You need to make sense of a complex data set. What’s the best way to get started?

To make sense of a complex dataset, start by defining your objectives and key questions you want the data to answer. Cleanse the data to ensure accuracy, removing duplicates or errors. Break the dataset into smaller, manageable segments. Use exploratory data analysis (EDA) techniques to identify patterns, trends, and anomalies. Employ visualization tools for better understanding and interpretation. Finally, apply statistical methods or machine learning algorithms if needed to extract deeper insights.

Making sense of a complex data set can feel overwhelming but with the right approach it can be an exciting and rewarding experience. Here are some steps you can take to get started Understand the data before diving into the data and clearly define what you want to achieve. are you looking for trends patterns outliers or specific relationships? Having a clear goal will guide your exploration and focus your efforts.

It is critical to understand a data set before working with it. Run a distribution or frequency of the data to understand some of the key characteristics. Look for outliers and anomalous data you might need to consider removing or creating a bound for. See if the data is consistently populated or if there are potential gaps or missing data...never take a data set for granted. One of the most important considerations is how will this data connect to other data sets. Rarely are data sets used in isolation. Understanding what a primary key or identifier is and how is can work with other data sets is crucial. Once these steps are considered determine some hypotheses to test and do not simply mine the data.

To make sense of complex data, start by understanding its context and variables. Identify key questions, assumptions, and what relationships we are trying to find. Next clean the data, and filter out any irrelevant data.

I would first take a step back and ask, what are you really looking for? While defining the problem statement or hypothesis is important (and necessary) when you take a step back to ask what question, you can gain clarity to help you assess whether the data set is complete, relevant, or even meaningful. Some questions are better answered with qualitative data over quantitative, or vice versa. Making the data make sense to the question is really important. It helps you think of the right metrics, gather the right data (and type) and analyze it properly to garner meaningful results.

Explorar os dados atentamente é uma estratégia para entende-los. Não tem uma técnica exata, mas a curiosidade é algo que ajuda no mapeamento das informações, e nos leva a um trabalho investigativo. A origem dos dados é extremamente importante para qualquer levantamento.

To make sense of a complex dataset, having a clear understanding of the business process that generated the data and what answers does the business need from the data is imperative, in addition to data cleaning and wrangling techniques everyone has already mentioned. For example, what is the acceptable range of transaction value? Do we really expect customer aged 45 years? Only understanding the business context would help an analyst see obvious problems.

Once you have a clear goal, it's important to explore your data. This involves checking the source, quality, format, and structure of the data. It's also crucial to identify any missing, inconsistent, or erroneous values and determine how to handle them. Descriptive statistics, like mean, median, mode, standard deviation, and frequency, can help summarize and examine the data. Exploratory data analysis (EDA) techniques, such as histograms, box plots, scatter plots, and correlation matrices, are great for visualizing and discovering patterns, trends, outliers, and relationships in the data. It's all about getting familiar with your data and uncovering insights! 📊

Add a creative session asking "what questions could this data answer?". Involving others in a needs session can open up new lines of discovery and fins answers to critical but sometimes obscure questions. Also consider if your data is a candidate for anomaly detection, which can find things you won't know to ask.

One helpful practice is to visualize your data and examine its distributions, especially when working with numerical data. This is a powerful tool that provides initial insights into the underlying story within your data. With this information, further analysis can be conducted to uncover hidden patterns or valuable insights.

without a clean data set, you will have faulty results and no one needs or wants that, so a proper clean up of your data, will give you all the information you need but in a data set that you can later analyze.

I’ve found AI can be extremely helpful when trying to transform data. This is especially true when working with complex Excel formulas as it’s possible to describe what’s required in plain English and use AI to generate a formula that’s ready to go.

After exploring the data, it's crucial to clean and transform it for further analysis. This involves using techniques like removing duplicates, fixing typos, replacing missing values, and standardizing formats to improve data quality. Additionally, data transformation techniques like scaling, normalizing, encoding, and aggregating can help modify the shape, range, or type of the data. By cleaning and transforming the data, we can ensure its accuracy, consistency, and suitability for our goals. It's all about getting the data in tip-top shape for analysis! 💪🔍

When cleaning and transforming data, in order to expedite my search, I use hide columns, filters, sort and highlight values. My first step is usually hiding unnecessary columns. Next, I filter down to the most critical details be they dates or other identifyers. Lastly, I sort and highlight in order to quickly find my results. If I’m working with complex data sets that need more manipulation than a simple clean up, I tend to lean on tools such as pivot tables and power pivots.

You can’t manage what you can’t measure Peter Drucker Data is the new gold in the global economy. Data allows organizations to measure the effectiveness of a given strategy: When strategies are put into place to overcome a challenge, collecting data will allow you to determine how well your solution is performing, and whether or not your approach needs to be tweaked or changed over the long-term.

The basic principle I’m applying (with good results) for customers with large amounts of data from old systems, mergers and acquisitions is very simple to start it off - first reduce before you refine. Working with quality if data does not make any sense until you have reduced your data population. Set the business rules for this firstly e.g. remove all customer data that has not been active in any transactions for the last five years. This can be applied to customer, vendors, materials, BoMs, cost centers and so on. Then start by sorting the data. If you have introduced data governance at some point then you can assume that data records that have been created after that point holds a certain quality. Contact me if you would like guidance.

Excel can be a good place to start but when it comes to more intricate and extensive data manipulations, one might want to opt for SQL or Python to handle more complex data analyses.

Building a Machine Learning model to evaluate trends might sound very flashy. But do you really need one! Most of the analytics from data can be revealed just by building a simple dashboard in spreadsheets or by using a BI tool. For example you want to check how much increase in sales is roughly expected in the coming year. You don't need an ML model to get those insights. A BI tool can easily give you the expected results based on the trends of previous data.

Once you researched & sorted with your user & result specific data as per your needs, analyse there parameters to choose the tool that simplify that data. First thing first, you should have the handy knowledge of the data analysis tool that you are utilising for your goal. Further get into the tool to organise your data to get user specific results.

Entendo que devemos selecionar métodos e ferramentas que facilitem a colaboração e a comunicação entre equipes, como plataformas de gestão de processos que ofereçam recursos de modelagem, automação e monitoramento. Considero muito importante também, ferramentas que permitam a participação ativa de todos os envolvidos no desenho e melhoria contínua dos processos.

Remember that your clients and/or stakeholders care primarily about the actionable insights you can deliver -- what they should start doing, stop doing, or continue doing -- from your analysis. They are less concerned about the intermediate steps, regardless of how much work it took you to get there. (I've done some of my best coding ever in data prep, but it's irrelevant to the client.) And actionable insights are easiest to communicate with the simplest graphs possible: bar charts and line charts. I use them extensively because they're clear and easy to interpret, even for non-data specialists. Put your actionable insights on the same slide as the graph that supports it, and point to the part of the graph that matters. Guide the eyes.

I streamline client communications by leveraging the STAR methodology for root cause analysis, focusing on Situation, Task, Action, and Result. This approach involves identifying the SITUATION, pinpointing the TASK at hand, outlining the necessary ACTIONS to be taken, and forecasting the expected RESULTS. This structure ensures clarity and precision in communicating complex findings. To ensure the communication of my findings are both efficient and impactful, I frequently condensed this analysis into a one-page summary, meticulously designed for clarity and conciseness. This layout allows clients to quickly grasp the core insights and recommended actions without being overwhelmed by unnecessary information.

LLega uno de los momentos más importantes del proceso, la comunicación y visualización de datos. Va ser indispensable tener en claro quien es nuestro cliente final o cliente interno, muy diferencia a quien es nuestro usuario. Porque si tenemos en claro a quién va dirijido (concepto del marketing), vamos a desarrollar el formato correspondiente de cómo visualizar la información que generamos (tableros de Power BI, documentos entregables, slides, narrativas, etc)

In my experience the more succinctly you’re able to communicate data the better. Most people won’t come away from a presentation remembering details from charts and graphs even if they’ve already been simplified from the original data set. Whilst there’s nothing wrong with including charts and graphs in presentations, if it’s possible to distill data down to a single figure it’ll likely have a far stronger impact during and after the presentation.

Gráficos limpos, claros e auto-explicativos são ótimos aliados em comunicação de resultados. Uma dica é pensar que uma ótima forma de comunicação é quando não é necessário que haja um comunicador intermediário, mas que, pelo próprio gráfico / apresentação já se entenda o que quer se dizer.

One of the greatest resources when you get a dump of fresh data from a client/source is someone who has worked with it before, or ideally is the data owner who knows it inside out. Find that person and talk to them, they will be able to make your life a lot easier.

The data exploration process is best performed when you have a clear sense of what your stakeholders are looking for. This can include expected values, screenshots, reports, or even the data tables if they are familiar enough with them. You’ll also want to understand what visualizations can best be displayed from their requests. It’s best to look at a granular level if possible and aggregate as needed.

Cuando hay que analizar un conjunto de datos, es necesario definir que se quiere lograr, luego ordenar los datos para saber con que recurso contamos, cuando se trabaja en orden todo fluye.

Before you do anything else, ask for a data dictionary! It’s impossible to do anything with data without knowing what is being measured, and how it was measured. You also need to distinguish between invariants (such as, in retail data sets, customer-related data) and variants (in the same situation, what was bought and how much was spent).