves unmatched rows from the right table but discards unmatched
rows from the left table. You can use it on the same tables and get the same
result by reversing the order in which you present tables to the join:

SELECT *
FROM EMPLOYEE E RIGHT OUTER JOIN DEPT D
ON (D.DeptID = E.DeptID)
RIGHT OUTER JOIN LOCATION L
ON (L.LocationID = D.LocationID) ;

In this formulation, the first join produces a table that contains all departments,
whether they have an associated employee or not. The second join produces a table that contains all locations, whether they have an associated department or not.

Full outer join
The full outer join combines the functions of the left outer join and the right
outer join. It retains the unmatched rows from both the left and the right tables. Consider the most general case of the company database used in the preceding examples. It could have Locations with no departments, Departments with no locations, Departments with no employees, Employees with no departments

To show all locations, departments, and employees, regardless of whether
they have corresponding rows in the other tables, use a full outer join in the
following form:

SELECT *
FROM LOCATION L FULL JOIN DEPT D
ON (L.LocationID = D.LocationID)
FULL JOIN EMPLOYEE E
ON (D.DeptID = E.DeptID) ;

Data Control Language Commands
The Data Control Language (DCL) has four commands: COMMIT, ROLLBACK,
GRANT, and REVOKE. These commands protect the database from harm, either
accidental or intentional.

Further readings;
1. Codd's 12 rules

2. Database_normalization

MySQL database optimization

1.One of the first places to look to improve performance is queries, particularly the ones that run often.
2.Big gains can be achieved by analyzing these queries and rewriting them efficiently. You can use MySQL’s SHOW QUERY LOG to get an idea of which queries need to be fine tuned.
3.If optimization of your queries s a first goal, the first thing to do is to try implementing indexes. If you have a column involved in searching ( where clause), grouping ( group by clause) or sorting(Order by clause) and columns used in JOIN operations, it is likely result in a performance gain if you create index on these columns.
4.Another factor in creating index is cardinality( i.e.number of unique values),the higher the cardinality, the greater the chance that MySQL uses the index in queries.
5.Index speeds up SELECT queries but slows DELETE,INSERT and UPDTE queries, so create indexes on those columns which are mostly used in SELECT queries.
6.Create scheduled job to REBUILD INDEXES.
7.If business operations doesn’t affect, schedule to RESTART server weekly once when no users are connected to system.
8.Run ANALYZE TABLE command frequently ( schedule it weekly)
9.Run OPTIMIZE TABLE command frequently ( schedule it weekly)
10.SCHEMA OPTIMIZATION: Optimize data types and use DeNormalization if required.
11.Design your tables to minimize their space on the disk. This can result in huge improvements by reducing the amount of data written to and read from disk. Smaller tables normally require less main memory while their contents are being actively processed during query execution. Any space reduction for table data also results in smaller indexes that can be processed faster.
12.If your application makes several database requests to perform related updates, combining the statements into a stored PROCEDURE can help performance. Similarly, if your application computes a single result based on several column values or large volumes of data, combining the computation into a UDF (user-defined function) can help performance.
13.Disk seeks are a huge performance bottleneck. This problem becomes more apparent when the amount of data starts to grow so large that effective caching becomes impossible. If you find this issue then Increase the number of available disk spindles (and thereby reduce the seek overhead) by either symlinking files to different disks or striping the disks.
14.Check if you need partitioning of tables either ROW BASED PARTITIONING or HORIZONTAL PARTITIONING.

For more information, pl use following links.
http://dev.mysql.com/doc/refman/5.5/en/optimization.html
http://docs.oracle.com/cd/E17952_01/refman-5.5-en/mysql-tips.html

IDENT_CURRENT,@@IDENTITY and SCOPE_IDENTITY

IDENT_CURRENT returns the last identity value generated for a specific table in any session and any scope.

@@IDENTITY returns the last identity value generated for any table in the current session, across all scopes.

SCOPE_IDENTITY returns the last identity value generated for any table in the current session and the current scope.

An INSERT trigger is defined on T1. When a row is inserted to T1, the trigger fires and inserts a row in T2. This scenario illustrates two scopes: the insert on T1, and the insert on T2 by the trigger.

Assuming that both T1 and T2 have identity columns, @@IDENTITY and SCOPE_IDENTITY will return different values at the end of an INSERT statement on T1. @@IDENTITY will return the last identity column value inserted across any scope in the current session. This is the value inserted in T2. SCOPE_IDENTITY() will return the IDENTITY value inserted in T1. This was the last insert that occurred in the same scope. The SCOPE_IDENTITY() function will return the null value if the function is invoked before any INSERT statements into an identity column occur in the scope.

Failed statements and transactions can change the current identity for a table and create gaps in the identity column values. The identity value is never rolled back even though the transaction that tried to insert the value into the table is not committed. For example, if an INSERT statement fails because of an IGNORE_DUP_KEY violation, the current identity value for the table is still incremented.

Be cautious about using IDENT_CURRENT to predict the next generated identity value. The actual generated value may be different from IDENT_CURRENT plus IDENT_INCR because of insertions performed by other sessions.


CREATE TABLE T1 (
ID int IDENTITY(1,1)PRIMARY KEY,
Name varchar(20) NOT NULL
)

INSERT T1
VALUES ('A')
INSERT T1
VALUES ('B')
INSERT T1
VALUES ('C')

CREATE TABLE T2 (
T2ID int IDENTITY(1,1)PRIMARY KEY,
ID int
)

CREATE TRIGGER Trig_T1
ON T1
FOR INSERT AS
BEGIN
INSERT into T2
select ID from inserted
END

FIRE the trigger and determine what identity values you obtain with the @@IDENTITY,IDENT_CURRENT and SCOPE_IDENTITY functions.*/
INSERT T1 VALUES ('Rosalie')

SELECT SCOPE_IDENTITY() AS [SCOPE_IDENTITY]
GO
SELECT @@IDENTITY AS [@@IDENTITY]
GO
SELECT IDENT_CURRENT ('T1') AS T1_Identity;
GO
SELECT IDENT_CURRENT ('T2') AS T2_Identity;

Hive - Data Warehousing & Analytics on Hadoop

Hive is an open source, peta-byte scale date warehousing framework based on Hadoop that was developed by the Data Infrastructure Team at Facebook that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL.


Hive architecture:
Hive organizes data in tables and partitions. A good partitioning scheme allows Hive to prune data while processing a query and that has a direct impact on how fast a result of the query can be produced. Behind the scenes, Hive stores partitions and tables into directories in Hadoop File System (HDFS).


Hive comprises of the following major components:

-Metastore: To store the meta data.
-Query compiler and execution engine: To convert SQL queries to a sequence of map/reduce jobs that are then executed on Hadoop.
- SerDe and ObjectInspectors: Programmable interfaces and implementations of common data formats and types.
-UDF and UDAF: Programmable interfaces and implementations for user defined functions (scalar and aggregate functions).
-Clients: Command line client similar to Mysql command line and a web UI.

Data Flow into Hadoop Cloud:




For more Information:
http://www.vldb.org/pvldb/2/vldb09-938.pdf

Business Analytics: Good Data and Poor Data

Today, most organizations use data in two ways:
Transactional/Operational use (“running the business”), and Analytic use (“improving the business”).

Good business demands GoodData.
Business analytics can provide amazing insights into how an organization is operating -- in hindsight, with insight and with foresight. But one must be attentive to the quality of the data being analyzed and put first things first. Step one is to check the validity of the data, ensure its quality and completeness. Step two is to ask those key questions that help provide the information needed to make informed decisions. Internal auditors, armed with analytic technologies of their own, can provide a huge amount of assistance in determining data quality and addressing the risk of drawing incorrect conclusions based on bad data.

It is of great value to any enterprise risk management program to incorporate a program that includes processes for assessing, measuring, reporting, reacting to, and controlling different aspects of risks associated with poor data quality.

Data quality is a critical prerequisite to effective business analytics. Poor data quality jeopardizes the performance and efficiency of operational systems. It undermines the value of analytic and business intelligence systems upon which
organizations rely to make key decisions. Decisions based on poor data can result in direct financial loss.

Business leaders need to pay serious attention to the accuracy, quality and reliability of their data. The most obvious cause for poor data quality is data entry. If an organization has no standards or IT controls for how data is entered into a system, the data will quickly reflect their lack. It is in this way that duplicate entries are made in master data.

While Business Intelligence tools can create beautiful and compelling dashboards and graphics, if the data that the tools rely on is of poor quality, their results are
meaningless, or at least potentially badly flawed.
Data cleansing and data quality are important in order to ensure quality results from business intelligence analytics.

You can never fully predict how business users will want to analyze their data, so give them complete freedom to drill down in any direction they choose.Deliver them something good in a week as opposed to something great in six months.Good data is attained by integrating multiple data sources, deriving a ‘single version of the truth,’ and putting that good data (and unstructured content) into a data warehouse where the BA/BI tools can perform their magic. DDD (data-driven decision making) begins and ends with good data.Analytics applications that nicely present dashboards, scorecards, historical trends, predictive analys, and give me actionable insights, can all benefit from good data. Good data begins with data integration, data quality, and a good data warehouse.

A Whole New Mind: Why Right-Brainers Will Rule the Future charts the rise of right-brain thinking in modern economies and describes the six abilities individuals and organizations must master in an outsourced, automated age

Daniel Pink references three prevailing trends pointing towards the future of business and the economy:
1. Abundance (consumers have too many choices, nothing is scarce)
2. Asia (everything that can be outsourced, is)
3. Automation (computerization, robots, technology, processes).

This brings up three crucial questions for the success of any business:

1. Can a computer do it faster?
2. Is what I'm offering in demand in an age of abundance?
3. Can someone overseas do it cheaper?

When these questions are present, creativity becomes the competitive difference that can differentiate commodities.
The future belongs to a different kind of person with a different kind of mind: artists, inventors, storytellers-creative and holistic "right-brain" thinkers whose abilities mark the fault line between who gets ahead and who doesn't. Drawing on research from around the world, Pink outlines the six fundamentally human abilities that are absolute essentials for professional success and personal fulfillment-and reveals how to master them

1. Design - Moving beyond function to engage the sense.
2. Story - Narrative added to products and services - not just argument. Best of the six senses.
3. Symphony - Adding invention and big picture thinking (not just detail focus).
4. Empathy - Going beyond logic and engaging emotion and intuition.
5. Play - Bringing humor and light-heartedness to business and products.
6. Meaning - the purpose is the journey, give meaning to life from inside yourself.


The book A Whole New Mind: Why Right-Brainers Will Rule the Future by Daniel H. Pink, left- directed thinking leads to a analytical person, whereas right-directed thinking leads to creative person.

Left-directed thinking:
America is currently organized around a cadre of accountants, doctors, engineers, executives and lawyers. These "knowledge workers" excel at the ability to acquire and marry facts to data, and these abilities are typically accrued through a series of standardized tests such as the PSAT, SAT, GMAT, LSAT and MCAT.

Right directed thinking:
Design, empathy, play, and other "soft" aptitudes have become the focal point for individuals and companies that want to stand out above the others in a crowded marketplace. Look no further than Apple's design-triumph, the physically appealing and emotionally compelling iPod, for quick confirmation of this notion!

Dan advocates to leave the left directed thinking to Asia as it is available in abundance and cheap, and direct educational system towards right directed thinking to turn out creative people like Steve Jobs and Bill Gates.