How can you improve your data analysis cycle?

To enhance the data analysis cycle, one must refine data collection, prioritize robust preprocessing, craft insightful features, embrace advanced techniques like machine learning, iterate through modeling approaches, communicate effectively with visualization, foster interdisciplinary collaboration, learn from feedback, automate processes, and remain attuned to emerging trends. Approaching analysis with scholarly wisdom leads to deeper insights and enduring impact.

Aligning your goals with the data recipients' needs is paramount. What are their questions, concerns, and pain points? Success in data analysis doesn't stem solely from pursuing the questions that intrigue you; it comes from a deep understanding of the questions your audience is asking.

Setting clear goals and questions before data analysis is like creating a plan for a trip. Without a well-defined destination and waypoints, the risk of wandering aimlessly increases, leading to wasted time and resources. The SMART framework serves as an excellent guide for this crucial planning phase. 1. Specific: Clarify the exact metrics or KPIs to focus on. Avoid vagueness. 2. Measurable: Ensure the goal can be quantified using metrics like percentages, dollar values, or other numerical units. 3. Achievable: Goals should be ambitious yet realistic. 4. Relevant: Align the goals with broader business objectives. 5. Time-bound: Assign a specific deadline for the goals.

When dealing with data one should adhere to the following: 1. Identify research goals and questions. This will help you determine the type of data you need to collect. 2. Choose the right data collection method. There are many methods available, such as surveys, interviews, observations, and experiments. 3. Collect the data. This can be time-consuming, but make sure it's accurate and reliable. Follow ethical guidelines, such as getting informed consent. 4. Store and analyze the data. Use software to help you access and analyze the data. (The best software for you will depend on your specific needs and resources). 5. Draw conclusions and make recommendations. Use your data to answer your research questions and make recommendations.

You need to define what the analysis cycle is: does it begin from collection or post-collection? If it is post-collection, several of steps are removed from the cycle - you can't scrutinise the validity of the data and must focus purely on analysis; if data collection is part of your cycle, then the first step is to understand whether the data is truthful to the source. These are hopefully a given to anyone working with data. When it comes to pure analysis, limiting the cycle to "Data Analysis" sets you up for trouble: a systematic approach needs to be taken: first, consider he obvious, does the data tell a story that makes sense? Second, does it make sense from other perspectives. The needed steps of the cycle flow from this logic.

In case of data that is a combination from various sources, consider the way these various datasets will communicate with each other. In other words, make sure you have enough columns to perform joins upon, and columns to be treated as foreign keys across these datasets. This is essential to create a useful, combined master dataset.

Many analysts just use 1st party business data and it’s often good enough. Sometimes though, you’ll want to consider additional data, which is called data enrichment. Alteryx says, “Data enrichment is the process of combining first party data from internal sources with disparate data from other internal systems or third party data from external sources.” The goal is to make the data more useful for the business’s goals. Examples of companies that provide data enrichment include Nielsen and LinkedIn. Nielsen provides data on competitive research and consumer habits. LinkedIn Sales Navigator provides details on prospective clients and customers. Data enrichment needs vary by industry and job function.

"Choose Your Data Sources" – Many times, you may not have the liberty to select your data sources, especially when dealing with retrospective data. Hence, I believe the title should be modified to "Managing Various Data Sources." When working with retrospective data, you'll need to invest time in comprehending data collection tools, potential error origins, data credibility, outliers, and more. On the other hand, when dealing with prospective data, your focus should shift to ensuring clean data. This can be achieved through measures like implementing validation in data collection tools or utilizing electronic surveys/forms, which facilitate the incorporation of validation. The choice of tools will vary to save both time and effort.

It is always good to have data triangulation of two basic source of data - routine monitoring and periodic survey (if available). Although data collection process is different on both cases but it give right direction.

Live data vs offline data - better to practise on both but the Excel guys love offline data and top of the chain wants you to run the dashes using the live data sources. so if you want to have best of both worlds, get hands dirty with both of them!

While adding custom columns to your data, always design the formula with the error case in mind. The formula should return an absolute value instead of an error in case of any dependent values being missing. This will enhance the quality of your data considerably.

The title of this paragraph rightfully belongs to the data cleaning process, a crucial initial step for accurate analysis. Data visualization holds significance, but it follows essential cleaning. Visualization can assist in the cleaning process, such as identifying outliers via box plots. However, when discerning data patterns or trends, data cleaning takes precedence to prevent potentially misleading results. Furthermore, data preparation involves more than just addressing missing values and outliers. It requires handling inconsistent formats, addressing duplicates, and ensuring uniformity in data types. Properly prepared data ensures that subsequent analysis and modeling yield reliable outcomes

Exploring and visualizing data is a bridge from abstract goals to actionable insights. Start with descriptive statistics like mean and median to understand your data's basic traits. Select appropriate visualizations: line charts for time-series data, bar graphs for categorical data, and mapping tools for geospatial data. Interactive dashboards can offer real-time insights and are useful for stakeholders. Check Data Quality by checking for errors or missing values; cleaning is essential for accurate analysis. Feature engineering can emerge during this phase, so keep an eye out for variables that could serve as effective predictors in machine learning models. Finish with simple, yet effective visuals that clearly tell your story.

Blank cells / data points are ALMOST always unacceptable. N/A or a zero; SOMETHING to indicate that data is not missing but instead is literally not applicable or null.

Exploring and visualizing your data can provide valuable insights and help identify patterns, trends, and outliers. Visualization techniques like charts, graphs, and dashboards make it easier to understand complex data sets and communicate findings effectively.

The process of analyzing and interpreting data often involves leveraging inferential statistics to uncover relationships within a dataset. It's vital to complement these methods with critical thinking and domain knowledge. This fusion enables an evaluation of validity, significance, and the real-world implications of the findings. By combining these approaches, one can draw more valid insights, offer informed recommendations, and construct persuasive arguments based on a comprehensive and theoretically-informed understanding of the data.

The analysis and interpretation phase is where data takes on actionable meaning. Use inferential statistics like t-tests or ANOVAs to validate hypotheses and generalize findings. Choose suitable machine learning models or algorithms aligned with your research question. Apply critical thinking, considering potential lurking variables or confounding factors. Leverage domain knowledge to make your interpretation statistically accurate and practically relevant. Examine p-values and confidence intervals to assess statistical significance but differentiate it from practical significance. Summarize findings and make actionable recommendations.

Employing diverse algorithms and frameworks for data exploration and relationship building is crucial. However, clear communication with stakeholders and data engineers throughout the analysis process remains pivotal. This ensures a comprehensive grasp of data structure and business context, leading to meaningful insights. Moreover, understanding the business landscape is vital. This knowledge enhances critical thinking and aids in addressing pertinent challenges. Tailoring presentations to stakeholders' backgrounds is instrumental in conveying insights effectively. Emphasizing seamless communication is integral to successful data analysis endeavors.

Once you've explored the data, it's time to analyze it to uncover meaningful insights. This involves applying statistical methods, predictive modeling, or other analytical techniques to extract valuable information. It's crucial to interpret the results in the context of your goals and questions to derive actionable conclusions.

Using your analytical and logical thinking, you should have different options to interpret your data. I like to implement certain models so I can create insights and conclusions so I can present the proper analysis to my stakeholders.

Empathy is a key. Put your shoes in your audience's perspective. Think about what they want to know, and elaborate on how the findings will be communicated. The goal is to communicate the finding, so it does not just inform the audience but also makes them understand

I've always liked asking the end user how they'd like the data summarized. The key is to ask at the start of the analysis cycle! Sometimes the end user won't have even considered how they intend to digest your findings and asking in advance plants the seed that you're thinking about the end result as well and are committed to the work.

Communicating your findings effectively is essential for ensuring that your analysis has an impact. Whether through reports, presentations, or interactive visualizations, conveying the insights clearly and compellingly facilitates understanding and decision-making among stakeholders.

There is an interest balance between being descriptive and being persuasive, and how one manages that depends on the goal of the analysis. If the analysis is focusing on discovery, the interpretation should stay clear of having any opinions, so that the analysis does not bias in a certain direction. On the other hand, if the analysis is to drive to a decision, it is important to form a strong opinion at the end of the data analysis. It is okay if the recipient does not agree, but having opinions is the starting point of being helpful.

By employing analytical and logical reasoning, diverse approaches can be applied to interpret the data. I prefer employing specific models to derive insights and conclusions, allowing me to present comprehensive analyses to stakeholders.

Your analysis will never be perfect in one shot. After you've presented your report, ask for comments and feedback from different stakeholders across teams. Refine your future analyses by incorporating that feedback. It's a good idea to ask them what they liked, what was not sufficiently explained, and what was missing that they would've liked to see. Over time you'll develop a standard and consistent style that will appreciated by a wide range of audiences at your firm.

Continuous improvement is key to enhancing the effectiveness of your data analysis cycle. Regularly review your processes, methodologies, and outcomes to identify areas for optimization. Incorporating feedback and lessons learned from previous analyses enables you to refine your approach and achieve better results over time.

A great way to solicit feedback is asking "What was most valuable about this analysis for you?" It accomplishes a few things: It assumes that the analysis was valuable, it personalizes the feedback, and lowers the barrier to future feedback. After all, if this analysis was valuable, the next one will be too!

Data analytics can be a challenge as many things in life it can easily become a never ending task. Choose small measurable and narrow goals to start and then build iteratively from there. Publish, document, share, each of the iterations and don't feel ashamed if the first iterations result is "data is showing XYZ, is that possible"? Test your results and collect feedback from stakeholders from the very beggining to avoid carrying wrong assumptions longer than necessary.

Don’t be obsessed with sample size. In my experience, some people question the validity of an analysis because it comes from a small sample of people. However, a small sample does not automatically negate the findings. On the contrary, if we can observe the same effect in a small but representative sample, it might prove more useful than a study from a large sample. Effect size matters more. Data analysis in business is meant to give us confidence in nimble decision making when time is a key constraint. Statistical significance is good to have but we’re not publishing an academic paper that require higher level of statistical rigour.

I think it is important to emphasize that Less is more when it comes to Data analysis. More often than not, the PM/Data analysis team has this habit of coming up with 20-30 different metrics to track for any feature and what ends up happening is that it becomes much more difficult to really understand what is happening as 5 of the metrics moved in Positive direction while other metrics moved in the negative direction. Hence, it is important to identify early on, the key metrics that are important for your feature/product even before starting the data analysis process. This makes the whole process more crisp and more insightful

Principle of Simplicity Remember Albert Einstein's statement "Everything should be made as simple as possible, but not simpler". When attempting to explain complex ideas or concepts, it is important to simplify them as much as possible without oversimplifying or distorting the main point. It is also useful to apply this principle when choosing your tools. Forget that fancy and sophisticated AI new tool if it is not really necessary. But If It is, go for it.

The Curse of Dimensionality - It’s important to keep in mind when building your model that as you add more features, you are increasing the possible answers making it potentially harder to predict your answer. This is why it is important to do the analysis first to find what can best predict what the audience is looking for. We should only be adding feature which give us good gain and correlate well to a prediction. More features in a model does not always mean better unless those features tie very well to the model.

I’d add a kind of stages, above the described cycle steps. Like initial research, refinements, improvements etc. It’s not such straightforward as described