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What is the best way to validate and cleanse data during integration?

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Data integration is the process of combining data from different sources into a unified view. Data governance is the set of policies, standards, and practices that ensure the quality, security, and usability of data. To achieve effective data integration and data governance, you need to validate and cleanse your data during the integration process. This means checking the accuracy, completeness, consistency, and relevance of your data, and correcting any errors or anomalies. In this article, we will explore some of the best ways to validate and cleanse data during integration, and how they can benefit your data-driven initiatives.

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1 Why validate and cleanse data during integration?

Data validation and cleansing are essential steps to ensure that your integrated data is reliable, trustworthy, and actionable. Without them, you risk having inaccurate, incomplete, inconsistent, or irrelevant data that can compromise your analysis, reporting, decision-making, and compliance. For example, if you integrate customer data from different systems, you may encounter duplicate records, missing values, mismatched formats, or outdated information. These issues can affect your customer segmentation, targeting, personalization, and retention strategies. By validating and cleansing your data during integration, you can avoid these problems and improve your data quality and value.

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Kaushikkumar Patel

Data-Driven Solutions Architect | AWS Solutions | Credit Card Analytics
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Ensuring data integrity and quality requires through validation and cleansing to eliminate invalid, unparsed, duplicate, or NULL entries. This process is vital, especially in the finance sector, where precise data is crucial for accurate bank vs. Industry trend analysis.

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2 How to validate data during integration?

Data validation is a process used to verify that data meets predefined rules and criteria for integration projects. These rules and criteria can be based on business requirements, data standards, data models, or data quality dimensions, and can be performed before, during, or after the data is transferred or transformed. Common methods and tools for data validation during integration include schema validation tools or languages (e.g. XML Schema, JSON Schema, SQL DDL), business rule validation tools or languages (e.g. Drools, Excel, Python), and data quality validation tools or frameworks (e.g. Apache Data Quality, Talend Data Quality, DQpy). Schema validation checks if data conforms to the target schema structure and format; business rule validation checks if the data complies with specific business logic; and data quality validation checks if the data meets desired levels of quality such as accuracy, completeness, consistency, timeliness, or uniqueness.

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Ashu Dhall, CDMP?

Data & AI Strategist | Practitioner | President DAMA San Francisco (Bay Area)
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Effective data validation during integration involves performing & automating checks on data at various stages . This includes following validations: Schema: Verifying structure and format of data meet the target system. Data type: Ensuring that data values are of the correct type, e.g. numbers, dates, text Range : Checking that data values fall within acceptable ranges Consistency: Identifying & resolving inconsistencies between data from different sources. Completeness: Ensuring that all required fields are present. Uniqueness: Verifying that data values are unique within the target system. With these checks, we can improve the quality of data being integrated and reduce the risk of downstream problems.

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3 How to cleanse data during integration?

Data cleansing is the process of correcting or removing errors or anomalies identified during data validation. These may be caused by human errors, system errors, or data decay and can be performed manually or automatically depending on the complexity and volume of your data. Common methods and tools for data cleansing during integration include data correction, which involves modifying or replacing incorrect or inconsistent values in your data with tools such as Excel's Find and Replace, SQL's UPDATE, or Python's replace; data imputation, which involves filling in missing or null values with tools such as Excel's Fill, SQL's COALESCE, or Python's fillna; and statistical methods like mean, median, mode, or regression; and data deduplication, which involves identifying and removing duplicate records with tools such as Excel's Remove Duplicates, SQL's DISTINCT, or Python's drop_duplicates; as well as fuzzy matching or record linkage methods like Levenshtein distance, Jaccard similarity, or probabilistic matching to detect and merge near-duplicate records based on the similarity of their attributes.

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4 What are the benefits of validating and cleansing data during integration?

Validating and cleansing your data during integration can offer numerous advantages, such as improving data quality and consistency, reducing errors and risks, increasing data efficiency and productivity, and boosting data value and innovation. Doing so can enhance your data analysis, reporting, and decision-making capabilities, improve your data security and compliance with regulatory standards, save you time and resources in the integration process, and help you gain more insights from your data.

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Piyush Malik

LinkedIN TopVoice 2023 | Data, AppliedAI, Technology & Strategy | CXO | BOD Advisor | Entrepreneur | Analytics | Cloud | Do click ?? to be notified of my latest posts
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Without data validation & cleaning during the integration stage, the quality of the resulting data set will be questionable and any downstream processes be it operational workflow or analytical decision support related will be susceptible to misleading/incorrect interpretation. Even AI enabled systems (e.g. classification, recommendation or prediction engines) demand good quality source data and model accuracy is directly proportional to incoming/training dataset. The best way to handle is to validate and correct data at source via business rules otherwise at integration time, the cost of remediation rises exponentially.

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5 How to implement a data validation and cleansing strategy during integration?

To implement a data validation and cleansing strategy during integration, you need to define your objectives, scope, and criteria based on business needs, data standards, and data quality dimensions. Additionally, you should choose the appropriate methods, tools, and techniques according to your data sources, formats, and volumes. It is important to perform data validation and cleansing at the optimal stages of the integration process. Furthermore, you should document and communicate your results, issues, and actions in order to ensure transparency, accountability, and collaboration among your data stakeholders. Lastly, you should monitor and evaluate your performance, impact, and improvement to ensure continuous data quality and value.

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Ashu Dhall, CDMP?

Data & AI Strategist | Practitioner | President DAMA San Francisco (Bay Area)
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In one of my projects involving CRM (e.g., Salesforce) and SAP ERP integration for order management, we followed following strategy -Understanding integration requirements, including the handling of sales orders, customer data, and product -Data profiling, which unveiled issues such as inconsistent product codes, missing data, and duplicates. -Defining validation rules to ensure format consistency, complete customer data, and unique order numbers. -Implementing automation through scripts, which enhanced efficiency and reduced manual work. -Employing robust error handling and logging to capture issues, thereby triggering alerts for manual review in case of validation failure. -Maintaining data quality through continuous monitoring.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Raghu Mohan

Customer-Centric Problem Solver | Automotive, Digital Manufacturing & Technology Strategist| AI Advisor| Digital Transformation (Data, IoT, Cybersecurity, Cloud| Program Management|
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Outcome of Data governance, policies and procedures is data quality, leading to down stream operational and business efficiencies.

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What is the best way to validate and cleanse data during integration?

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1 Why validate and cleanse data during integration?

2 How to validate data during integration?

3 How to cleanse data during integration?

4 What are the benefits of validating and cleansing data during integration?

5 How to implement a data validation and cleansing strategy during integration?

6 Here’s what else to consider

Data Governance

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What is the best way to validate and cleanse data during integration?

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5

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1 Why validate and cleanse data during integration?

2 How to validate data during integration?

3 How to cleanse data during integration?

4 What are the benefits of validating and cleansing data during integration?

5 How to implement a data validation and cleansing strategy during integration?

6 Here’s what else to consider

Data Governance

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