Data is a decisive catalyst for performance, provided it is accurate, useful, relevant and reliable, so as to streamline decision-making. And this is where data qualification comes in, a continuous and rigorous process that evaluates, verifies and validates the data contained in a DB by following several key steps, from identifying the data most relevant to the business to updating and enriching the data.
Objective: implement a global approach to data quality management, ensuring that companies have accurate, actionable information to support their strategic and operational objectives.
Throughout this article, we will explore the specificity of database qualification, distinguishing it from related notions such as Data Cleaning, Data Profiling and Data Enrichment. We'll also present a concrete example of database qualification in the e-commerce sector, highlighting the benefits associated with implementing this approach and the risks of neglecting it.
What is database qualification?
Database qualification is the process of evaluating, verifying and validating the quality, relevance and accuracy of the data contained in a database. This process usually involves several key steps, such as :
- Identification of data relevant to the company or project in question
- Verification of data accuracy and consistency, particularly with regard to data entry errors, missing data and duplicates.
- Validate data to ensure it meets the quality and relevance standards set by the company.
- Updating and enriching data to ensure that it is up-to-date and complete.
It's important to note that database qualification is an ongoing process, requiring regular monitoring and constant updating to ensure that data remains reliable and relevant over time. This is part of a global approach to Data Quality Management (DQM), aimed at ensuring that companies have accurate, relevant and usable information.
Disambiguation: database qualification vs. related concepts
To better understand the specificity of database qualification, it is essential to distinguish it from related concepts such as Data Cleaning, Data Profiling and Data Enrichment. Although these concepts are related and often used in conjunction, they differ in their objectives, scope and methods.
#1 Data Cleaning (or Cleansing)
Data cleansing involves detecting and correcting errors and inconsistencies in data. It is an essential step in the database qualification process, involving the elimination of duplicates, the correction of input errors and the standardization of data formats.
#2 Data Profiling
Data Profiling is the analysis of data to determine its quality, structure and patterns. This process enables us to better understand the data and identify anomalies or potential problems. Data Profiling contributes to database qualification by providing an overview of the current data situation, but does not focus directly on resolving identified problems.
#3 Data Enrichment
Data Enrichment consists of adding additional information or improving the quality of existing data by supplementing, enriching or updating it using external sources or advanced analysis methods.
Data qualification: a concrete example in e-commerce
Let's take the example of a fictitious e-commerce company, which we'll call "StyleDirect", specializing in the sale of fashion clothing and accessories. StyleDirect has a customer database containing information such as first and last name, e-mail address, telephone number, postal address, purchase history and customer preferences.
Qualifying this database is crucial to ensuring the effectiveness of marketing campaigns, customer satisfaction and, more broadly, company profitability.
Here's a detailed example of the database qualification process for StyleDirect.
#1 Identification of relevant data
StyleDirect determines which data is essential for its activities, for example: contact information, purchase history and customer product and communication preferences.
#2 Data Cleaning
- Duplicate removal: StyleDirect identifies and removes duplicate customer records using duplicate detection algorithms based on criteria such as name, email address and phone number.
- Error correction: StyleDirect checks and corrects input errors such as typos in names and emails using automatic validation and correction techniques.
#3 Data Profiling
StyleDirect analyzes the quality, structure and patterns of its database to better understand the data and identify anomalies or potential problems. For example, it can detect customers with an unusually high purchase history, which may indicate fraud or a technical problem.
The company can also identify customers who return an abnormally high percentage of their orders... a behavior that can reveal several problems such as :
- Customer dissatisfaction: frequent returns could mean that customers are dissatisfied with the quality of products, the accuracy of website descriptions or the suitability of items for their needs.
- Abuse of the return policy: some customers may exploit StyleDirect's return policy to temporarily use items without paying for them, resulting in additional costs for the company and potential loss of revenue.
- Logistical problems: a high returns rate could also reveal problems with inventory management, packaging or shipping, leading to order errors and customer dissatisfaction.
#4 Customer segmentation
After cleaning and assessing the quality of the data, StyleDirect segments its customer database according to specific criteria such as :
- Amount and frequency of past purchases ;
- Product preferences (categories, brands and styles) ;
- Engagement with marketing communications (email open rates, link clicks, etc.).
#5 Data Enrichment
StyleDirect enriches its database with additional, useful demographic information such as age, gender or geographical location, using external data sources or predictive analysis techniques.
The company will also complement customer product and communication preferences by analyzing interaction data on its website: pages visited, items put in the basket and searches carried out.
#6 Data validation and updating
StyleDirect regularly validates the data in its database to ensure that it remains up-to-date and relevant over time. For example, it can send confirmation emails to inactive customers, or update purchase data after each new transaction.
By following this detailed database qualification process, StyleDirect ensures that its data is accurate, relevant and actionable to support decision-making. With qualified data, StyleDirect can better target marketing campaigns (higher ROI), improve customer satisfaction and optimize resources, resulting in better overall performance and increased profitability.
Summary: why is it imperative to qualify your database?
In this table, we have compiled the decisive advantages of data qualification, as well as the risks associated with an unqualified database.
| Element | Interest | Risk of unqualified data |
| Data accuracy | Reliability and accuracy of stored information | Erroneous decisions based on inaccurate data |
| Better decision-making based on reliable data | Ineffective or counter-productive marketing efforts | |
| Loss of customer confidence and damage to corporate reputation | ||
| Data relevance | Identify data crucial to the company and its objectives | Inefficient allocation of resources to irrelevant data |
| Focus on the most useful information for decision-making | Decision-making based on obsolete or irrelevant information | |
| Difficulty identifying and solving operational problems | ||
| Data integrity | Data consistency and homogeneity between different sources and systems | Inconsistencies and communication errors between systems and departments |
| Simplified data integration and consolidation processes | Data analysis and processing difficulties due to inconsistencies | |
| Waste of time and effort resolving data problems | ||
| Updating data | Keeping information up-to-date and relevant over time | Decisions based on obsolete or outdated information |
| Adapt to market changes and evolving customer needs | Difficulty in anticipating and responding to market trends and opportunities = latent loss of earnings | |
| Deterioration in the effectiveness of marketing and sales strategies | ||
| Data accessibility | Easier access and understanding of data by users | Loss of time and productivity in searching for and understanding data |
| Improved collaboration and information sharing between departments | Poor communication and disconnected decision-making between departments (misalignment) | |
| Difficulty in deriving actionable insights from data |
Data Enso: the zen solution for your Data
Whether you're in retail, bancassurance, automotive, industry, B2B, high-tech or public administration , Data Enso mobilizes its expertise to transform your Data capital into a real performance gas pedal. We work on :
- Reliability of collected data: data entry assistance, automatic correction in real time
- Verification of the veracity of collected data: real-time verification tool for emails, phone numbers and companies
- Enrich your databases: retrieve company information sheets with few initial fields to complete
- Streamline collection systems by reducing the number of fields to be filled in
- Clean and correct your existing data with our batch solutions.
We offer you a free, no-obligation 30-day trial with access to all Enso Data APIs (EnsoEmail, EnsoPhone, EnsoB2B and EnsoAdress).
