Here's how you can manage incomplete or messy data effectively.

For many projects, it is very helpful to identify issues using knowledge of the business. The data flow from the source systems to the targets is only the beginning. We must establish daily automated controls with alerts and check and check again and again. Thus, all will be fine, maybe. The knowledge of the business will help us sometimes to detect issues that the data quality tools or the established check processes cannot. Of course, this knowledge is difficult to obtain but is a very strong ally.

The first step towards any data correction/cleansing is to identify the issue. This can be easily done if someone has a strong domain knowledge along with the business context. The right context enables you to identify what the right data should be , thereafter it is just about comparing the data and identifying the outliers. Again this needs to be followed up by understanding the root cause which will help is identifying the best strategy to deal with it. Everything should be not left to data cleansing process.

Identifying the issue can be overwhelming, especially if you have numerous data problems and large amounts of records (sifting through 100 million rows can be daunting!). My recommendation is to avoid the cliche of boiling the ocean. Yes, frequency distributions and stats are good, but chances are there are a few fields with the biggest impact to bad data. Focus on those with an impact to downstream analysis and start there. I'll give an example: date of birth. DOB cannot occur in the future, and storing a DOB 300 years in the past is unlikely. These DOB errors are probably due to data entry errors or bad interface checks and can easily be captured. Finding these can provide the biggest bang for your buck and a win for idenfying.

I will give example from Power BI: When dealing with incomplete or messy data in Power BI, setting up validation rules is important for maintaining data quality. I suggest creating summary tables or cards to highlight anomalies such as counts of nulls or unexpected values. These visual cues allow to quickly spot and address issues before they impact reports. This proactive approach ensures that data quality issues are identified and resolved early, keeping the Power BI visuals accurate and reliable. Also, try using the power of Power Query as much as possible for cleaning up the data before you load it to model

It’s important to note: 1. When it comes to data identifying problems is not an approach linear. This is because as you are understanding the data the context of it changes and thus the problems may have to be redefined. And thus identifying problems and cleaning are reiterative. 2. Already alluded to above, understanding context is crucial to the endeavour. Garbage in, garbage out. If you are not optimising your understanding of the context from the first step. The results from processing data may not be useful.

Data Cleaning: Taming the Mess Diagnosed the data issues? Now clean it! Fix errors, fill in missing values, and eliminate duplicates using techniques like imputation and algorithms. Document your cleaning journey to maintain consistency and enable future audits. Think of data cleaning as organizing a cluttered room. It transforms messy data into a well-structured one, ready to fuel your analysis.

Many times when we use SQL joins we may get duplicate records which should be removed based on a sound logic. Empty values should be logically filled. Deletion of data should be done very carefully while cleaning the data as it may impact the overall analysis.

Data cleaning can be done either by removing rows with missing values or imputing values with appropriate strategies (mean, median or mode). In machine learning, during data pre-processing, sklearn provides some handy libraries for the same, like sklearn.impute (strategy=mean). In Power Bi, Power Query section, similar thinga can be adopted

To manage incomplete or messy data during cleaning: 1. Inspect Data: Start by inspecting your data to understand its structure and content. Use functions like `head()`, `info()`, and `describe()`. 2. Identify Missing Data: Check for missing values in your dataset using methods such as `isnull().sum()`. 3. Handle Missing Data: Remove missing values using `dropna()` or fill them with appropriate values like the mean using `fillna()`. 4. Handle Duplicates: Identify and remove duplicate records with `drop_duplicates()`. 5. Handle Outliers: Detect and handle outliers, possibly using z-scores to filter them out. 6. Standardize/Normalize Data: Standardize data using tools like `StandardScaler` and normalize data with `MinMaxScaler`.

Most people think of cleaning data as just going through and fixing the old data. That is a crucial part of it, however the more important piece is cleaning your future data and that comes from communication. In my experience the vast majority of bad data comes from manual inputs. So just writing a script won’t solve it, but building a relationship with the business partners inputting the data can. Sell them on why more careful data inputs will benefit them and you’ll see much higher long term improvements.

For seamless data analysis, get your data singing in harmony: Uniform Formats: Convert data to a common language (e.g., consistent date format). Bridging the Gap: Align data from various sources for smooth integration. Consistent Labeling: Ensure all entries follow the same structure for clarity. Imagine your data as people from different countries. Standardization is like teaching them a common language. This allows them to communicate effectively, just like your data sets will work together seamlessly for analysis. By ensuring consistency, you avoid confusion and errors, leading to more reliable and insightful results.

You need a consistent method to take data from raw to enriched. Consider moving data through several consistent steps to minimize mistakes, squash bugs faster and reduce duplicative work. For example, move your data through these steps: * Raw data: unchanged, as-is from source system * Prepped data: light data cleanup including fixing duplicate records and correcting data types * Conformed data: add in business logic. This is a crucial layer. Only bring in columns you need. * Core data: your final, cleaned data models for the team. Joins together multiple confirmed models into one (eg marrying account data with product usage data) * Output data: data models used specifically for specific end use case, such as powering a dashboard.

Think further than just usual conventions of data formats - adopt standard naming conventions for data elements. If you are standing up HCM, you'll want to use standard names for items such as "EMP_ID" or "EE_ID" but choose what it will be. Don't try to match PRNR to EMP_ID to EE_ID beyond the source systems. Standard conventions for dates, number formats, and addresses are great but map once and go.

Data standardization is a critical aspect of data preprocessing in analytics, machine learning, and various applications requiring data-driven insights. It refers to the process of converting data into a uniform format, ensuring its consistency across different datasets or variables. Multiple ways to achieve it: 1. Scale adjustment 2. Categorical consistency 3. Date formatting 4. Null value handling Etc.

Data standardization is essential for consistent and efficient data analysis. It involves converting data to a unified format, aligning disparate sources, and ensuring entries follow a common pattern. Dates should be formatted uniformly, and categorical data consistently labeled. This reduces confusion and errors when analyzing across datasets. Key steps include: Format conversion - Transform data from various sources into a standard format, like YYYY-MM-DD for dates. Data alignment - Match, merge, and deduplicate data from different sources to ensure accuracy. Formatting - Adjust data to the required format for analysis, such as standardizing numerical data. Data dictionary - Document data definitions, sources, and structure.

While data validation is crucial for ensuring data accuracy and suitability, it's important to complement rule-based validations with outlier detection techniques. Rules like Regex for email addresses and numeric range constraints are essential but may miss outliers or anomalous data points. Incorporating statistical methods or machine learning algorithms can help identify and handle such outliers, enhancing data integrity. Regularly updating validation rules based on evolving data patterns also ensures ongoing relevance and reliability.

A valida??o de dados é de suma importancia para valida??o de estratégias de negócios atuais, vejo muitas postagem falando sobre jornada do cliente, por exemplo, somente com um bom processo de valida??o poderá mitigar falsos positivos em temas como esse. Sem falar em iniciativas de monetiza??o de dado ou integra??es com sistemas/ferramentas satélites.

This is where you should rely on your business SME’s such as Data Stewards. They should know the data better than anyone and can validate its intended use

Data validation acts as your data's security guard: Keeps Data Accurate: Sets rules to prevent errors and inconsistencies during data entry. Validation Rules Enforced: Like a watchful eye, it checks for mismatched formats or out-of-range values (e.g., negative quantities for inventory). Regex to the Rescue: Uses regular expressions to validate specific patterns like email addresses. Data validation is like a gatekeeper, ensuring only accurate and relevant data enters your system. This helps maintain clean data over time, leading to more reliable analysis and better decision-making.

Data validation is a crucial step in ensuring the quality and accuracy of data in large enterprises, especially for complex datasets. Without it, errors can go undetected and lead to problems like data loss or corruption. Here are some ways to validate data in enterprises: 1. Perform data profiling 2. Check for duplicates and errors 3. Document inconsistencies 4. Follow best practices 5. Use automated or manual tests

Data Management on Autopilot: Automation to the Rescue Free yourself from data drudgery with automation: Repetitive Tasks on Cruise Control: Automate data cleaning, validation, and other routine processes. Focus on What Matters: More time for complex analysis and strategic thinking. Reduced Errors, Increased Efficiency: Automation minimizes human error for consistent data quality. Imagine data management as a long, winding road. Automation puts your data on autopilot, allowing you to focus on the scenic route of data analysis and strategic decision-making. By automating repetitive tasks, you ensure consistent data quality while freeing up valuable time to delve deeper into insights and drive business success.

I often use Power query to automate the cleaning process for periodic reports. Quick examples: Rectifying inaccuracies, filling in missing values, removing duplicates, standardizing data & handling outliers. When automating, aim for a system that's: Reliable: Like a coffee maker that always makes your coffee the same way. Scalable: Like a conveyor belt that can handle more packages during the holiday seasons. Easy to maintain: Like a self-cleaning oven that doesn’t need much attention. In essence, good automation should make your data processes as smooth and dependable as using your favourite household gadgets.

- Build robust ETL pipelines that include steps for data cleaning, transformation, and validation. Tools like Azure Data Factory can help automate and schedule these processes. - Integrate data quality checks into your pipelines to automatically detect and handle incomplete or messy data.

O uso de ferramentas de inteligência artificial/Gen AI podem ajudar no tratamento das informa??es permitindo que o acesso dos usuários a determinadas informa??es seja mais eficiente e com maior integridade!as ferramentas de AI permitem a busca automatica de informa??es em bases definidas como cadastro de clientes, fornecedores, incluindo informa??es dos proprios profissionais. Este tipo de integra??o evita o reprocessamento e possiveis erros e/ou vazamento de informa??es.

Automating the processes in the organization is a very important decision, especially when there are many manual process that uses lots of resources and humman efforts to achieve same tasks again and again.. There are ways to automate data processes in enterprises: 1. Workflow automation: streamline business processes by automating repetitive tasks, allowing employees to focus on other work and increase productivity. 2. Robotic process automation: Uses software and applications to automate business processes, including repetitive tasks like data collection, analysis, and customer service. 3. Embedding the workflow: In many scenerios organizations even embed the workflow prcess in the transactional system to ease the usage.

O uso de ferramentas de visualiza??o (dashboards) como PowerBI, Tableau, Qlik permitem o monitoramento mais adequado e continuo das informa??es. O uso destas ferramentas por parte dos usuários permite uma maior qualidade na tomada de decis?o e a flexibilidade quanto a forma de preparar e reportar informa??es. Estas ferramentas sao muito uteis para o monitoramento e controle de atividades recorrentes e permitem a identifica??o de eventuais n?o conformidades e/ou erros nos processos e transa??es realizadas!

- Implement continuous monitoring of data quality using dashboards and alerts. Tools like Datadog or custom monitoring scripts can be used. - Set up alerts for deviations from expected data patterns or quality thresholds.

Monitor with preventive tools such as Monte Carlo Data. Or open source tools more in a reactive way with dbt or great_expectations.

To enable continuous data quality monitoring, consider the following products and tools: Automated validation: Statistical analysis and data validation using Minitab or SAS ensures data accuracy. Real-time monitoring: Netdata and Prometheus capture and analyze data in real time, providing instant alerts. Data Profiling: LabVIEW and MATLAB support data profiling to identify anomalies and patterns. Metadata Management: Apache Atlas and Collibra Data Catalog for storing and managing metadata. Regular Auditing: Tableau Data Quality and IBM InfoSphere Data Quality support data profiling and auditing. Centralized Monitoring: Grafana and Kibana build centralized dashboards. User Feedback Loops: Business Power BI and Tableau.

Consistently monitoring data quality is crucial. It's important to have systems in place that alert us to any emerging issues promptly, allowing us to address them before they impact our analysis. Conducting audits is also beneficial as they help identify any overlooked errors, ensuring that our data stays accurate and dependable for decision-making. Being proactive and vigilant is key to maintaining the integrity of our data.

Try to establish a "data analytics by design" methodology. This requires data leaders to create frameworks for educating business & product leaders and software developers on why it is important to think about data analytics at the point of product requirement and detailed design versus having data be an after thought. If product and software developers build software with appropriate data tags, high quality data is generated when the product ships which translates into high quality, reliable insight on the back end.

Unless you understand your business you can collect all the data in the world and have zero impact. This may sound trite and obvious but I have seen plenty of successful companies who simply do not understand WHY they are successful and therefore hoard meaningless data turned into metrics that while they may show positive impact of some activity or other in fact may be lying straight to your face. What is it your customers are expecting you to solve for them, what is your purpose? Unless you are collecting data about how well you solve their problem and fulfil your purpose. Well, best of luck.

In terms of data piplines , it is worth implementing data quality alerts.They can be based on some basic ideas , for example counting daily amount of rows beeing inserted in your datamarts, checking if these amounts correspond to cofidence intervals. That solution can be implemented with the help of various etl-orchstartors such as airflow,prefect ... Also it is worth maintainig proper documentations , for example "source to target" docs, which has clear explantions types and transformations applied to data fields. In terms of ML algorithms , you can either fill NA values with the most frequent value in a field, either with max/mean/median value or just drop this line ( the last option can lead to sufficient data losses)

just get a good data tool such as Monte Carlo Data so it can be proactive in detecting the issues, instead of reactive, its impossible to predict everything, and more complicated when the distribution of the data changes over time, Monte Carlo Data can alert on that. After that just implement corrections in the process with the business to process partially the data, flag the data, stop from processing because that data is business critical. And then keep bugging the operational teams to correct it hehehe

“Messy” data is a result of disagreement of multiple stakeholders on how to use the data, disparate sources of data and undocumented use cases. Before mechanically “fixing” data we should get collaborative agreement on how the data should be used. Precisely, what questions are we trying to answer. From there, it becomes more clear on how to enforce data integrity.