Quelles sont les étapes clés pour effectuer des contrôles d’intégrité des données ?

I don’t mind, let it be as it is - everyone has their own role.. As a data analyst, I often begin a project without a clear understanding of how the data is organized or how it should be structured. This approach sets me apart from many others in the field. Over a period of 1-4 months, I immerse myself in the data and the company's processes, gradually developing a unique model and organizing the data in a way that meets the company's specific needs. Eventually, this structured data begins to function seamlessly. However, not all companies are open to such flexible approaches, as they often prefer standardized procedures.

The best practices to preserve data integrity are: 1.Validate Input: While the info provider may be end-user or another software system, they can also come from a malicious individual. Hence, validation will verify that the input is correct & reliable 2. Validate Data: After input verification, the datasets need to be validated by ensuring, the data process is not corrupted & that the incoming metrics are accurate 3. Remove Duplicate entries: Any duplicate sources of data should be promptly removed 4. Perform regular backups: Backing up data regularly will prevent accidental data loss & unintentional alterations 5. Control Access: The security systems should be in place to prevent hackers and unauthorized users from accessing information

In my journey with AI, especially on projects involving computer vision models like ResNet, Mask R-CNN, and MobileNet, I've learned the true value of data integrity. It all starts with defining what good data looks like and making sure it mirrors the real world accurately. Then, it's about carefully choosing and prepping this data. Cleaning and checking the data helps weed out mistakes and biases, making sure the AI works right. Keeping an eye on the data and documenting everything keeps it trustworthy. Sticking to high data integrity means being thorough and aiming for excellence, ensuring our AI models are effective, fair, and built on a solid base. This approach is key to developing AI solutions that people can really count on.

Start by defining the data quality criteria that are relevant to your specific analytics goals. Common criteria include accuracy, completeness, consistency, timeliness, validity, and uniqueness. Prioritize these based on your project requirements. Understand where your data comes from. Identify the sources (databases, files, APIs) and the formats (CSV, JSON, SQL) in which the data is stored. This step helps you prepare for subsequent checks. Profile your data to understand its characteristics, such as data types, distributions, and missing values. Assess the quality of each attribute and identify potential issues.

In my experience, these are the key steps in performing data integrity checks: - Understand Data Structure: Know how your data is organized and its expected format. - Identify Key Data Fields: Determine critical data fields that need validation. - Define Validation Rules: Establish rules for each data field (e.g., data type, length, range). - Develop & Reference Organization/department-wide data integrity protocols: Employ data integrity standards and protocols for consistent outcomes. - Document Findings: Keep records of data integrity checks and any corrective actions taken. - Implement Access Controls: Limit access to data to prevent unauthorized modifications.

After defining data quality criteria, the subsequent step is to identify data sources and formats for your analytics project. Pinpoint the origins (databases, files, APIs) and structures (CSV, JSON, XML) of the data. This process aids in gauging data scope, availability, and accessibility while anticipating challenges in integration and transformation.

Identifying data sources and formats in a Google Cloud Platform (GCP) project entails pinpointing sources like BigQuery databases and file formats such as CSV or JSON from Google Cloud Storage. This enables efficient integration within GCP services like Dataflow or Dataprep, streamlining data processing and analysis workflows.

In my experience identifying data sources and formats is a crucial step vevuase it helps you to build the data pipeline in a effective way. In my opinion data sources can be categorized into external and internal and classification is very important. Data format is also important for effective analysis, enterprises are geneating data from variety ways like structure, semi structured and un-structured data. Overall it's all about the choosing the right data source and understanding their formats are foundational to any project success.

Identifying data sources and formats is pivotal for a successful data analytics endeavor. It provides clarity on where data originates and how it's structured, enabling efficient data acquisition and processing. This understanding is essential for anticipating integration challenges and ensuring compatibility, ultimately optimizing the analytics workflow for more accurate and insightful results.

Identifying data sources and formats is crucial for successful data integrity checks 📊. Data sources encompass databases, files, APIs, web pages, or surveys, while data formats include CSV, JSON, XML, or PDF. Understanding these aspects clarifies data scope, availability, and accessibility, as well as potential integration and transformation challenges. By comprehensively identifying sources and formats, you lay the foundation for effective data analysis and decision-making in your project 🌟.

Data profiling and assessment serve as indispensable tools for gaining comprehensive insights into the dataset. By scrutinizing its attributes and quality, analysts can discern underlying trends, patterns, and irregularities. Moreover, this process aids in the prioritization of data problems, thereby facilitating targeted resolutions to enhance overall data integrity.

Following the identification of data sources and formats, the third step involves performing data profiling and assessment. Data profiling entails analyzing metadata and statistics, including types, values, ranges, distributions, frequencies, patterns, and outliers. Simultaneously, data assessment involves evaluating and comparing data quality against predefined criteria, identifying issues like missing values, duplicates, errors, inconsistencies, or anomalies. These processes provide valuable insights, understanding of the data, and aid in prioritizing and resolving data problems.

Data profiling involves analyzing data structure & content (types, values, ranges) to understand characteristics. Assessment compares this profile to predefined data quality rules (completeness, accuracy, consistency) to identify and quantify potential errors and inconsistencies. This step helps prioritize data cleaning efforts for a more reliable data set.

Performing data profiling and assessment is the third crucial step in ensuring data integrity 📊. Data profiling involves collecting and analyzing metadata and statistics, including data types, values, ranges, distributions, frequencies, patterns, and outliers. Data assessment entails evaluating data quality against predefined criteria and identifying issues like missing values, duplicates, errors, inconsistencies, or anomalies. These processes provide insights into the data and prioritize problem resolution, ensuring its reliability and suitability for analysis and decision-making 🌟.

As part of data assessment, trend analysis and pattern selection by ignoring abnormalities is one of the critical aspects in model building. These data patterns to be reviewed and validated periodically to maintain the accuracy of data model. For example, consider a week or a 10 days CPU utilization pattern ignoring one-time utilization spikes will help in building server utilization CPU model more accurate when compared with a model build with a monthly data with inconsistent abnormalities.

Validate the data against other data sources (if available) and also ask business users of the data if it matches with what they have in their reports/records

Implementing data cleansing and validation is critical to ensuring the accuracy and reliability of the data used in analytics. By systematically identifying and rectifying errors, inconsistencies, and inaccuracies, organizations can mitigate the risk of flawed insights and erroneous conclusions. Moreover, this process not only enhances data integrity but also streamlines subsequent analysis and decision-making processes, ultimately maximizing the value derived from data-driven initiatives. In essence, data cleansing and validation serve as indispensable safeguards, bolstering the trustworthiness and effectiveness of data-driven strategies.

Data cleaning, the first component of this phase, involves a series of meticulous procedures designed to purify the dataset. This may include eliminating duplicates, correcting spelling errors, standardizing formats, and replacing or deleting missing or aberrant values. This process requires in-depth knowledge of the application domain, technical knowledge about the nuances and idiosyncrasies of the data in question. Furthermore, data cleansing is not a one-time act, but an ongoing process that reflects the dynamic nature of data and organizations. Data validation, in turn, ensures that the cleaned data still meets the necessary quality criteria and is suitable for the intended analytical purposes.

For data cleansing and validation, monitoring utility can be implemented which keeps on checking the quality of data depending on various required checks. A scheduler can be created to run the utility for data cleansing and validation and alert accordingly for any faults and if cleansing is needed.

Implementing data cleansing and validation is the pivotal fourth step in ensuring data integrity 🧹. Data cleansing involves correcting and enhancing data quality through techniques like removing, replacing, standardizing, formatting, or enriching data. Data validation verifies that data meets quality criteria and analytics requirements, employing methods such as rules, constraints, checks, or tests. These processes enhance data integrity, preparing it for further analysis and processing, thereby facilitating informed decision-making 📊.

Documenting and monitoring data quality is akin to keeping a watchful eye on our data's health and history. It involves creating records of data sources, formats, and quality criteria, much like keeping a detailed medical chart for our data. Monitoring entails regularly checking data against predefined quality metrics, ensuring it remains accurate, complete, and reliable over time. It's like conducting routine check-ups to catch any issues before they escalate. By documenting and monitoring data quality, we maintain transparency, accountability, and trustworthiness in our data, enabling informed decision-making and business continuity. Let's keep our data quality in check—it's the lifeline of our operations! #DataQuality

Monitoring data quality now benefits from new tools democratizing access to audit reports and intelligence, making it easier for all business users to interpret data health. These tools generate user-friendly reports highlighting accuracy, completeness, and consistency, facilitating proactive management of data quality. By simplifying complex analysis, they empower users across the organization to engage with data quality initiatives, fostering a culture of continuous improvement. The ease of use and accessibility of these modern solutions are revolutionizing how companies maintain high-quality data and derive insights.

It is crucial to maintain transparency and accountability by documenting data quality standards, processes, and outcomes. This documentation should include the integrity checks performed, findings, and corrective actions taken. To monitor data quality over time, it is essential to establish key performance indicators (KPIs) and metrics. Regularly reviewing and analyzing these metrics helps identify trends, patterns, or emerging issues that require attention. Continuous monitoring enables proactive intervention and ensures ongoing data integrity.

Documenting and monitoring data quality constitute the essential fifth step in ensuring data integrity 📝. Documenting involves recording data quality metrics, indicators, scores, or dashboards to report results effectively. Monitoring encompasses tracking data quality performance, including trends, changes, or alerts. These practices facilitate communication and demonstration of data integrity while enabling timely identification and resolution of any arising data quality issues. By documenting and monitoring data quality, organizations uphold data reliability and support informed decision-making processes 🌐.

The fifth step for performing data integrity checks is to document and monitor data quality. Documenting involves recording and reporting data quality results, like metrics or dashboards. Monitoring includes tracking data quality trends or changes. These actions help communicate data integrity and address any issues over time.

Establishing data governance and policies is fundamental for ensuring data integrity. Begin by defining clear data governance frameworks that outline responsibilities, data standards, and procedures for managing and securing data across its lifecycle. Develop comprehensive data policies that set forth guidelines for data quality, privacy, compliance, and usage. These policies should be communicated across the organization to ensure uniform understanding and implementation. Regularly review and update governance frameworks and policies to adapt to changing regulatory requirements and business needs, thereby maintaining a strong foundation for data integrity checks.

Entering the final phase of the Data Analytics Life Cycle (DALC), termed the Data Presentation and Governance Phase, we focus on communicating insights derived from analysis to decision-makers. However, ensuring Confidentiality, Integrity, and Availability (CIA) of data remains paramount in this phase for robust data security. Implementing access restrictions to reports and datasets, adhering to data standards, and conducting regular audit checks contribute to maintaining data integrity. These measures are instrumental in ensuring the successful completion of data analytics projects while upholding data integrity and security standards.

Establishing data governance and policies serves as the crucial final step in ensuring data integrity 🏛️. Data governance encompasses roles, responsibilities, rules, and processes governing data quality and integrity organization-wide. Data policies are guidelines and standards enforcing data quality and integrity expectations and practices. These initiatives foster a data culture and environment conducive to maintaining data integrity. Additionally, they align and coordinate efforts and initiatives related to data quality and integrity across the organization, supporting informed decision-making and organizational goals 🌟.

Conduct regular reviews and maintenance activities to ensure ongoing data integrity. This includes periodic audits, updates to data integrity rules, and adjustments to validation checks based on evolving business needs.

Define governance frameworks, roles, and responsibilities for managing data assets. Establish policies and procedures for data stewardship, access control, data retention, and data lifecycle management. Implement data quality controls as part of the overall governance framework to ensure compliance with regulations and organizational standards.

Here are some ways to ensure data integrity 1. Validate the data. The data which we onboard should be validated and should meet company/org standards. 2. Data encryption: If the data on boarded is sensitive and needs to be protected from unauthorized access, it should be encrypted when in transit and rest 3. Should provide access to only specific users based on the roles 4. All the changes made to the data should be audited. 5. It should be backed up on regular basis for preventing data loss and corruption 6. Data should be cleaned and maintained and unnecessary noises should be removed.

All the above points mentioned are mandatory for data integrity. Understanding business requirements is crucial for determining data integrity needs. Categorizing and defining relationships between data, along with mastering SQL and Unix scripts, aids in profiling data. Utilizing data profiling tools or Python frameworks can further enhance data analysis. This holistic approach ensures comprehensive data integrity checks to maintain accuracy and reliability.

Step zero should be identification of Org's data maturity level for each individual line of business. Once that is done, it becomes easy to identify the next step. There will be continuous growth of data and at the same time, utilization of data will be dynamic. Hence all the steps are very much required. Focus must be to prioritize the steps properly and execute the task of each step in a timely manner.

With the advent of LLMs, data quality checks have become easier. In addition to the methodological approaches to ensuring data quality mentioned in the other steps, a good check would be to run the data through an LLM-powered data quality solution so you can uncover factual issues which rule-based systems miss. In addition, LLMs are also great at explaining issues. You can use ChatGPT for quick checks or use Anode for more detailed ones.

Hello folks, Think of data integrity checks like double-checking your homework to make sure you didn't miss anything. First, you need to understand what data integrity is all about. It's basically making sure your data is accurate, consistent, and reliable. Now, onto the steps: Check for Completeness: Make sure you've got all the pieces of your data puzzle. Just like making sure you have all the ingredients before baking the cake. Accuracy Verification: Double-check that each piece of data is correct. You don't want to use expired eggs in your cake, right? Consistency Check: Ensure that your data is in harmony with itself. Further comes the Validation, Security assessment and documentation.