How can you maintain a positive outlook when working with difficult data sets?

Before diving into any dataset, I make sure I have a strong handle on the answers to the following two questions: 1) What are the fundamental business question(s) that my analysis is intended to answer? 2) What is the stakeholder's expectation re: the product I will be producing from this dataset? Particularly with difficult datasets, you want to make sure you're not fighting a bigger battle than necessary. For example, if the product is memo summarizing a statistical analysis needed to answer the business key business questions, I may need to clean and model far less data than if I am expected to build a dynamic dashboard involving the additional dimension tables necessary to build out the full data model required by the dashboard.

I believe it's important to do a few pre checks before diving into the actual analysis. Understand the problem thoroughly and make sure it satisfies the SMART Criteria. Split the requirement into smaller goals and check if each one is achievable. Finalize your goals and handle the task in phases. Have a strong hold on the key fields in your dataset. I usually talk to my seniors/peers to check if they have worked on similar task before. This could give you half the answer sometimes. Having basic functional knowledge has helped me a lot in deciding my approach to solve an issue. You could talk to related stakeholders and translate functional insights into technical aspects. After having this clarity, the task wouldn’t so complex after all.

Honestly, working with data without an objective is like beating around the bush. Ask yourself some basic questions like; - what am I trying to achieve? - what kind of data are you working with ? Some of these explorations will help you determine the data analysis approach. Communicate your findings and ask for feedback. Once you have understood the goal, analysed the data all the difficulties are solved in itself. Its all about effective communication of your findings and act accordingly to the feedback received.

Reading poetry by the great poets is a must. Glory be to God for dappled things – For skies of couple-colour as a brinded cow; For rose-moles all in stipple upon trout that swim; Fresh-firecoal chestnut-falls; finches’ wings; Landscape plotted and pieced – fold, fallow, and plough; And áll trádes, their gear and tackle and trim. All things counter, original, spare, strange; Whatever is fickle, freckled (who knows how?) With swift, slow; sweet, sour; adazzle, dim; He fathers-forth whose beauty is past change: Praise him. Source: Gerard Manley Hopkins: Poems and Prose (Penguin Classics, 1985)

There is no solution without an underlying problem. Just as stated above, using the SMART framework is a practical and helpful tool to help you state your goal.

From my perspective, defining the problem or question is critical once you’ve grasped the context. Without a clear focus, it's easy to get lost in endless data exploration. On one project, after understanding the stakeholders' goals, I used a SMART framework to set specific, measurable objectives, and this helped reduce delays and identify cost-saving opportunities. This focused approach kept my analysis on track and ensured I worked with the most relevant data.

To define a starting point, I usually use 2 approaches: 1) Where are my quick wins? 2) Where can I get more value? To answer those questions, I must know my data and keep my stakeholders close, so I can understand their expectations.

In my experience, I start by challenging the data with logical conditions. I run queries and analyses to see what is true or not true about the data. In this way the data speaks to you about its nature. There are times when "tricky" is really "messy", so I do what I can to clean it up based on rules previously determined. Other times, the data is "tricky" because it presents profound problems with or lack of governance. At that time, its best to define data quality processes and pass them on to those handling the data.

Working with tricky data can be tough. To stay positive, try breaking the work into smaller parts. This makes it easier to handle. Also, keep an open mind and stay curious about what you can learn from the data. If you feel stuck, take short breaks to clear your mind. And don't forget to celebrate small wins along the way. They help you see progress and keep you feeling good about the work you're doing.

Before moving on to complex data analysis techniques, it's smart to first get familiar with your data using straightforward methods. Look at simple summaries (like mean, median, or mode), create basic graphs (like histograms or scatter plots), and check the overall structure and cleanliness of the data. This initial step helps you uncover any problems with the data—things like missing, duplicates or extra entries, unusual values, or errors. Fixing these early on makes your data more reliable and your results more accurate and trustworthy, setting you up for success when you apply more advanced methods later on.

Without doing EDA we might end up with wrong analysis. As Duplicates, Missing Values & Outliers skew the end results. Let's dive some what deeper into it. If we don't remove duplicates our Final Value will be more than double than actual which should be. And vice a versa happen if we don't fill up missing values. And outliers land is to wrong Final Average Value which is also not good .

Break down complex problems into smaller, manageable tasks. Tackling one piece at a time can make the process less overwhelming and help maintain motivation.

When working with difficult data, once I have a strong grasp of the larger business context, I dive into an Exploratory Data Analysis (EDA) to better understand the nature of the data. There are many great EDA packages in Python and R that will automate much of the work in this stage by producing a full EDA report with just a line or two of code. These reports address an array of data quality challenges: duplicate records, outliers, missing variables, improperly coded categorical variables, improperly typed fields, etc. With a good handle on the range of data pitfalls to be addressed prior to performing my primary business analysis, I can zero in on a strategy for addressing the data quality issues.

Manter a mente aberta é sem dúvida uma necessidade durante a análise de dados por vários motivos. é importante n?o olharmos aquelas letras e números com uma ideia pré-definida do que eles iram mostrar. Ser flexível e deixar os dados falarem é essencial. Outra maneira de deixar a criatividade aflorar, é se possível, pedir opini?o de outros colegas, e n?o nos fecharmos na nossa bolha. Muitas vezes o outro consegue ver o que nos n?o conseguimos.

Flexibility is key when dealing with challenging data sets. Be open to trying different approaches and methodologies. Think creatively about how to preprocess, transform, or interpret the data. Embracing a mindset of experimentation often leads to innovative solutions.

In the world of BFSI data analytics, flexibility is our superpower! ??♀? Recently, our fraud detection team faced a Hydra of messy datasets. Traditional methods? Useless. So we got creative! We built a hybrid model, blending machine learning with good ol' domain expertise. Think AI meets veteran fraud investigator intuition. ?????♂? A/B tested like crazy! One algorithm caught more fraud, another reduced false positives. Our solution? We used both! Created a two-stage screening process that's now our secret weapon. Remember, the path to insight is rarely straight. Embrace the twists, celebrate the pivots! #FinTechDataAgility

Flexibility and creativity are key in everything we do in life and data analysis is no exception ?? - Adaptability: Tackling tough data sets often means changing tactics - think of it as using different tools for different challenges. - Data Cleansing & Transformation: This is like prepping ingredients for a complex recipe, ensuring everything is just right for the analysis. - Evaluation Tools: Techniques like A/B testing and cross-validation are essential for measuring the effectiveness of your approach, much like a pilot testing different flight paths. Embracing these strategies leads to more robust, accurate insights.

La creatividad y curiosidad son herramientas importantes para descubrir patrones en los set de datos. Más allá también se pueden crear nuevas columnas o indicadores los cuales pueden relacionarse al problema u oportunidad de negocio.

Communicating the findings to the stakeholders. In my experience, it is important to present the key findings to the stakeholders clearly and concisely. While it is easy to be carried away by numerous findings of our analysis it is vital to include what the stakeholders are interested in and relevant information that affects them. At the same time, the limitations of the data and the margin of error/confidence level of the findings should always be included in the presentation and the documentation of the report with our recommendations.

Comunique claramente suas descobertas, tanto para você mesmo quanto para outros interessados. A transparência no processo ajuda a manter o foco na resolu??o de problemas.

Data storytelling in wealth management is an art! ???? Our AI portfolio optimization project challenged us to translate algorithms into advisor insights. Microsoft Fabric dashboards brought numbers to life. Portfolio risk became a visual journey. We highlighted data limitations alongside wins. That outlier skewing results? A teachable moment on market anomalies. Lesson: In finance, clarity breeds trust. Break it down, visualize it, keep it real. Your stakeholders will thank you! #WealthTechCommunication

Entender que a comunica??o é a tarefa mais importante em todo processo. Adaptar seu sistema de comunica??o ao contexto da virtualiza??o do trabalho. E estruturar um sistema formal de comunica??o, definindo as bases de uma linguagem comum para toda organiza??o. Definir rituais e processos formais para garantir a escalabilidade e fluidez da comunica??o. Estabelecendo uma governan?a para o sistema de comunica??o da organiza??o estruturando métricas e auditoria de acompanhamento.

Presenting recent analytics findings concisely. Tailor communication based on the audience—high level for executives, more technical for specialists. Use visuals for clarity, follow a storytelling structure, and emphasize key insights. Be transparent about limitations. Provide clear, actionable recommendations. Encourage questions and follow up for clarity. Document the process for future reference.

Being able to accept and adjust the things as per the mutually acceptable grounds is the talent. One should be always open to feedback as one of the principles of sales also says that "after service" to any product is the high grossing feature anyone will bet on.

Sometimes what makes sense to you might not make sense to end user. Therefore, it is important to get feedback from peers and stakeholders on what they liked and what they didn't like. Learn from your mistakes and work on feedback received.

One thing I have found helpful learn from feedback until and unless you don't receive a feedback there is no scope of improvement. And one can really learn from it and start implementing which I always do. Mark the difference.

Esteja receptivo ao feedback. Seja através de colegas, stakeholders ou análises posteriores, o feedback é uma ferramenta valiosa para aprimorar suas habilidades de manipula??o de dados.

Se tivermos um ritmo de feedback, só iremos melhorar na nossa jornada na evolu??o tanto como profissional como indivíduo. Podendo mudar a nossa rota rapidamente caso estejamos indo por um caminho n?o tanto eficaz. O feedback é uma necessidade fundamental para quem está em busca de entregar o melhor a sociedade. Tornando um relacionamento satisfatório entre o profissional e o cliente.

If you want to learn how to better handle difficult datasets, hang out where the experts who love wrestling with these questions hang out - the nightly @Excel BI Challenge on LinkedIn. 7x a week, 365 days/yr, Vijay Verma posts data cleaning and transformation problems that range from challenging to fiendishly difficult. Although these challenges were originally intended for Excel and Power Query, responses include R, Python, SQL, M, F, Julia, DAX - whatever you choose is fine. And the community, composed of some of the best BI coders in the world (including top finishers in the Excel World Championship and multiple MVPs) could not be friendlier or more welcoming. If you want to level up your skills in this critical area, check it out!

Get a better data set. Some data is just trash and should be purged. Data quality trumps all. And I don't have anything else to say on this matter. Is this enough characters for you, LI?

Data validation is key in making sure your analysis is both high-quality and reliable. Basically, it's about going through your data carefully to make sure everything is accurate and lines up. The best move is to get a solid validation process in place from the start. This means deciding what counts as valid data, keeping an eye out for any outliers, and making sure all the data types and formats are in line. A pro tip: automate these checks if you can. It's a real time-saver and helps cut down on mistakes. The bottom line is when your data's been thoroughly checked and validated, you can trust your analysis more, and any conclusions you draw will be on solid ground.

Em projetos de Business Intelligence (BI), é essencial manter uma perspectiva positiva ao lidar com conjuntos de dados difíceis, pois esses desafios muitas vezes representam oportunidades para melhorar a compreens?o dos negócios e impulsionar decis?es mais informadas. Ao enfrentar dados complexos, é importante focar na análise e interpreta??o dos dados para extrair insights valiosos, em vez de se deter apenas nas dificuldades técnicas. Lembre-se de que cada obstáculo superado é um passo em dire??o ao sucesso do projeto e ao aprimoramento das habilidades de análise de dados da equipe.