How do you manage complex data?

For example: In a healthcare setting focused on improving Type 2 Diabetes outcomes, objectives may include identifying factors affecting outcomes, predicting high-risk patients, and evaluating treatments. Relevant data is collected from health records, patient surveys, wearables, and public datasets. This data is cleaned, standardized, and preprocessed to create a suitable dataset. An exploratory data analysis is conducted to understand the data's characteristics. Based on these insights, appropriate statistical or machine learning models are chosen to meet the set objectives. The results are interpreted and communicated to stakeholders, with the process iteratively fine-tuned.

Integrate data from various sources. Use Extract, Transform, Load to clean, transform, and load the data in the repository. Identify the data storage solutions based on the volume and type of data. Set up the pre-processing pipeline to handle missing values, outliers, and inconsistencies. Normalize data to ensure consistency. Present your findings in a clear and understandable manner. Use visualizations, charts, and narratives to communicate complex information. Opt for cloud-based solutions for scalability and flexibility. Maintain historical records by implementing version control. Set up regular backup process to prevent data loss. Document data processing workflow for easy knowledge sharing within the team.

Something I learnt in consulting which is my go to hack for defining the objectives sharper is having dummy output or slides. You almost visualize the kind of insights you would need. It's not the actual insight but the level of depth or category of insight that should come out of the analysis. This assumes you have done some data exploration already.

In my field of profession, sometimes dividing the data or problem into little pieces is an effective method to not get lost. Trying to illustrate a whole picture of the project and then go to the details is another approach which is helpful in mega projects.

Having an objective and plan of what you want to achieve from is very important. Once you have a the objective and your plan to achieve the objective you need to review the data to see if the data set is useful data, if the can and needs to massaged to meet your needs. If the data is useful format to your needs. If you find data is also needed from other sources, gather that data and combine the data if needed. Many times data is needed from multiple sources and formatted to met the users need.

For example, in a hospital setting, patient data from electronic health records could be used for a study on disease patterns. However, it may contain incorrect entries, duplicates, or missing values. You'd need to format dates uniformly, standardize measurement units, and rectify inconsistencies in data entry. For missing values, appropriate imputation methods should be used. Duplicates should be identified and removed. Erroneous entries, such as a patient's weight recorded as 5000 kg, must be corrected or removed. Tools like Python's pandas library, or software like Excel, can automate these tasks.

Data management in the health industry is really very vital as it may lead to great consequences as it relates to human life and well-being. I see this step as the most delicate and backbone of the data life-cycle.

Processing data and making it clean from raw, its very important aspect of data analytics. And SQL can be very useful tool to handle the large data set for this purpose.

Məlumatları təmizləmək və onları modelləşdirməyə hazır vəziyyətə gətirmək, analizdə ən çox zaman alan və diqqət tələb hissədir. Çünki analizin bundan sonrakı mərhələlərinin nə qədər uğurlu olması, bu hissədən asılıdır. Məlumatların çirkli hissələrinin təmizlənməsi, duplikat məlumatların aradan qaldırılıb sadəcə unik məlumatların saxlanılması, yarımçıq məlumatların tamlaşdırılması, modelləşdirmə üçün məlumatların lazımi struktura uyğun yığılması, bu hissənin əsas parçasıdır.

Cleaning data is vital in so many cases. The integrity of a research study that could be used as precedent for changes to law, policy, and human lives can all be changed by a single mistake.

Suppose we aim to predict readmission rates of diabetes patients. This involves complex healthcare data, requiring tools like Python for data preprocessing and cleaning, libraries like pandas for handling data frames, and sklearn for machine learning. We may start with descriptive statistics and data visualization (using seaborn/matplotlib) for exploratory data analysis to understand the data. Inferential statistics might help identify significant factors influencing readmissions. Predictive analytics or machine learning methods like logistic regression, random forests, or neural networks could then be applied to build a predictive model. The choice depends on data, skills, and objectives.

In my experience, data story telling is often dry. Pick out the most important conclusive facts while calling out the boundary conditions of the study. As you summarize your key fundings, it’s vital to be assertive and not nebulous in your conclusion.

For instance, you've analyzed a healthcare dataset and found that obesity and smoking are significant risk factors for heart disease. To communicate this, create simple bar charts showing the higher prevalence of heart disease among obese and smoking individuals. Explain in plain language that being obese or a smoker increases heart disease risk. Highlight that while the data shows strong associations, it doesn't guarantee every obese or smoker will develop heart disease, acknowledging the limitations and uncertainties. If there's alternative interpretation - e.g., the role of genetic factors - mention this. Tailor your delivery to your audience, ensuring clarity and relevance.

Getting the results from the analysis is one task and Presenting the outcomes is entirely a new task. In my opinion presentation of analysis results can alter the perspective of the audience/delegates/clients. These conclusions and findings will decide the next steps to the project so, we need to be more cautious about how we are going to show them to the stakeholders.

If you've created a model predicting hospital readmissions in diabetes patients. As new patient data comes in, this model should be updated to reflect current trends. If the hospital implements a new treatment regimen, your analysis may need to account for this change. Feedback from clinicians could suggest the inclusion of additional variables like patient diet. Your data sources may also expand, for example, incorporating wearable device data. Regularly revising and updating your model, incorporating feedback, and adapting to changes in the healthcare environment ensures your analysis remains accurate, relevant, and actionable.

Based on my expertise, I strongly agree as in the energy sector, almost all the data are trends; in this case, an online monitoring is needed, especially when you feel meaningful changes. Sometimes, monitoring is not possible, so a simulator should be used to predict the fluctuation of data over time or under certain conditions. Meanwhile, as time passes, the real result should be matched with predicted ones, here is where we have the best feedback to regulate suggestions, methods, conditions, etc.

Data is not simply a monolith of cold facts. Data has consequences and implications regarding the trajectory of the future for human lives.

Before starting an analysis that may require large amounts of data it is essential to know if the juice is worth the squeeze. Test the hypotheses thoroughly with stakeholders before lifting a finger to run models against large datasets.

As a scientist, be as objective as possible. Make a deep effort in order to remove personal biases prior to analyzing complex data.