Data Management
Our data management can take the data service to the next level. As well as improving the quality of your data, this service closes the data loop and generates a virtuous circle of continuous improvement. Our data processing and cleaning can produce some fantastic results for your campaigns but a deeper level of data management can ensure that this work is built on for future campaigns.

Ensuring that CRM systems and master databases are refreshed with the latest and best data and enriched with new data will improve ROI from campaigns, allow for more personalisation and relevance and enhance the customer experience.

DCK can achieve this by analysing activity on digital communications and managing direct mail returns and undeliverable mail. This analysis is used to constantly update the database.

Our data cleaning processes are used to bring a level of consistency to your data sets. Automated programmes are used to detect and remove corrupted or inaccurate data from your data sets.

Some of our automated data cleaning actions include:

• Deduping or removing duplicate records:
Our automated programmes check for duplicates within the data by comparing various data fields across records. Deduping can also be used across multiple databases, with the option of setting priorities on databases to show which records should be kept and which can be flagged for removal.
• Removal of incomplete records:
Automated programmes remove any records from the data that have only 1 line of address. Data is isolated and presented to the customer for review or processed according to pre-set rules.
• Removing duplicate cells within 1 address:
Checks are made to ensure no line of address is replicated within a record – i.e. 1 Sample Street, Sample Town, Sample Town, Sample County is amended to 1 Sample Street, Sample Town, Sample County.
• Processing invalid Symbols:
Ensuring data that includes symbols such as fadas do not become corrupted when output to excel. Checking for the appearance of any of these symbols in the data and flagging these to the customer for the correct action to be confirmed.
• Dealing with short addresses:
Short records can be reviewed to ensure the address are completed. In cases where the address can be amended, i.e. by checking on google maps, we can try to complete the address to ensure no extra records have to be removed from the data.
• Spell Check:
Spell checks are run across all first names in the database to flag any obvious misspellings in the data that has been supplied.
• Eircodes:
Dabases can be updated to include Eircodes.
• Postcodes:
Cases where Dublin postcodes are in separate cells are flagged and are amended to ensure that they appear on the same line of address, i.e.
• Max Address Lines:
Data is tested for the longest address line. This identifies any record that may have an unusually long address, where more than one address line has been entered into 1 cell. In cases like these, we can separate them out. For cases where no amendment can be made, we proof these records to ensure the address will fit correctly into the artwork provided and that no text overlaps onto any images provided on the layout.
• Missing Information:
All variables being used in the layout are checked for blank cells. For cases where a first name, or Salutation is missing, we identify these and send them back to the customer for the correct action to be identified.
• Duplicate Information In One Cell:
Checks are run to see if any cell have the same information repeated within them, i.e. 1 Sample Street Sample Street. This check will identify that the 1st line of address has been written into the same cell twice, and we can then remove the duplicate information.
• Record Locality:
An analysis is completed to identify if the record are being mailed to Ireland, UK or Rest of World.
• Postaim Sortation:
Data is be Postaim sorted into the correct Postaim areas to achieve postal discount rates for DM.
• Suppression List:
Suppression lists can by run against the database to highlight and extract records that are not to be mailed. Check are completed by comparing the mail file against the suppression file, and records that are not to be mailed are removed.

Cleaned and corrected data will improve the RoI for communications campaigns by ensuring only valid addresses are used. It also helps to ensure that communications are not being sent to an address if a customer has moved away and flags when someone needs to be contacted for an update of their details.