What are the best strategies for cleaning data with domain-specific knowledge?

It’s also important to know who “owns” or leads the process(es) that generate the data. Work with this person or team to develop and implement data standards so that data cleaning does not always produce a Sisyphean task. The best way to help them see the issues is to present back detailed and summary data that highlights the issues and allows the business owner to see the results of data cleaning in real time (or near real time). This also works best when it’s easy for the data stewards to get from the report of data quality to the system where the data cleaning need to take place.

I fill Automation is the key , where we can save time and lesser the human errors Domain knowledge expert is also the key Identify the steps needs to taken in Data cleaning. Steps may be as follows 1. Duplicate Data Removal 2. Removal of Irrelevant data 3. Working on Missing data 4. Validation

Spending upfront time (around 30-40% of the timeline) to really grasp the project's source, purpose, and domain knowledge is an awesome investment. This strong foundation will make the rest of the project a breeze . Let's leverage Power Query's column quality, distribution, and profile features to identify outliers and missing values – especially for those key columns ?. Keeping the layer order with dim tables on top and fact tables on the bottom floors is a fantastic practice for clear cross-filtering (think of it as data flowing like a beautiful waterfall). Box plot charts and Quick Insights are your data-discovering best friends! Use them wisely to uncover hidden gems and make insightful conclusions for your report.

Cleaning data with domain-specific knowledge involves leveraging expertise in the field to identify and address data anomalies effectively. Collaborating with domain experts helps understand data intricacies and common errors, enabling the creation of tailored validation rules and cleaning procedures. Utilizing external data sources relevant to the domain can validate and enrich existing data, improving its quality. Customizing data cleaning techniques ensures alignment with domain standards and regulations, such as healthcare privacy laws. Documenting domain-specific decisions and rationale facilitates transparency and knowledge sharing among team members, enhancing the data cleaning process.

Specialized expertise is essential for tasks like data cleaning and determining the significance of highly dependent columns. This level of proficiency often develops through practical experience or consultation with domain experts.

Additionally, defining data quality criteria and metrics are key components of Data Governance. It will define how available, usable the data is as well checking the integrity and security of the data employed in the organization.

Defining data quality criteria and metrics is essential for effective data cleaning. Consider aspects like accuracy, validity, consistency, completeness, timeliness, and uniqueness. Tailor these criteria to the specific domain and analysis goals to ensure high-quality data for reliable insights.

In addition to the great advice other contributors have added. Having a subject matter expert involved is critical. Including a SME in the process leads to more accurate results in less time because they can identify inconsistencies and anomalies more quickly.

Just as a tailor measures twice and cuts once, define your data quality criteria before diving in. Establish metrics like accuracy, completeness, and consistency to evaluate the reliability of your data. Consider factors like missing values, duplicates, and outliers that may affect analysis. By setting clear standards, you ensure the integrity and trustworthiness of your insights.

Defining criteria for data quality—accuracy, validity, consistency, completeness, timeliness, uniqueness—varies by domain. These criteria ensure data meets the specific requirements of your analysis. Tailoring these definitions to the domain improves data cleaning's relevance and effectiveness.

Exploring and visualizing data aids in identifying data quality issues effectively. Through descriptive statistics and visualizations like charts and graphs, anomalies, patterns, and trends become apparent. For instance, histograms reveal data distribution, while scatter plots unveil relationships between variables. Leveraging domain-specific knowledge helps in interpreting findings and ensuring data coherence and plausibility.

When cleaning data with domain-specific knowledge, start by understanding the unique characteristics and intricacies of the domain. Utilize domain experts to identify anomalies, outliers, and inconsistencies. Employ specific techniques tailored to the domain, such as handling missing values, addressing data inaccuracies, and normalizing formats. Explore the data through descriptive statistics, histograms, and box plots to uncover patterns and outliers. Visualize relationships using scatter plots, heatmaps, and correlation matrices. Iteratively refine cleaning strategies based on insights gained from both domain knowledge and exploratory data analysis to ensure data accuracy and reliability.

Descriptive statistics and visualizations uncover issues and patterns. Tools like histograms, box plots, and scatter plots reveal data distribution and outliers. Domain knowledge aids in interpreting these visualizations, ensuring plausibility and coherency. This step is iterative, with continuous exploration as data is cleaned.

When dealing with a new data set start with checking the descriptive statistics. It gives you a great sense of the type of data and sometimes already reveals problematic data. Getting an overview of the unique values within each column is another great first exploration to understand the data. After you know exactly how your data is built use visualizations to see outliers and check if any feature values should be normalized.

In the process of cleansing data with domain-specific insight, begin by comprehending the distinct traits and complexities inherent to the domain. Collaborate with subject matter experts to pinpoint irregularities, anomalies, and disparities within the data. Employ customized methodologies tailored to the domain's requirements, such as managing missing values, rectifying inaccuracies, and standardizing formats. Delve into the data through descriptive analytics tools like histograms and box plots to reveal underlying trends and aberrations. Utilize visualization techniques such as scatter plots, heatmaps, and correlation matrices to elucidate interrelationships.

When it comes to data cleaning, although there are standard methods to replace, impute or standardize the data, certain actions should be vetted by SME's as a go/no-go solution. For example, certain blanks in data are to be treated as 0 and kept for the analysis/modelling objectives. They cannot be removed. When imputing or distributing data over geo-locations or time periods, certain places/dates need to be ignored or given extra credit for. These can be identified by analyzing macro economic factors that impact the domain. Basic idea is the cleaning/transformation techniques adopted should not introduce unwanted errors/bias or take away significant insights from data.

Domain / industry knowledge gives more perspective with regards to handling missing values. If we have the required domain knowledge, we'd know if it'll be better for us to replace a missing value with a 0 or use a specific average in its place. Domain knowledge also helps identify anomalies more easily and helps validate data inconsistencies that may have occurred due to data entry issues, manual errors, incorrect computations etc.

Develop a custom cleaning rules tailored to the specific characteristics and requirements of the domain. These rules can address common data quality issues, such as missing values, outliers, inconsistencies, and inaccuracies.

Many may clean the data but in the process can change the pattern of data. So, Data cleaning should be discreet and methodical.

Data cleaning is an iterative process. Especially in most common issues, like missing values and standardization, choose the proper technique is not a one-size-fit-all approach, but should be refined and re-evaluated regularly

In practice, business analysts are aligned with domain knowledge or in other words have specific knowledge tied to a data source. On data cleaning strategies the business analyst would know what are the key fields relevant for the business as well as how do they impact current reporting and future AI/ML models. Once those fields are identified work backwards to understand how to correct them at the source by working closely with the development team responsible for deploying that particular system and put in place proper QA process for checking this master data sets. In parallel put in place data transformation tech. where data is indeed collected, use inference/ ML techniques such as linear regression for numerical fields with NULL values.

As per me: Write down and explain how you cleaned the data, using simple words that everyone can understand. This helps everyone on the team know what you did and why. Keeping a record also makes it easier to look back on later if needed.

In my experience, documenting the data cleaning process, including the rationale behind cleaning decisions and any domain-specific considerations promotes transparency and reproducibility while facilitating collaboration with domain experts.

When you clean or transform data with a team, you should document not only the steps you take, but also the reasons behind them. Sometimes, you may use common methods like filling or filtering missing values, but other times, you may need domain knowledge. This documentation helps with smooth transitions and alerts your teammates about the columns that require domain expertise. It also encourages collaboration with the domain experts.

When I transform the data, I take time to record what I am doing, so that next time I use the data, I am sure to understand what I did before and why. With python coming everywhere, I tend to create the code for the data transformation in python. This way it is reproducible and relatively easy to understand, even if the comments are poor. If I work in Excel, I make an effort to create a 'Version' sheet where I explain in plain text and in as many details as possible what I did and why. It is very time consuming and boring in the beginning but it saves a lot of time later. I insist on recording your own steps in plain text and not just keeping the formulas that can be too complex for quick understanding later or by someone else than you.

Involve the stakeholder or end consumer in the business. I cannot count the number of times a business process owner makes a request, and the IT team goes away to build the request without involving the business stakeholders for iterative discussions. Then, when they come back, both parties are upset because both teams missed the mark. Business requirements may change, and their vision of what is being built doesn't match the development team's vision. Build a v1 and make adjustments. The first version doesn't need to be perfect. The numbers don't need to be an exact match, and the process to get something built doesn't need to be optimized on day one. Don't let perfection get in the way of progress. Artificial pressure wastes time.

In addition to above, domain data requires applying ‘business context’ using industry ontology and taxonomy to catalog and build enterprise-wide master/reference data assets.

Financial institutions are subject to stringent regulatory requirements concerning data handling, privacy, and security. Data cleaning processes must comply with regulations such as GDPR, CCPA, and others that govern data privacy and protection. Ensuring compliance while cleaning data, especially when dealing with personally identifiable information (PII), adds an additional layer of complexity and requires careful planning and execution.

Maintain good #metadata (data about data) to help with prioritizing cleansing efforts and managing how data is used, who owns it, what its relevance and value is relative to the effort needed to maintain it, etc. Effective metadata stewardship also helps with data governance rule enforcement and tracking when and why certain data should be excluded from consideration in decision support systems.

a. Collaborate with Experts: b. Continuous Learning: c. Data Privacy: d. Temporal Aspects: e. Data Governance: f. Handling Domain-Specific Outliers: g. Custom Validation Rules: h. Feedback Loops: