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What are the most effective ways to clean databases?

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Data quality and validation are essential aspects of data management, as they ensure the accuracy, completeness, and consistency of the data stored in databases. However, data can often be corrupted, duplicated, outdated, or incomplete due to various reasons, such as human errors, system failures, or incompatible formats. Therefore, data cleaning is a process of identifying and correcting these issues in the data, using various methods and tools. In this article, we will discuss some of the most effective ways to clean databases, and how they can improve your data analysis and decision making.

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Jonathan Kirschen, MBA

Head Of Information Technology | PM | Bilingual English and Portuguese
Elton Cunha

Developer | Data Analyst | C# .NET | SQL | Python | Azure | AWS
Vinicius Massullo

Data Manager | Data Science | BA | BI | ETL | DAX | AmorSaúde

1 Define your data quality goals

Before you start cleaning your data, you need to have a clear idea of what you want to achieve with your data quality improvement. You should define your data quality goals based on the purpose and context of your data analysis, and the standards and requirements of your industry or domain. For example, you may want to ensure that your data is accurate, consistent, relevant, complete, or timely. You should also specify the criteria and metrics that you will use to measure your data quality, and the acceptable level of errors or deviations.

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Jonathan Kirschen, MBA

Head Of Information Technology | PM | Bilingual English and Portuguese
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The most effective ways to clean databases include regularly conducting data audits to identify and remove duplicate or outdated information, implementing automated processes to regularly clean and update the database, and educating staff on the importance of maintaining clean data. It's also important to establish strict data entry protocols to prevent the introduction of unnecessary or incorrect information. Additionally, utilizing data cleansing tools and software can help streamline the process and ensure data integrity. By consistently implementing these practices, the database will remain organized, accurate, and efficient.

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Brendon Berndt

Executive Manager at Westpac
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A first step in any data quality goal is to look to the data governance policies within your organisation. If your organisation uses data products (and many do these days), there will likely be standards that have been defined with your specific industry needs in mind. Keep in mind the risks of not having good data quality as the mitigations to these risks often go a long way to improving data quality outcomes.

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Vinicius Massullo

Data Manager | Data Science | BA | BI | ETL | DAX | AmorSaúde
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As metas de qualidade de dados s?o essenciais para: Direcionar o processo: Elas estabelecem objetivos claros para a higieniza??o. Avaliar a eficácia: Permitem medir o sucesso da limpeza de dados. Priorizar esfor?os: Focar nas áreas mais críticas. Monitorar continuamente: Identificar desvios e corrigi-los. Facilitar a comunica??o: Todos compreendem o que está sendo buscado. Dados limpos s?o a base para decis?es sólidas e resultados confiáveis.

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Jon Andoni Baranda

Technical Program Manager II @ Microsoft - Azure | AI Cloud/Data Infrastructure Engineering | Driving Technical Projects to Success | AWS, Microsoft and Google Certified
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Cleaning databases effectively involves several key steps. Firstly, defining data quality goals is crucial to guide the cleaning process. This includes establishing clear objectives aligned with data analysis purposes and industry standards. Secondly, identifying and removing duplicates is essential to mitigate confusion and inefficiency. Lastly, validating and standardizing data enhances accuracy and consistency. This step involves checking and correcting formats, types, and values according to predefined rules.

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2 Identify and remove duplicates

One of the most common data quality issues is duplication, which occurs when the same data is recorded more than once in the database, either partially or completely. Duplication can cause confusion, inconsistency, and inefficiency in your data analysis, and waste your storage space and resources. Therefore, you should identify and remove duplicates from your database, using various techniques and tools. For example, you can use SQL queries, Excel functions, or specialized software to find and delete duplicate records, based on certain attributes or keys.

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Elton Cunha

Developer | Data Analyst | C# .NET | SQL | Python | Azure | AWS
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Data duplicates, can be a bad information, when we talk about a database with million entry, duplicate data definitely are dangerous for future insights from business and bad decisions. Depending of de type of duplication, We will use a especific tools, personaly I like to adjuste theses duplication using T-SQL, because I can visualize in real time the effects of my actions. But other tools exists, like Excel, Power BI,

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3 Validate and standardize your data

Another important step in data cleaning is to validate and standardize your data, which means to check and correct the format, type, and value of your data, according to predefined rules and conventions. Validation and standardization can help you avoid errors, inconsistencies, and ambiguities in your data, and make it easier to compare, integrate, and analyze. For example, you can use regular expressions, data validation functions, or data quality software to validate and standardize your data, such as dates, phone numbers, email addresses, or currency.

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Vinicius Massullo

Data Manager | Data Science | BA | BI | ETL | DAX | AmorSaúde
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A higieniza??o de dados é essencial para as empresas, sendo a padroniza??o e organiza??o dos dados um passo essencial para garantir sua confiabilidade e utilidade nas estratégias de negócio. A valida??o e padroniza??o s?o cruciais nesse processo, pois reduzem erros e inconsistências, facilitando a tomada de decis?es estratégicas, minimizando o retrabalho e garantindo o cumprimento das regulamenta??es, como a LGPD. Além disso, dados precisos contribuem para uma melhor experiência dos usuários, destacando a importancia de manter a qualidade dos bancos de dados para o sucesso do negócio.

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4 Handle missing and null values

Missing and null values are another common data quality issue, which occur when some data is not recorded or available in the database. Missing and null values can affect your data analysis, as they may indicate incomplete or unreliable data, or skew your statistical results. Therefore, you should handle missing and null values in your database, using various methods and tools. For example, you can use SQL functions, Excel formulas, or data imputation techniques to handle missing and null values, such as deleting, replacing, or estimating them.

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Brendon Berndt

Executive Manager at Westpac
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Don't just remove them! Nulls can actually tell you a lot more than you think. They may not even be a straight forward data quality issue, depending on the collection method and the purpose of the data. Context is king here, and knowing why a value is null will inform how to handle them, and whether an assigned value is even appropriate.

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Aleksandra Trybalska (she/her)

Sociologist | HRIS Analyst at Trustwave
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Effective database cleaning hinges on adeptly handling missing and null values. These gaps in data can skew analyses and compromise accuracy. Start by identifying missing values through data profiling tools. Choose appropriate strategies: impute with statistical measures, use interpolation techniques, or categorize missing data where appropriate. Transparently document choices made to preserve data context. Automated scripts can expedite this process, ensuring a comprehensive and meticulously curated database. Addressing missing and null values not only fortifies data quality but also fosters a reliable foundation for insightful analyses and informed decision-making.

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5 Check and correct spelling and grammar

Spelling and grammar errors are also frequent data quality issues, especially in text-based data, such as names, addresses, or comments. Spelling and grammar errors can cause confusion, misinterpretation, and miscommunication in your data analysis, and affect your credibility and professionalism. Therefore, you should check and correct spelling and grammar errors in your database, using various tools and resources. For example, you can use spell checkers, grammar checkers, or dictionaries to check and correct spelling and grammar errors, or use data cleansing software to automate the process.

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6 Review and verify your data

The final step in data cleaning is to review and verify your data, which means to inspect and confirm the quality and accuracy of your data, after applying the previous steps. Reviewing and verifying your data can help you ensure that your data cleaning process was effective and successful, and that your data meets your data quality goals and standards. For example, you can use data quality reports, dashboards, or charts to review and verify your data, or use data quality software to monitor and track your data quality over time.

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7 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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What are the most effective ways to clean databases?

1

2

3

4

5

6

7

1 Define your data quality goals

2 Identify and remove duplicates

3 Validate and standardize your data

4 Handle missing and null values

5 Check and correct spelling and grammar

6 Review and verify your data

7 Here’s what else to consider

Data Management

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What are the most effective ways to clean databases?

1

2

3

4

5

6

7

1 Define your data quality goals

2 Identify and remove duplicates

3 Validate and standardize your data

4 Handle missing and null values

5 Check and correct spelling and grammar

6 Review and verify your data

7 Here’s what else to consider

Data Management

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