How can you evaluate data from external sources?

Biases in data are subtle yet game-changing. In sustainable energy and healthcare tech, watch for commercial interests that can skew results. Don't rely solely on 'prestigious' sources—assess data on merit. Sustainable energy fields evolve fast; over-relying on old data can misguide. Being in global events highlighted regional biases to me: regional success doesn't equal global truth. In AI, avoid only favoring the newest tech; history has value. True innovation balances introspection with broadened perspectives.

When looking any external data, it is important to understand the purpose with which data is generated. For e.g. data generated for Research and Development for a product development or data compiled by marketing team to understand the trends. The data sets mentioned above will definitely have different structure. However, they can yield common information when structured in appropriate way. Most importantly, how the seeker structures data in hand will be the key.

This is a critical dependency when it comes to evaluating a data source. If the source lacks credibility, all other aspects of the data become irrelevant. A layer when it comes to credibility is understanding potential biases in the data source. Biased or non-objective data can lead to skewed insights and misguided marketing efforts. Assessing the source's objectivity is essential to making sound decisions. A reputable source is more likely to provide accurate and trustworthy data.

In addition, It is critical to not only examine a single data source but to diversify where your data comes from. For example, in the lending business if you are working toward building a scoring system for your customers or simply wanting to get more information, major credit bureaus like Equifax, Experian and TransUnion can be your go to choice. Understand their VantageScore or FICO would offer you plenty insights. Having access to multiple sources can provide a more thorough and comprehensive picture for your analysis.

Knowing your data source is crucial when evaluating data from external sources to ensure data quality, reliability, and integrity. Here are some key considerations: 1. Source Credibility 2. Data Collection Methods 3. Sampling Techniques 4. Data Collection Period 5. Data Format and Structure 6. Data Definitions 7. Data Quality Checks 8. Data Collection Bias 9. Data Privacy and Security 10. Data Documentation 11. Data Ownership and Licensing 12. Data Validation 13. Historical Performance 14. Expert Consultation 15. Redundancy In summary, conducting a thorough assessment of your data source is a critical step in data evaluation.

Checking the data format and structure is a fundamental step when evaluating data from external sources. Here's why it's important and what you should consider: 1. Compatibility 2. Consistency 3. Column Headers 4. Data Types 5. Missing Values 6. Data Encoding 7. Data Size and Sampling 8. Data Hierarchies 9. Delimiters 10. Timestamps and Time Zones 11. Data Units 12. Encoding and Special Characters 13. Data Dictionary 14. Data Transformation Requirements 15. Versioning By thoroughly examining the data format and structure, you'll be better prepared to handle and analyze the data accurately and efficiently. It also helps identify potential challenges and areas where data cleaning or transformation may be necessary.

In my experience, I've found SQL to be an invaluable tool for thoroughly assessing incoming data. Its insistence on adhering to well-defined data types minimizes errors and ensures data consistency. You can for example use regex to enforce specific structures for incoming data like phone numbers or emails, to align with specific patterns. Constraints like NOT NULL and UNIQUE also reinforces data consistency. These checks help ensure data integrity for further analysis.

A crucial step is understanding limitations with the data collection method. Is data collected within the precision of a lower quality but readily accessible instrument? Are you comfortable with the sacrifice of precision for reproducibility? Is there potential for human error in more qualitative datasets?

I like to start exploring a new dataset by running some simple descriptive statistics and also graphing the data when appropriate (sometimes you see things in the distributions, etc. that are obscured in the descriptive statistics). It is also helpful to start thinking about granularity / level of detail early on - if I need to aggregate transactions on multiple dimensions, and the data came to me pre-aggregated, it may not be fit for my purpose if the granularity is too coarse.

I would look at the following : Source Reputation: Consider the source's trustworthiness and accuracy. Also understanding data collection methodology is critical. Multiple Perspectives: Seek information from various sources for a comprehensive understanding. Consistency: Verify if the data aligns with information from other sources. Bias Check: Be aware of potential biases in the data source. Fact-Checking: Verify data, especially if it seems too good to be true.

This is perhaps the most important step in validating data your data sources. Although we all understand why this is important for external sources, let's take a moment to appreciate why is it necessary for internal data sources as well. Within an organization, different teams might be defining metrics differently. As a simple example, suppose the game development team defines a project as "high performing" if a large of people are playing the game, whereas finance team describes it as "high performing" if it earns a lot of revenue. Hence, now we have 2 data sources of high performing games, which will have differences. Always keep a check on the logic and pipeline through which data is built to avoid these errors, and always validate.

Comparing data with other sources is a valuable practice when evaluating data from external sources. Here's why it's important and how to go about it: *Importance of Comparing Data:* 1. Data Validation 2. Error Detection 3. Completeness 4. Bias Assessment *How to Compare Data with Other Sources:* 1. Identify Comparable Variables 2. Collect Data from Multiple Sources 3. Standardize Data 4. Perform Descriptive Analysis 5. Visualize Data 6. Statistical Tests 7. Analyze Discrepancies 8. Seek Expert Input 9. Document Your Findings 10. Use Consensus or Weighted Averages 11. Consider the Context Comparing data from multiple sources is a robust strategy to ensure the reliability and validity of your data.

Comparing 3rd party data with other sources is a critical step. It uncovers insights not apparent from standalone data. This process enables a holistic view by considering various perspectives. It's like assembling pieces of a puzzle, where inconsistencies may signal caution, and alignment provides confidence in analysis.

Consider how adding additional data sources to existing internal data will affect your data model. Can you incorporate this data by joining across common primary keys? Are the data sources at the same granularity? How will it be used? What value does it add? For whom?

It is necessary to compare or extrapolate, don't forget in statistic when two value have a huge deviation from del medias, those values are removed.

Analyzing data with caution is paramount when evaluating data from external sources. Here are some key principles to keep in mind: 1. *Data Source Assessment 2. Data Cleaning 3. Understand Data Limitations 4. Data Transformation 5. Context Matters 6. Comparative Analysis 7. Statistical Validity 8. Visualizations 9. Robustness Checks 10. Peer Review 11. Document Your Process 12. Interpret Results Cautiously 13. Consider Ethical Implications 14. Consult Experts 15. Peer Validation In summary, exercising caution throughout the data analysis process is essential when working with external data sources.

When assessing the data, you may want to consider cultural differences, such as norms, standards and terminoloy. You may also consider consulting subject-matter experts.

In this final step of evaluating external data, thorough scrutiny is crucial. Blind trust can lead to errors, so rigorous testing and validation are vital. Recognizing the limitations of existing data while maintaining awareness of ethical considerations is paramount. Data analysis isn't just a skill; it's a responsibility.

Part of analyzing any data with caution is to consider competing hypotheses, and test the data to understand whether alternative explanations might be relevant to your question. This is even more important when that data comes from an external source. Robustness and sensitivity checking are key approaches that can identify and capture potential issues, as well as highlight special circumstances in the data.

Check the dimensions of the data set in a data frame analysis and verify any empty cells in the grid. Missing data can be estimated with k cluster techniques or removed from the set. Variances of each column shows a measure of the number of outliers or consistency in the data group. Checking the data type of each column indicates whether quantitative or qualitative records have been made.

Don't waste substantial effort validating data from unreliable platforms. The task of cleaning up our internal, reliable data is already a formidable challenge. How confident can we be in our ability to validate, assess, cleanse, and utilize external data for our decision-making processes?

Check for Legal permissions and ethical considerations I.e. whether you are allowed to use the data and have they been received from the right sources. Additionally, cross verify the data with other reputable sources to check for consistency. You could also get expert opinion from the field to validate the quality and reliability of data.

Checking for simple correlations and performing basic statistical functions like mean and standard deviation gives a lot of insightful inferences about the data. We should ensure that the sample size is enough such that it doesn’t cause any bias.

Always ask why and how a data set exists. Does the collection of this data make sense? Is it really a data set or is it the result of a survey? Or is it derived from another data set? First class USPS mail statistics sorted by zip code certainly exists as well sourced data. A derived data set by income could be generated, with attendant errors. But could you generate a data set of first class mail by age of sender? You could survey for that, but probably not generate a data set that illuminates that question from good sources. Always think through how a data set could or would be generated, and be skeptical of data sets which are unlikely to exist or unlikely to be derived cleanly.

Verifying data sources goes beyond the basics; it includes comparing and connecting data with others for a more robust analysis. This approach provides certainty in identifying correlations and differences. To ensure data accuracy, it's crucial to discuss insights with the relevant business units before presenting them, adding value through collaborative validation.