Data Correction

Do you struggle with errors, incomplete or imprecise information in your systems? Is there an urgent need for your business to address data quality issues by correcting data? Whatever the case, you can now maintain accurate, reliable and error-free data with our data correction service.

What Is Data Correction?

Data correction entails a series of tasks to correct errors following specific rules and standards. Such tasks include:

• Data cleaning (correct typos, unnecessary spaces, commas or other marks),
• Data standardization (transformation or data to look similar)
• Data deduplication (identify records existing more that once)
• Data matching (matching records from different tables / databases)
• Filling blanks (filling blank fields on specific records, based on specific rules)
• Useless data deletion (delete records that can not be corrected or are useseless for the business)

The execution of such tasks may vary depending on the quantity and complexity of data, and the checks/corrections that need to be made.

At Deep Dive we usually follow three stages to ensure top quality:

1. Data standards definition – Correction rules outline
2. Data preparation & tools configuration
3. Core correction process

Data Preparation & Tools Configuration

After having defined the data standards and data correction rules, we move on to the next step: data preparation. Depending on the case, we might need to:

• export data from one or more sources
• make some initial filtering or transformation
• make a first data analysis to make sure that we are ready to proceed to the next stage. 

Then, it is time to configure our analytics tool so as to fit the specific data and serve the checks we need to run. Depending on the tool used, this may include writing a few code scripts.

Common data correction tasks

From a technical point of view, there are some specific tasks that can be done during the data cleansing process, depending on the outcome of the previous stages. Some usual tasks are:

• correcting typos based on grammar / languages or other generic rules
• correcting format errors based on specific standards
• homogenize information by transforming data according to rules
• deduplicating information by deleting or merging data records
• data removing by deleting or just flagging records that can not be corrected
• matching data from different fields, tables or systems

Some systems may offer some functionalities to execute this kind of corrections, in a way that those can be done directly in a system, in the production environment. This is usually possible for limited number of corrections that may be done manually. In some other cases mass / semi-automatic edits are possible inside the system. Nevertheless, in most cases data have to be exported, processed with a specific algorithm and re-imported to the system. Depending of the system's technology and architecture this task may vary, presenting different difficulties and risks. Some systems are very rigid and are not helpful in such a project, presenting obstacles in online corrections, in exporting and re-importing processed data.