Data mining helps businesses process huge volumes of data to spot common patterns or discover new information about their customers as a whole. Without automation, dealing with this amount of big data wouldn’t be possible, but marketing automation technology alone lacks the executive functioning to guide a data mining program.
When creating tables, each column can store one type of data and that is defined during the table creation.
|Binary||Binary, length 0 to 8,000 bytes|
|Char||Character, length 0 to 8,000 bytes|
|Datetime||8-byte datetime. Range from January 1, 1753 through December 31, 9999, with an accuracy of three-hundredths of a second|
|Image||Variable length binary data. Maximum length 2,147,483,647|
|Integer||4-byte integer. Value range from -2,147,483,648 through 2,147,483,647|
|Money||8-byte money. Range from -922,337,203,685,477.5808 through +922,337,203,685,477.5807, with accuracy to a ten-thousandth of a monetary unit.|
|Numeric||Decimal – can set precision and scale. Range -10^38 +1 through 10^38-1|
|Smalldatetime||4-byte datetime. Range from January 1, 1900, through June 6, 2079, with an accuracy of one minute|
|Smallint||2-byte integer. Range from -32,768 through 32,767|
|Smallmoney||4-byte money. Range from 214,748.3648 through +214,748.3647, with accuracy to a ten-thousandth of a monetary unit.|
|Text||Variable-length text, maximum length 2,147,483,647|
|Tinyint||1-byte integer. Range from 0 through 255|
|Varchar||Variable-length character, length 0 to 8,000 bytes|
Our data science team at Reach Marketing uses a number of data mining techniques to reveal insights about customers:
A kind of pattern recognition, cluster detection looks at vast data sets to see areas around which data points tend to group. These patterns are invisible at the level of individual prospect interactions, and only powerful databases are able to see them on the macro level.
If cluster detection looks for crowds, anomaly detection looks for any data point that stands out in a crowd. By finding outliers and anomalies, our data mining experts can explore new markets or see nascent trends before the competition even knows they’re starting.
Existing data can be a powerful predictor of future outcomes. Using regression to process customer and prospect data can predict engagement, retention, sales cycle length, and more.
More formally, an ERD (Entity Relationship Diagram). But the relationships are not shown. They have to be explicitly defined via the SSMS interface, or programmatically. This requires PRIMARY and FOREIGN keys.
So what do those symbols mean? They represent the number of matching records on each side
of the relationship.
Primary Keys must be unique. No value can repeat within a table. Although this is probably obvious in this case, there are many exceptions in the business world that will result in this requirement to be false.