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.

Data Types

When creating tables, each column can store one type of data and that is defined during the table creation.

Data TypeDescription
BinaryBinary, length 0 to 8,000 bytes
CharCharacter, length 0 to 8,000 bytes
Datetime8-byte datetime. Range from January 1, 1753 through December 31, 9999, with an accuracy of three-hundredths of a second
ImageVariable length binary data. Maximum length 2,147,483,647
Integer4-byte integer. Value range from -2,147,483,648 through 2,147,483,647
Money8-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.
NumericDecimal – can set precision and scale. Range -10^38 +1 through 10^38-1
Smalldatetime4-byte datetime. Range from January 1, 1900, through June 6, 2079, with an accuracy of one minute
Smallint2-byte integer. Range from -32,768 through 32,767
Smallmoney4-byte money. Range from 214,748.3648 through +214,748.3647, with accuracy to a ten-thousandth of a monetary unit.
TextVariable-length text, maximum length 2,147,483,647
Tinyint1-byte integer. Range from 0 through 255
VarcharVariable-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:

Cluster Detection

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.

Anomaly Detection

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.


Your centralized data warehouse is phenomenally powerful, but it also needs to be friendly and approachable. Data marts balance power and ease of use by narrowing the window through which users view data. When our database marketing managers develop data marts for your team, they take into account who’s going to use it, how it will be used, and what customization options will optimize it.

What Defines a Data Mart?


A data mart is adapted to the team that uses it, so your marketing data mart might feature lead scores and nurture programs, while your sales team’s would include direct access to the CRM.


Although the user-facing side of a data mart is specialized, it’s plugged directly into the main data warehouse on the back end.


Data structures that don’t need to be part of the larger data warehouse are stored in the data mart to filter and condense the information traveling through it.

Database Diagram

Database Diagram

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.


ERD Diagram

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.

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