What data profiling tools are most effective for cleaning data for visualizations?

I work with SAS and Power BI most of the time. But still use Excel for quick check or power point for executive presentations.

The imperative for employing data profiling practices is rooted in the principles of Assurance, Efficiency, and Reliability. Data quality assurance, a meticulous evaluation process, ensures data accuracy. Profound comprehension of data source structures via profiling optimizes integration. Identification and rectification of anomalies establish data trustworthiness, furnishing decision-makers confidence. This converges to assure data quality, enhance efficiency, and bolster data-driven decisions.

Data profiling is an integral part of the Data Analytics journey. Here’s how Data Profiling contributes to the Data Analytics process: 1. Understanding the data quality 2. Assessing the Data Relevance 3. Identifying data patterns and relationships 4. Data Preparation & Transformations 5. Data Exploration 6. Ensuring Data Integrity

It will ensure better quality and more credible data. Data profiling can help make better sense of the relationship between different data sets and sources, and help support data governance procedures. This consist of below points 1. Performing data quality assessment. 2. Identifying data types, recurring patterns, etc. 3. Tagging data with descriptions and keywords. 4. Group data into categories. 5. Identifying the metadata and its accuracy. 6. Performing inter-table analysis. 7. Identifying functional dependencies.

I have always found that I need to see with my own eyes what the data says and whether it says what we think it says. Looking at csv files can reveal all sorts of things that relaying on dashboards would never. Once you understand (with samples) what your data actually says, then you can understand whether your processes are actually working, whether your data says what you think it says, etc. So many times I have discovered that the actually data is much more messy than we thought. It is worth looking as often it seems that nobody has looked and you can discover amazing things.

While making the selection one should look for below point 1. Data Sources and Compatibility. 2. Data Profiling Features. 3. Ease of Use. 4. ETL Support. 5. Data Processing Needs and Scalability. 6. Automation and Scheduling. 7. Support and Reviews.

When it comes to choosing Data Profiling tools in regard to Power BI, I look at the below questions. 1. Is it Cost-effective i.e. Open Source ? 2. Does it provide summary, history and documentation capabilities ? 3. Is it Secured and Confidential ? 4. Does it provide the Automation capabilities ?

If Pandas isn't enough for you, consider the following when choosing: Compatibility: Ensure it works with your data sources, databases, and formats. Scalability: Confirm it can handle your data volume efficiently. Data Visualization: Check for robust visualization capabilities. Data Quality Assessment: Ensure it can detect missing, duplicate, or inconsistent data. Customization: Seek tools that allow you to tailor profiling to your specific needs. Automation: Consider tools with automation options for repetitive tasks. Integration: Verify it integrates well with your existing data workflow and tools. Cost: Evaluate pricing models to fit your budget. Open-source tools are great choice in most cases.

One thing I've found helpful: When choosing a data profiling tool, it's important to consider the following factors: 1. The size and complexity of your data: If you have a large and complex dataset, you'll need a tool that can handle large data volumes and complex data structures. 2. Your budget: Data profiling tools can range in price from free to several thousand dollars. Choose a tool that fits your budget and needs. 3. Your technical skills: Some data profiling tools require more technical skills than others. Choose a tool that is appropriate for your skill level. What I understand is Data profiling tools are for all, regardless of data analysis or programming skills.

There are multiple tools with different level of features helping different type of users 1. SQL Server Integration Services (SSIS) 2. DAX Studio (Power Query) 3. IBM InfoSphere Information Analyzer 4. Talend Open Studio 5. Apache Griffin 6. Informatica Data Quality and Profiling 7. SAP Business Objects Data Services

For Python, the simple usage of panda profiling is a great tool for quick profiling of your data and setting the foundation for deeper analysis.

Here are the sample Data Profiling tools that I prefer based on Cost and User Friendly. 1. Knime 2. DAX Studio 3. Tabular Editor 2. Power Query 3. Apache Nifi

One can automate Exploratory Data Analysis process using pandas-profiling and sweetviz. For enterprise grade data cleaning, TIBCO Clarity is great tool. From database perspective, Oracle Enterprise Data Quality is good. For handling big data, IBM Infosphere provides support for data quality. Dataloader is great tool for Salesforce ecosystem.

I agree with Damian that even a simple use of the pandas profiling python library is a great foundational step. I've also had successes using the skimpy and dataprep libraries. This is the beauty of Python. So many good resources to help you along your journey.

1. Use data profiling at project start to discover if data is suitable for analysis—and make a “go / no go” decision. 2. Identify and correct data quality issues in source data, even before starting to move it into target database. 3. Identify data quality issues that can be corrected by Extract-Transform-Load (ETL), while data is moved from source to target. Data profiling can uncover if additional manual processing is needed. 4. Identify unanticipated business rules, hierarchical structures and foreign key / private key relationships, use them to fine-tune the ETL process.

Its always important to document the data profiling which you find before and after optimizing, this helps the developers to understand the loopholes and History of data issues which can be rectified in future.

If there are notable outliers discovered during the data analysis, I always make a point to say so in the summary report or make mention in presentation. Anyone with data access could make leaps of logic about the data before it's been properly cleaned and if their understanding of the data is flawed, it could lead to surprise and dismay upon examination of the deliverables. Just be upfront about the manner in which you changed the data (omissions, missing values, outliers, etc.) by rigorously documenting your process.

Data profiling tool its important tools to help and enables users to easily assess the quality and reliability of data sets. then it will guide for right decisions to the top managements,

When using a data profiling tool, it's important to start by defining your data quality objectives and criteria. This will help you to identify the specific data quality problems that you need to address. I disagree with the statement that you should plan your data profiling and cleaning workflow in advance. While it is helpful to have a general idea of how you want to profile and clean your data, it is also important to be flexible and adaptable. As you profile your data, you may discover new data quality problems that need to be addressed. During a project ,while profiling the data, we discovered a number of new data quality problems that we had not anticipated. We had to adapt our plan accordingly in order to address these new problems

I would say instead of picking the one tool to learn which comes with license go for a Open source tools such as Power Query, DAX Studio and Knime to get started and the learning materials for these tools is easily available in Udemy and Youtube

To learn about data profiling or tool, I would recommend online courses that are freely available on YouTube, edx, Coursera etc. Its best to have basics clear before moving to next phase.

I have found the following resources to be helpful for learning more about data profiling tools: Online courses and tutorials: There are a number of online courses and tutorials available on data profiling tools. For example, DataCamp offers a course on Data Cleaning in Python or R. Books: There are a number of books available on data profiling tools. For example, Data Quality: The Accuracy Dimension by Jack E. Olson is a good resource for learning about the fundamentals of data quality and data profiling. Blogs and podcasts: There are a number of blogs and podcasts available that cover

In R there are many EDA solutions for you to try. But my favorite is skimr; this package is awesome to understand your data easily. You can literally run an EDA on a tweet.

There are plenty of tools to get started with quick data profiling, but it all leads back to having a solid database structure and prioritizing data governance. With clean pipelines and structure, data profiling can be almost entirely automated. By adhering to industry standards, any analyst should be able to jump into your data and do some initial EDA using tools such as basic Python libraries (Pandas, Matplotlib, Seaborn) without much preface.

You want to consider repeatability and if you need this functionality in your visualization tool. I really like how Power BI wraps this up inside the tool itself for this.

Don't start working on something you don't understand how is works!! Many here recommended power BI - this is a visualization tool and you need to know DAX to use it properly. If you want to analyze you data (detect anomalies, show correlations, discover nulls) then this is not the right tool. If you want to build a dashboard and present you data then yes - it is. Always think what you already know and how you can utilize it to you own benefits in terms of working with the data.