How can you overcome common challenges in data analysis projects?

In addition to the already mentioned points, data source verification might be useful in some cases. Analysts can check the data collection methods, and also cross-referencing the data with other trusted sources if possible. Furthermore, analysts might want to interact with the teams or individuals that are responsible for data warehousing to understand how the problems arises and collaborate together to get them resolved.

Adding more to the pointers mentioned above, These are some techniques I use to kickstart with “Data analysis” quality checks Step1, Talking to SME’s before performing quality checks is an important step to understand how users are logging the data( data can come from multiple platforms). Step2, Talking to Data Engineers on the sources of data pull. These 2 important steps will guide the next steps of quality and validations. These will also help you figure out any “Missing Data Source”. Talking to SME’s and Engineers will give a good picture on entirety of product you’re working on and helps pull the strings together for better insights.

Being transparent about how the data is collected helps assess the quality of dataset. The process involved in obtaining data helps with data cleaning decisions. Equally collaboration in structuring datasets across all those who will use the database is useful to prevent multicollinearity.

Ensure data quality and completeness through cleansing and validation. Integrate data from various sources using data warehouses and ETL processes. Allocate sufficient resources and consider outsourcing for specialized expertise. Prioritize data security and privacy. Define project scope and manage changes to avoid delays. Choose suitable analysis tools and focus on relevant metrics. Handle big data with distributed computing and cloud solutions. Select appropriate models and validate their accuracy. Foster collaboration and update stakeholders regularly. Address change management to gain buy-in from all parties. Manage time effectively and monitor progress closely. Encourage continuous learning and stay updated on data analysis techniques.

If there is a problem and an immediate need, validation and cleaning are good steps. However, companies should consider a proactive approach to data quality. What is the most important data? Who owns it? How is it captured and controlled? Putting together a data quality strategy can help prepare good quality data from the get go.

Every minute that you spend properly typing your data upstream is worth multiple hours of saved efforts when analyzing and visualizing your data downstream. Use properly-outlined categories instead of strings whenever possible. Distinguish extensive (summable) variables from intensive (non-summable) ones if you can. And learn how to use frequency-of-frequency charts to spot errors in your data.

In my experience, dates are always a problem. It should be simple math, but the formats are different and change depending on the tool. I haven’t figured out the solution to fix them - I hope someone can share best practices.

In my experience an investmenting time in data modeling to have different source with a common key , that will let they be compared each other, is a very time invest but very need.

Using data visualizations is a critical part of this task for technical and nontechnical stakeholders alike. The subtle difference between the two is a matter of knowing the audience. More technical users want a dashboard, they want to be able to extract the data that they need and run with it. Non-technical users want meaning, they want you to connect this to where they see the most value. These are iterative processes and we have to risk getting it wrong. Getting it wrong can be just as useful because it can shape a final product and a format that suits people’s needs. It can be deflating to make a stunning data story and be told that you missed the point entirely. At the same time, it helps develop our ability to think on our feet.

Conveying your points to co-workers successfully is all about knowing your audience. What specific understanding, access and detail is important to the people who use the information. Many may just need a simple, broad overview while others require fulsome understanding and possibly dashboard like access. When in doubt, ask departments to provide what level is required for their staff and what is the best way to convey the information. This will show your commitment to teamwork and sincere desire communicate effectively. I love to use short pre and post testing to gauge success and an after training, anonymous questionaire will provide the information you need to improve!

I recommend doing a dry run with a subject matter expert. Make sure they understand the narrative and agree. When they do, bring them to meeting to help tell the story. The audience will naturally have more confidence in the result. This is especially necessary if the result opposes the assumed result.

Data comunication is need during all the data analysis, i have data from different department of a company, and i have not the technicla knowledge, so i need an strong comunication to better understand of each souce of data. Then, in my case, i preffer to comunicate in a familiary lenguages the result of the analysis to better understanding and to achieve different point of view from other expert. I considerer that one analysis could be feed and feed by ecach interaction and comunication. It is not an alone work, it is a constantly feedback and retrofit analysis.

My mother told me, "Just because you can, doesn't mean you should." Data/AI governance teams should have clear frameworks for what is permissible and what is not. Government regulations help drive these guidelines. GDPR specifies how EU resident data can be used. Recent US state regulations, such as CCPA/CPRA, give California residents the right to opt out of automated decision-making and have their data deleted under certain circumstances. Generally, protected personal attributes cannot be used for decision-making (race, sex, etc.) in many situations. Bias needs to be controlled at both the data intake and evaluated at the output. Internal AI mechanisms may not be easily evaluated; however, risk/bias can be measured through the output.

At least you could give me the opportunity to finish writing/rewriting my perspective on the subject matter at hand, prior to deletion.

For this reason it will be very important the file documentation for a better safe information, and to have all the data reffencial from a font of source, to have in hand the correct font of who give me specific information, to make the data confident with the corresponding support.

Simply put, if you are reading this, YOU have a data problem. Nowadays having "bad data" is akin to acknowledging that humans are imperfect. So take the following to heart: automating or analyzing your process based on bad data just makes bad things happen faster. Use this to your advantage and don't take data cleanup and cleanliness practices lightly, it will pay off later. It always does!