What's your strategy for analyzing complex data?

- Clearly define the business problem your try to solve. What will define of data analysis project will be successful? - Identify the data you need to solve this problem. What are the data sources and how will we obtain the data. - Have domain expertise. You need to have knowledge of the process, industry or organization. Sometimes you also need to have technical expertise to understand the structure of the data in various systems. It's always very powerful to bring domain knowledge to the table yourself but you can as well establish a project team with the required skills and expertise. - Extract and transform data. Your domain knowledge will enable you to assess data quality and correctly model the data. - Analyse and visualize.

Don't be daunted by data! Complexity lives in our interpretation, not the data itself. When stuck, zoom out, simplify, and seek analogies. Every problem has a solution out there, you just need to find the right perspective. Complexity is an illusion. Break down the problem, find relatable examples, and voila! Your unique challenge suddenly becomes just another solved use case in the grand scheme of things.

Complex data are relative to the analyst trying to analyse the data. For me, when I encounter a dataset that initially appears intricate, I begin by immersing myself in a thorough understanding of the dataset, gaining a holistic overview of its contents and structures. I place a strong emphasis on recognizing the intended audience for whom the analysis is intended. This awareness enables me to tailor my analysis to meet their specific needs, ensuring that the insights extracted are not only profound but also relevant. I formulate hypotheses that serve as guiding beacons throughout the analysis process. This helps in directing my efforts toward investigating specific aspects of the data that align with the goals. Next is embarking on EDA

Key points to consider: 1. What business problem are you solving? 2. How the data look like? 3. Why the data in such condition at the first place? 4. Is the data analyze-able? What method and tool can you use? 5. How is the effort like?

When dealing with complex data, I have found success by applying the core concepts of the yellow belt i.e. defining the problem and conducting root cause analysis. It involves- defining the problem and goal, identifying the current process and the consumer, and outlining their needs. This approach can help you analyze complex data from a management perspective and individually reveal the layers of complexity. To define the problem and goal, it's essential to talk to various stakeholders and consider the bigger picture. Now, could you dive deep- and identify the current process? This involves the data discovery phase and a list of critical stakeholders. Finally, enlist the needs of your customer to ensure that you meet their requirements.

Effectively managing complex data mandates systematic exploration and refining. This phase ensures data readiness, scrutinizing missing values, outliers, and inconsistencies, paving the way for robust analysis. Employing tools like descriptive statistics and visualization sheds light on data quality and distribution, enriching insights. Techniques such as imputation, transformation, normalization, and aggregation strengthen data integrity, enabling precise interpretation. This meticulous process aligns with the pursuit of data excellence, preparing the groundwork for meaningful analysis.

One of the first steps to do when you begin analysis is to "sanity check" the data. You dont want to go too far down the road of analysis to find out the data are wrong. I was once given a forecasted FTE (Full Time Equivalent) report for a retail bank's branch network, and it took me all of 3 seconds to know it was wrong. The branch level FTE numbers were in tens of thousands, which is impossible! Sanity checking data often requires having some domain knowledge (or common sense!). If you dont have domain expertise, which is not a good position to be in, then show aggregated numbers to some expert and have them confirm the validity of the numbers.

Before you jump to cleaning with statistics, build a data dictionary. Understand what each term means, and relevance to project. Removing terms only because it is unavailable for 70% of the users, may be disastrous to your project. Use EDA, but only look at relevant columns, so that you are not bombarded with information. Increasing the number of decisions you have to take after EDA (column to keep, values to impute), will eventually drag you down a vicious cycle. Focus on what is really important for the analyses. Sit with a stakeholder, and dial it down for them what have you done to clean the data. They might not be interested in jargons, but you might have imputed mean - where you should have just put 0.

There are times where – even after having produced all the data visualizations, summary statistics, correlation heatmaps, etc. – you might be still scratching your head why the data is the way they are. If you can afford the time to take one step further, it is worth talking to the data engineers, architects, or whoever oversees the overall data pipeline to understand the ETL process. At the minimum, talking with them will either confirm your understanding of how the data is generated, or – if the overall process of how the data is generated is contrary to how you thought it was done – you will be able to call out data inconsistencies from a data analyst's / scientist's lens.

Exploring and cleaning data are pivotal stages in data analysis, crucial for reliable insights and effective modeling. Data exploration unveils patterns and insights through visualizations and initial analysis. Cleaning rectifies errors, ensuring accuracy by addressing missing values, inaccuracies, and duplicates. For instance, platforms like Python's Pandas library and tools like Tableau facilitate these processes. Additionally, Customer Data Platforms (CDPs) such as Salesforce and Segment aid in organizing and analyzing customer data comprehensively. In the data-driven era, these steps, coupled with CDP integration, are essential for informed decision-making and trustworthy predictive models.

Crafting an effective analysis approach requires judicious method selection, closely tied to your data's nature. Adapting from descriptive, inferential, predictive, to prescriptive methods resonates with tailored insights. The interplay between method and data type shapes your analytical direction. Balancing complexity in models, algorithms, or techniques mirrors strategic acumen. This alignment underscores precision, as your analysis strategy bridges diverse methods and data intricacies, revealing nuanced insights that empower decision-making.

Knowing what you are trying to achieve is very important in analyzing complex data. This is called Focus. It is easy to be distracted with many complex data in front of you but when you have your Focus, sieving less important data will be easy.

Refer back to the goal and scope decided and consider if you want to summarize data and trends, draw conclusions about a population, predict the future for a population or variable, or provide data-driven decision-making. Analysis types should be defined at the goal and scope stage since designing analysis after viewing data poses a risk of confirmation bias and manipulation. A few considerations can be the replication ability of the analysis model and whether the resulting data calculations are in the 'essential' or 'nice to know category'. For efficiency at this stage, try and maintain only the former and work with that. Doing so can also reduce data sprawl.

Incredible details in selecting different approaches to data whether it is numerical, categorical and predictive in statistical methods in.

For structured data sets with well-defined variables and objectives, traditional statistical methods such as regression analysis or hypothesis testing can be powerful tools. These methods help uncover relationships, patterns, and significance within the data, providing actionable insights. On the other hand, when dealing with unstructured or large-scale data, advanced techniques like machine learning and artificial intelligence come into play. They enable us to extract valuable information from vast datasets, identify trends, and make predictions. Moreover, for data involving temporal trends or seasonality, time series analysis is invaluable. It helps us understand patterns over time and make informed decisions based on historical data.

Just because your data is complicated, it doesn't mean your tools have to be. If your data can be easily managed with some analyses in Excel, go for it. Don't force yourself to use Python if you don't need to or don't know how to. If your data is as large as it complex and you are comfortable using SQL, then use that. Match your skillset to the data, don't just force yourself to use the most elaborate methods if they are not needed. Remember... simpler is often better.

It is good if we utilize Excel , SQL , Power bi and sometimes Python as primary programming language due to its versatility and extensive libraries for data analysis and machine learning, such as Pandas, NumPy, and Scikit-learn. You can follow a structured process, including data preprocessing, exploratory data analysis (EDA), feature selection, model training and evaluation. Throughout these steps, we pay attention to accuracy metrics, statistical significance and potential biases. Communicating results transparently and acknowledging limitations is crucial for a comprehensive interpretation.

Imagine you've opted for a predictive analysis method to enhance customer retention for an e-commerce platform. Armed with Excel, you craft a systematic approach, embarking on feature selection, ensuring only impactful variables are considered. Data division aids in training and testing your predictive model. Hypothesis testing comes into play as you validate assumptions. As solutions take shape, you optimize for the most effective outcomes. Your focus shifts to result assessment, where accuracy, reliability, and significance are measured. By identifying limitations or biases, you refine the model's robustness. This pragmatic application will showcase adeptness in steering complex analysis, extracting valuable insights from intricate data.

There are typically multiple ways to solve a problem. Consider ways to use your existing skillset and the tools already available. BI systems are great and have their place, but not everyone has access to these or the education to use them. It's ok to work with what you have and make sure others understand what you are/aren't comfortable analyzing with your existing resources.

The process of analyzing complex data involves a series of systematic steps, from defining objectives to communicating results. Adapt your approach to the specific requirements of your analysis and always be prepared to iterate and improve as you gain insights from the data. This strategy will help you make sense of complex datasets and extract valuable information from them.

Verbalize your insights. Clearly, concisely and with numbers wherever possible. One of the things I appreciate at Amazon is using documents as much as possible over slides. Presenting your insights as words and numbers instead of just some fancy chart (pointless word clouds anyone?) forces you to really think about what your data is saying and to not put the burden of deriving insights on the reader. This by no means is a dismissal of data visualization but rather a reminder to use the best and most efficient method of delivering insights. This also relies heavily on a culture of writing and reading on the part of most of the organization.

Picture this scenario: you've extracted actionable insights to optimize marketing strategies. Now, the focus shifts to effective communication. This step entails bridging insights to decision-makers without any distracting complexities. Clarity is paramount when addressing various stakeholders. Distilling key findings and decision-supporting elements aligns with strategic communication. Then you tailor the message for easy consumption with different formats and simplify complex insights with stunning data visualizations. This process parallels a translator converting intricate concepts into accessible language. Your role underscores seamless insights transfer, illuminating a pathway from data to impactful decisions.

Super important to know your audience when communicating insights. Whether that audience is broad or targeted, the nature of storytelling needs to reflect that audience's understanding of the underlying material. Consider the use of jargon or acronyms, consider what level of analysis that audience is accustomed to, and, importantly, direct the audience to what next steps could be informed by the presentation's insights.

Effectively conveying insights is crucial. Tailor visualizations to your audience's expertise level to clearly tell the data's story. Utilize various charts like line or bar for trends and comparisons, and employ graphs such as scatter plots or correlation matrices to illustrate relationships. Dashboards are excellent for providing an interactive summary of key metrics. In addition to visual tools, a comprehensive report is vital. It should succinctly present the main findings, thoroughly outline the methodology including data sources and analysis techniques, and clarify any underlying assumptions. Focus on clarity and brevity, provide actionable steps, and avoid jargon to enhance accessibility and impact.

I think a visual way of communicating the insights helps put forth the point and support decision making in a short period of time. On the other hand, a detailed documentation of the findings is necessary in the long run for analysts or new team members who might visit the legacy code and analysis. Fancy power points with 4 word key points and a graph will not be of much use and lead to wasted time and effort. Its like reinventing the wheel in many cases.

Adapting data, methods, tools, and skills is an ever-evolving necessity. Just as technology advances, so do challenges and opportunities. Keeping pace ensures relevancy. Seeking feedback unveils blind spots and fuels growth. Reviewing performance unveils successes and shortfalls. Identifying areas for improvement fosters agility. In this dynamic landscape, embracing change is a strategic imperative. A continuous cycle of enhancement drives innovation, equipping professionals to navigate shifting terrains adeptly. My take on this? If I'm not innovating, I'm stagnating!

As you grow in experience in a field, subject, or even type of analysis, leverage previous experiences to expand upon original goals, identify future data pitfalls, or extend insights beyond the original ask. Even if situations aren't obviously directly analogous, there is often a previous learning that can be applied to improve rigor, approach, or results.

Routines are an amazing way of improving and learning from data. Create a routine of reviewing your reports, create the habits of updating data against the newest trends. If you're delivering an ongoing report or updating a monthly sales dashboard, implement new charts, views... this will produce more insights as you will be learning more and more about your data, product and audience.

I like to conduct a post mortem analysis to find out if there were any blindspots in my recommendation to the team, and if I find a blindspot or a new perspective, I do a root-cause analysis to see if i can avoid missing this in the future and provide better analysis, recommendations, and perhaps even faster more automated analyses.

Remain abreast of emerging trends and advancements in data analytics, perpetually drawing lessons from each analysis to refine your strategies. Implement a feedback loop to vigilantly track the efficacy of executed strategies and decisions derived from the data analysis, utilizing this feedback for ongoing enhancement. Meticulously record your methodologies, presumptions, and discoveries to ensure transparency and for future reference. Actively disseminate knowledge and insights among your team and stakeholders, thereby nurturing a culture informed by data insights.

Steps to Analyze complex data: 📊 1) Understand: 🕵️♂️ Grasp data sources and structure. 2) Preprocess: 🧹 Clean, and handle missing values and outliers. 3) Objectives: 🎯 Define analysis goals. 4) Explore: 🔍 EDA for initial insights. 5) Hypotheses: 🤔 Formulate based on EDA. 6) Techniques: 🛠️ Choose fitting methods. 7) Model: 🧮 Apply chosen tools. 8) Interpret: 🧭 Understand the results' meaning. 9) Visualize: 📈 Create clear visuals. 10) Iterate: 🔁 Refine, learn, adapt. 11) Share: 🗣️ Communicate findings transparently. Stay adaptable and seek expert feedback for accurate analysis! 🚀🔍📊

Understanding your audience, their identity, and their objectives is crucial. Data itself is subjective, so maintaining an open-minded and unbiased approach is essential in data analysis, based on my experience. Data can be highly captivating, often leading to the presentation of biased information.

A good strategy for analyzing complex data involves understanding the context, data cleaning, exploratory data analysis (EDA), feature engineering, selecting appropriate models, iterative refinement, prioritizing interpretability, and rigorous validation and testing.

Sharing a few points from my experience: 1) Getting input from business stakeholders is critical before diving into the data. Questions such as What does success look like?/ What key business questions need to be answered?/What decisions need to be made? 2) Given complexity, I find rather than planning long sequential steps, planning shorter more iterative steps is more suitable 3) Getting feedback (if necessary) from stakeholders at regular intervals 4) Visualizing data to uncover patterns (esp. ones that spark further investigation) 5) With complex data, I prefer to rely on combining output from multiple models (i.e. ensemble)

“Be curious, not judgmental” -Walt Whitman via Ted Lasso Analysis is often mired by a pre-existing intention or desired outcome. We often start with a theory and subjectively - sometimes subconsciously- ask the data to prove it. Look for ways to learn what the data is telling you, instead of asking it to back up your assertions. As an example, I’ve played with ai; asking it for its insights on a data set, I’ve experienced a more objective and diverse discovery process.