Mapping your way to data success

Data consistency and quality are critical aspects of any data-driven organisation. Data mapping, data cleansing and data duplication are all key aspects to enhancing business success.They ensure that data is accurate, reliable and usable for decision-making.  We’ll explain how to use each of these quality control techniques to improve your data and business reporting. 

Quick Links: 

• Why data organisation matters more than you think
• What is Data Mapping? 
• What is Data Cleansing?
• What is Data Duplication? 
  
  
  

"I've seen firsthand the transformative power of effective data mapping and data organisation. When done right, it's like building a sturdy foundation for your business to grow from. It ensures that your data is accessible, reliable and actionable, enabling you to make informed decisions, optimise operations and deliver exceptional customer experiences."

"Think of it as investing in your business's future: the more organised and mapped your data is, the more opportunities you'll uncover and the more confident you'll be in your strategic direction." 

Kye Bessant, our Solutions Architect from Modern Visual

Why data organisation matters more than you think

Data organisation is often overlooked, but it's a crucial component of any successful business. Well-organised data provides a solid foundation for informed decision-making, efficient operations and improved customer experiences. By organising your data effectively, you can:

• Improve data accessibility: Easily locate and retrieve the information you need when you need it.
• Enhance data quality: Ensure that your data is accurate, consistent and reliable.
• Strengthen data security: Protect sensitive information from unauthorised access or breaches.
• Facilitate data analysis: Gain valuable insights from your data through effective analysis.
• Enhance customer satisfaction: Provide better customer service and personalised experiences.
• Optimise business processes: Streamline workflows and improve efficiency.
• Improve reporting: This ensures that the data used for reporting is reliable and trustworthy. Making better business decisions overall. 

What is Data Mapping?

Data mapping is the process of defining the relationships between data elements in two or more systems. It ensures that data fields from one system are correctly aligned with the corresponding fields in another. This prevents inconsistencies that can arise when data is transferred or integrated.

The benefits of data mapping, cleansing and deduplication for your businesses:

Data Mapping 

• Improved customer experience: Accurate data mapping ensures that customer information is consistently displayed and updated across different systems. This prevents frustrating inconsistencies and errors that can lead to a poor customer experience.
  
  
• Enhanced operational efficiency: By streamlining data transfer and integration, data mapping helps businesses reduce manual effort and errors, leading to increased efficiency and productivity.
  
  
• Better decision-making: Accurate and consistent data enables businesses to make informed decisions based on reliable information. This can lead to improved marketing campaigns, optimised inventory management and more effective customer service.

Key considerations in data mapping:

• Field identification: Identify the corresponding fields in each system that contain the same or similar data. Field identification helps create a unified view of the data across multiple systems. This means that regardless of which system you're looking at, you'll see consistent information.
  
  
• Data type matching: Ensure that the data types of the fields are compatible (e.g., both are text, numbers, or dates). Ensuring that data types match prevents errors during data transfer and integration. For example, trying to combine a text field with a numerical field can lead to incorrect results. Consistent data types make it easier to perform data analysis and calculations. It prevents errors and ensures that results are accurate and meaningful.
  
  
• Value mapping: If there are differences in the values used in the two systems (e.g., different codes for countries or product categories), define rules for mapping these values. Value mapping helps standardise data across different systems, making it easier to compare and analyse information. Consistent values ensure that reports and analyses are accurate and comparable across different systems. By defining clear mapping rules, you can prevent errors and inconsistencies in data.
  
  
• Business rules: Consider any business rules or constraints that apply to the data being mapped. Business rules ensure that data is consistent with the organisation's policies and procedures. By considering business rules, you can make informed decisions based on accurate and relevant data.

What is Data Cleansing?

Data cleansing is the process of identifying and correcting errors, inconsistencies and inaccuracies in data. It involves cleaning up data before it is integrated or used for analysis. Clean data is essential for accurate data analysis and reporting. By removing errors and inconsistencies, businesses can gain valuable insights from their data.

Common data cleansing tasks:

• Incorrect values: Correct errors in data values, such as typos or invalid formats.
  
  
• Inconsistencies: Resolve inconsistencies in data, such as conflicting values for the same field.
  
  
• Duplicates: Identify and remove duplicate records to avoid redundancy.
  
  
• Standardisation: Standardise data formats and values to ensure consistency across different systems.
  
  
• Missing values: Fill in missing values using business rules or imputation techniques.
  
  

Business Rules:

Business rules are specific guidelines or constraints that are based on the organisation's knowledge and expertise. They can be used to fill in missing values in a more informed manner. For example:

• Known values: If you know the possible values for a missing variable based on business knowledge, you can directly fill in the missing values.
• Default values: Assign a default value to missing values based on organisational standards or preferences.
• Logical rules: Use logical rules to infer missing values based on other related variables. For example, if a customer's address is missing but their city and state are known, you could use a geocoding service to infer the missing address.

 Imputation techniques:

Imputation techniques involve filling in missing values with estimated or calculated values. Here are some common techniques:

• Mean/Median/Mode imputation: Replace missing values with the mean, median or mode of the respective column. This is simple but can introduce bias if the distribution is skewed.
  
  
• Hot deck imputation: Assign missing values with values from a randomly selected donor record. This is useful when the data has a lot of variability.
  
  
• Cold deck imputation: Assign missing values with values from a specific donor record, such as the nearest neighbour or a record with similar characteristics.
  
  
• Regression imputation: Use regression analysis to predict missing values based on other variables in the dataset. This is effective when there's a strong relationship between the missing variable and other variables.
  
  
• Multiple Imputation: Create multiple imputed datasets by filling in missing values with different values. This helps to account for uncertainty in the imputation process.

Choosing the right method: The best method for filling in missing values depends on the nature of the data, the reasons for the missing values and the goals of the analysis. It's often a good idea to explore multiple techniques and evaluate the impact on the results.

What is Data Duplication?

Data duplication occurs when the same data is stored in multiple locations, often in different formats or with inconsistencies. This can lead to data quality issues and inefficiencies. Eliminating duplicate data can significantly reduce storage requirements, saving businesses money. By removing duplicate records, businesses can ensure that their data is more accurate and consistent.

Strategies for handling data duplication:

• Prevention: Implement measures to prevent duplication, such as unique identifiers or data validation rules.
• Detection: Use tools or algorithms to identify duplicate records.
• Resolution: Decide how to handle duplicates, such as merging them into a single record or deleting one of them.

Data validation rules are specific criteria or conditions that data must meet to be considered valid. They can be applied to individual fields or entire records. Examples of validation rules include:

• Required fields: Ensure that certain fields must be filled in.
• Data type validation: Verify that data is of the correct data type (e.g., text, number, date).
• Range validation: Check that values fall within a specified range.
• Format validation: Ensure that data adheres to a specific format (e.g., email address, phone number).
• Consistency checks: Verify that data is consistent with other related data.
• Lookup checks: Ensure that values exist in a predefined list or lookup table.

A unique identifier is a value that uniquely identifies a specific record or entity. It should be assigned to each record in a way that guarantees its uniqueness. Examples of unique identifiers include:

• Primary keys: In databases, primary keys are unique columns or combinations of columns that identify each record.
• Serial numbers: Assigned sequentially to each record.
• UUIDs (Universally Unique Identifiers): Unique 128-bit numbers that are generated randomly.
• Hash functions: Create unique values based on the contents of a record.

By using data validation rules and unique identifiers, organisations can significantly reduce the risk of data duplication and maintain the integrity of their data.

Key factors to consider when handling duplicates:

• Data quality: Assess the quality of the duplicate records to determine which one is more reliable.
  
  
• Business rules: Consider any business rules or preferences for handling duplicates.
  
  
• Cost-benefit analysis: Evaluate the cost of resolving duplicates versus the potential benefits of maintaining data integrity.

By effectively addressing data mapping, cleansing and duplication, you can ensure that your data is consistent, accurate and reliable. This in turn, enables better decision-making, improved operational efficiency and enhanced customer satisfaction.