Data Profiling

Too few organisations profile their data. Objective Marketing’s expertise enables businesses to gain better understanding of their customer base. Simply profiling “purchasers” and then overlaying this information against “window shoppers” can bring some eye opening information. Often, large numbers of “window shoppers” are continually mailed, but if they do not match the “purchasers” profile, then this is hugely wasteful.

Profiling data is not expensive
It is not a costly exercise to profile data, and the results give you valuable information about your customers or potential customers that are held on your database.

Through poor marketing you could have attracted a database full of individuals who cannot afford your products or services. Unless you profile them and look at their demographics (sex, age, income etc) you are never going to know if they are your ideal target market. How ineffective can it be to mail a list of people, the majority of which could never afford your product or service?

Similarly, data improvement efforts must start with an understanding of the integrity of the data. The first phase or methodology is data profiling (also known as “data discovery”). With data profiling, you can:

Discover the quality, characteristics and potential problems of information before beginning data-driven projects

Drastically reduce the time and resources required to find problematic data

Allow business analysts to have more control on the maintenance and management of data

Catalogue and analyse metadata and discover metadata relationships

Through data profiling, as you uncover non-standard or duplicate data, you can immediately develop business rules to correct these issues. This shortens the time between data discovery and data correction; helping you obtain more from your data profiling efforts.

The first step to building better data
As the initial step to any data quality program, data profiling provides a wealth of information about the data that you have. Objective Marketing data profiling solutions automatically identify data quality issues in a variety of ways, including:

Basic statistics, frequencies, ranges and outliers
Identify multiple spellings of the same content
Discover and validate data patterns and formats
Numeric range analysis
Identify and validate redundant data
Identify duplicate name and address, and non-name and address information
Validate data specific business rules within a single record or across sources

We hope you found this information useful. We would like to remind you that this subject is best served, and more effective as part of a complete marketing plan or strategy, rather than in isolation. For further expert advice please contact us to discuss your requirements or post a question in our blog.

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