Here's how you can tackle complex data problems in a Data Analytics role.

Understanding data and preprocessing the data is a crucial step in data analytics to ensure that the decisions we derive from the data is accurate and reliable. Clean the data to make sure we eliminate any inconsistencies in the data. Check for missing values and outliers that may introduce bias. Perform any data transformation as deemed necessary based on the problem under consideration. Also, consider eliminating duplicates.

It's very key to understand the complex data problem hence more time should be spent in trying to understand the scope and the objectives of the task at hand. Once that has been attained, the obvious steps the follows such as planning, data gathering etc but paying attention to the source, completeness, accuracy of the data as getting this wrong affect the overall results of the analysis. The next step would then be to explore the data and get better understanding. Based on the volumes and nature of the data, one can then choose the appropriate tool to use provided one is familiar with a number of data analytics tools. If the above is properly done, I guess the actual analysis and reporting shouldn't be hard depending on the complexity.

Take a deep breath and tell yourself - Perseverance is the key. 1. Identify the Source of truth clearly. There can be a single or multiple sources of data. 2. Always keep in mind the goal or business objective that you want to achieve. What decisions and actions do you want to drive by diving deep into this data? 3. Watch the data streams, attributes, formats, schema in whatever native form it is available. 4. Infer the key characteristics of data - quantity, quality, columns, rows, relationships between line items, etc. 5. Draw a model to show what shape & form matches or is close to your ultimate business objective. 6. Live and breathe with your chosen or prioritised dataset. Think about how to tackle anomalies and feedback to data source

Understanding and preprocessing data is indeed crucial in data analytics to ensure accurate and reliable insights. Based on my experience, here's what I've found most effective: - Start with thorough data cleaning. - Always check for missing values and outliers: Identify and handle these issues, which can significantly skew results if left unchecked. - Data transformation is key: Depending on the problem, need to perform necessary transformations, such as normalization or encoding categorical variables. - Don't forget to eliminate duplicates: I've found this step crucial for maintaining data integrity. - Leverage automated data quality checks: which has been invaluable for maintaining data reliability over time.

Frame the question you're trying to answer. What insights are crucial for business decisions? Clearly defined questions guide your data exploration and analysis. Collect relevant data from various sources, ensuring its quality and completeness. Scrutinize it for inconsistencies, missing values, and outliers that might skew results.

Many try to develop models without exploring and visualizing data. It is not recommended to do that. Garbage in Garage out. This is the first thing to do when starting a project.

Understanding Data for Complex Problems Scrutinize Data Sources: Examine where your data is coming from to ensure its reliability and relevance. Identify Data Types: Understand the different types of data you have (e.g., numerical, categorical). Explore Relationships: Analyze how different datasets relate to each other. Identify Issues: Look for missing values, outliers, or inconsistencies that may affect your analysis. Build a Solid Foundation: Ensure that your analytical models are based on thoroughly vetted data. Ensure Reliability: By understanding your data, you create a dependable basis for your conclusions.

I'm glad to share some insights on how to understand data based on my current experience as a business analyst. This is how it generously goes for me: Step 1: Identify the challenge. Step 2: Pretend to be a baby. Always try to maintain a curious and blank-slate mindset when facing datasets to avoid potential biases in analysis. This is critical for making authentic data-driven decisions. Step 3: Analyze data quality. The data can only be useful and interesting when it is clean and of high quality. Step 4: Understand the data structure and correlations between fields and records. Step 5: Define an actionable plan based on the analysis outcome. Step 6: Prosper with data-driven decisions through continuous and timely updates.

To understand complex data you need to understand business first then business transactions and then you will easily recognise relationship between data points and after that your analytical work will begin.

Undertaking complex data problems in a Data Analytics role comprises series of practical skills, analytical thinking, and effective communication. Here are some steps to take in any problem at all: Understanding the Problem Data Collection and Preparation Exploratory Data Analysis (EDA) Modeling and Analysis Interpreting Results Communicating Insights Implementation and Monitoring Continuous Learning and Improvement By following these processes, complex data problems can be solved in data analysis accurately to align with business goals.

Definir objetivos claros é essencial para orientar a jornada analítica em meio à complexidade dos dados. Enfatizo que sem objetivos bem definidos, é fácil se distrair com o mar de dados e perder de vista o que realmente importa. Objetivos claros atuam como um farol, mantendo o foco nas perguntas críticas e nos problemas que precisam de solução. Na análise de dados, o objetivo não é apenas processar números, mas sim extrair insights que possam ser traduzidos em ações estratégicas.

Data problems without objectives is subject to interpretation which can lead to bias and inaccuracy. Define your objectives and trace a clear definition of the end goal. Identify possible issues on your data that can affect the end goal and potentially influence in your end objectives.

With the vast number of tools in the market, it is essential to set key objectives and goals for your data analysis. A great first step is to determine the business questions or objectives that need answering. By establishing the targeted insights and outcomes, you can choose the best tools and analysis techniques needed while staying aligned with your goals. This approach will enhance productivity and ensure focus without compromising relevance.

Before diving in, define your goals. What questions are we trying to answer? What are the big-picture objectives? This focus acts as a compass, guiding your analysis and keeping you on track to unearth impactful insights that fuel strategic decisions. Remember, data analysis is more than number crunching – it's about uncovering meaning that drives results.

Connect with stakeholders to understand their needs, expectations, and the business context. Align data analytics objectives with the broader business goals. Understand the key performance indicators (KPIs) that the business cares about. Ensure your objectives are Specific, Measurable, Achievable, Relevant, and Time-bound. Clearly define what you aim to achieve. Identify how you will measure success. This could be through metrics like reduction in resolution time, increase in sales, etc. Set realistic goals considering the resources and constraints. Ensure the objectives are aligned with business priorities. Set a timeframe for achieving these objectives.

Defining the objective is crucial because it provides a clear focus and direction for the analysis. A well-defined objective helps in identifying the key questions to be answered and the specific data needed, ensuring that efforts are aligned with the desired outcomes. By establishing clear goals, we can communicate effectively with stakeholders, ensuring that the analysis meets their expectations and addresses their concerns.

Clearly defining objectives is crucial when dealing with complex data. Knowing the questions to answer or problems to solve guides the analytical approach, preventing distractions by irrelevant details. Innovate by using advanced AI and machine learning techniques while maintaining explainability. Data analytics is not just about crunching numbers; it's about extracting meaningful insights aligned with strategic goals. Implement visualization tools to make results comprehensible for all stakeholders.

Keeping focus on what you want to achieve cannot be pronounced heavily enough. Often asking the right questions to start is more difficult than finding answers. So make sure you and all colleagues working on the issue know what the analysis is about. Do not just jump into the data without at least roughly knowing where you are supposed to end.

Identify Stakeholders: Determine who will benefit from the insights derived from your data analysis. Stakeholders could be internal teams, decision-makers, customers, or other relevant parties. Understand Business Goals: Gain a thorough understanding of the business goals or problems that need addressing. This will help in your data analysis objective. Formulate Questions: Based on the business goals, questions that you want to answer with your data. These questions should be precise and measurable, guiding your approach. Prioritize Objectives: Prioritize Objectives based on their importance to the business. Define Success Criteria: Clearly articulate what success looks like for each objective such as KPIs, metrics.

Always start by clearly defining your objectives right from the get-go. Knowing what questions you want to answer or problems you aim to solve is key. This clarity not only keeps you on track but also helps you focus on what truly matters amidst all the data noise. When you define your objectives, think about how they fit into your organization’s bigger goals and the specific challenges you’re tackling. This broader perspective guides you in pinpointing the most impactful insights to pursue. Remember, data analysis isn’t just about numbers; it’s about uncovering meaningful insights that drive real decisions and actions. This will not only streamline your analysis but also ensure you’re making a direct impact where it matters.

Excel is the most popular for analyst .. I have primarily used Excel, solving almost all problems with this tool for many years. As Excel continues to develop, I can still effectively address all issues with its help. Additionally, I have trained in Python and SQL, which are also efficient and powerful tools. However, the core of my work remains with local Excel, regardless of the build or release.

Tool selection is key for effective data analytics. For large datasets and complex analyses, I prefer Python with libraries like Pandas and Scikit-learn. For visualization, Power BI has been my go-to, creating interactive dashboards that boost decision-making. Azure Data Factory is excellent for ETL processes, handling diverse data sources efficiently. For real-time data, I've used Azure Stream Analytics with Kafka. The best tool depends on your specific needs, data volume, and desired outcomes. I often combine tools for optimal results. For instance, I might use Python for data preparation, then Power BI for visualization. Always consider factors like team skills, scalability, and integration with existing systems when choosing tools.

Tool selection in data analytics is a critical determinant of efficiency and insight generation. Consider factors such as dataset size, complexity, and desired output when making choices. For instance, while Python offers unparalleled flexibility and a vast ecosystem of libraries, R might be a good choice for statistical modeling. Similarly, SQL is absolutely necessary for data manipulation while tools like Tableau or Power BI shine in interactive visualization. Crafting a strategic combination of tools tailored to specific data analysis requirements is essential for uncovering actionable insights.

Tool is important aspect as manual exercise is cumbersome and long drawn. A tool helps in: - Creating Repeatability and helps run several iterations, or periodic refresh of data. - Ensures quick and changing visualization such allows critical review and freeze on the best alternative visualization - Mechanization and on-demand publish of dashboard - Mechanized data transform through multiple stages - Minimize chances of error injection through steps - Select the best cost effective tool that suffices the need, not a sword to cut a loaf of bread. Focus must be on the "end" and not on the "means" (tool).

Depending on the complexity and nature of the data, we need to determine the tools that are best suited for achieving our objectives. Below some of tools and their features- Python Best for: General-purpose data analysis, large datasets, predictive modeling, machine learning, automation. R Best for: Statistical analysis, data visualization, academic research. Tableau Best for: Creating interactive and shareable dashboards, visualizing complex data quickly. Power BI Best for: Business intelligence, creating interactive reports and dashboards. Excel Best for: Basic to intermediate data analysis, quick calculations, small to medium datasets, financial modeling. SQL Best for: Managing and querying large datasets, relational database management.

When dealing with complex data, selecting the right tools is essential. Consider the following options: 1. Power BI (Great for data visualization) 2. Tableau (excellent for BI) 3. Qlik Sense (great for machine learning) 4. Klipfolio (perfect for real-time metrics) 5. Zoho Analytics (robust insights) 6. Domo (optimizes workflows)

Data science offers a wide variety of tools to choose from. While it is necessary to have a basic understanding of most of them, it is crucial to select the right tools for the specific task at hand. For example, Excel is often preferred for most visualization tasks. However, when dealing with large datasets, tools like Power BI become more suitable. This principle applies to every step in data analysis, from selecting the appropriate programming language to choosing the right communication tools.

Para o projeto final varios fatores podem influenciar na escolha de ferramentas que irão compor o pipeline, e estes devem ser avaliados criteriozamente como volume dos dados, frequencia de atualização e como cliente final espera visualizar. Para começar o projeto uma amostrar já é o ideal, e ferramentas mais basicas podem auxiliar a compreensão dos dados como o Excel, R e Python.

Choosing the right tool can significantly streamline your workflow and enhance your ability to uncover insights. For instance, Python and R are powerful for handling large datasets and performing complex statistical analyses. Excel, on the other hand, is excellent for quick data manipulation and exploratory analysis. Tableau is renowned for its ability to create interactive and visually appealing dashboards that make data insights easily accessible to non-technical stakeholders. It's also important to consider the scalability and integration capabilities of the tools you select. Tools that integrate well with other software and platforms can provide a more seamless workflow.

A minuciosidade na análise é o que separa um bom analista de um grande analista. A limpeza e preparação dos dados são etapas que não podem ser subestimadas. Dedicar tempo para normalizar formatos, tratar valores ausentes e remover duplicatas é essencial para a integridade dos resultados. Essas ações são a fundação sobre a qual análises confiáveis são construídas.

After defining a clear objective and selecting the appropriate tools, begin your analysis by cleaning and preparing your data. This step may include standardizing data formats, addressing missing values, and eliminating duplicates. Next, apply statistical models and algorithms to identify patterns and relationships within the data. Comprehensive analysis typically involves several iterations, where you test various hypotheses and fine-tune your models to ensure precision and reliability.

When it comes to analyzing : We should begin by meticulously preparing your data—cleaning, normalizing formats, and addressing missing values and duplicates. This foundational step sets the stage for accurate analysis. Utilize statistical models and algorithms to uncover intricate patterns and relationships within your dataset. Remember, analysis is an iterative process; testing various hypotheses and refining models ensures robustness and reliability in your findings. By committing to a comprehensive approach, you not only enhance the accuracy of your conclusions but also gain deeper insights that can drive informed decisions and transformative actions.

Clear objective and the right tools, the next step is to dive into the analysis. This typically involves cleaning and preparing the data, which includes tasks such as normalizing data formats, handling missing values, and removing duplicates.

Analyzing a dataset thoroughly is like detective work. You start by examining the data's origin and structure, cleaning inconsistencies, then exploring for patterns and trends. This exploration guides deeper analysis with statistics or machine learning to answer specific questions. Finally, you present your findings clearly to unlock valuable insights.

Thoroughly analyze by first cleaning and preparing your data—normalizing formats, handling missing values, and removing duplicates. Then, use statistical models and algorithms to explore patterns and relationships. Iteratively test hypotheses and refine models to ensure accuracy.

With clear objectives and the right tools, you can begin your analysis. Start by cleaning and preparing your data, which involves normalizing data formats, addressing missing values, and removing duplicates. Next, apply statistical models and algorithms to explore patterns and relationships within the data. A thorough analysis typically involves multiple iterations, testing various hypotheses, and refining your models to ensure accuracy.

Analysing data thoroughly gives us flexibility of what we can achieve and what are the steps , in simple words a perfect roadmap to the logic development which gives the end result in a very easy briefing insights.

After all the gathering, clear objectives set, you're now ready to analyse based on the objective of your dataset. You format promptly, trim white spaces, duplicates removal and other analyses should be carried out. Ensure you have a great data to allow a smooth flow through process of visualisation.

Next, use statistical models and algorithms to explore patterns and relationships within the data. Depending on your objectives, this could include descriptive statistics, predictive modeling, clustering, or regression analysis. Thorough analysis often involves multiple iterations, where you test different hypotheses, refine your models, and validate your findings. During this process, it’s essential to remain objective and critical of your results. Look out for biases or anomalies that might affect your conclusions. Utilizing techniques such as cross-validation or bootstrapping can help assess the robustness of your models and prevent overfitting.

The stakeholders should get convinced by your findings and insights to apply it in business model. To convince people, we need to interpret it by considering the business perspective and modify findings into business people's understanding format. Give a thought about your findings and how it can be apply into real-world scenario. How better we can tackle the problem and how to uproot at the root level.

In my opinion, proper interpretation ensures that the findings are accurately understood and relevant to the problem at hand. When interpreting results, it's essential to consider the context of the data, the methodologies used, and any potential biases or limitations that might affect the outcomes. It's important to validate the findings through comparison with known benchmarks or additional analysis to ensure reliability.

Interpretation of results might help in-hand to analyse our work flow is running fine or not. It also need to be checked and observed clearly and checking on logics thoroughly before using then on actual data with help of scenarios and testing.

A necessity, collaborate, team up with like mind people and be open positive criticism as regards your data would be a go to point.

In my experience providing a clear and concise definition of the data displayed will improve trust scores by alleviating back-and-forth clarification cycles. For instance, "Client Size: 75,825" will mean one thing to a Product Engineer (75,825 GB of data on a server), and something entirely different to an Account Executive ($75,825 potential ARR). Define your data, title visualizations properly, include legends, and eliminate remaining ambiguity by annotating your interpretations and sharing them alongside the data. Providing this degree of context will help ensure alignment, and increase confidence at the point of consumption.

To interpret results, you should develop and validate models. To translate findings into actionable insights. - Model Selection: Choose appropriate models based on the problem type (e.g., regression, classification, clustering). - Training and Testing: Split data into training and testing sets to build and validate models. - Model Evaluation: Use metrics like accuracy, precision, recall, F1 score, and AUC-ROC to evaluate model performance. - Hyperparameter Tuning: Optimize model parameters to improve performance.

Interpreting results is a crucial aspect of the data analysis process. It involves translating technical findings into actionable insights that can be understood by stakeholders with varying levels of data analytics expertise.

Data results are like seeing a bunch of rocks on the ground. Interpreting them is like figuring out what those rocks mean. Are they scattered from a dropped bag, or just random? Knowing the why behind the data lets you use it for something cool, like building a stone path!

Practicality. Complex analytics need to be presented so that they demonstrate how they are practical/actionable to business… Save the minutia for when it’s requested. Get to the point - how is this information relevant, impactful, and actionable. In other words, when presenting to a higher-up/CEO, your priority is time and purpose. Let them know what you (or another department) can do with this data to best improve the business.

Interpret results by translating technical findings into actionable insights for non-technical stakeholders. Focus on the story behind the numbers and the real-world implications of your findings. This step is crucial for turning complex data into strategic decisions.

Every good insight should be turned into action. If your action + insight doesn't produce a follow-up point of interest you have to question the original ask. Iteration (and being agile) is a critical skill. Your problem domain is dynamic, and so you should your approach.

Continuously cycling through the data analysis process allows you to discover more precise insights and improve your methods. Embracing this iterative approach means constantly asking new questions and refining your analysis, which leads to better, more reliable results. This mindset helps ensure that your contributions remain relevant and valuable to your organization.

By repeatedly refining your approach and testing new hypotheses, you can uncover deeper insights and adapt to evolving data patterns, turning complexity into clarity.

Iterate often. Data analytics is an iterative process. Revisit earlier steps, refine objectives, and explore further as new questions arise. Regular iterations ensure continuous improvement in your problem-solving approach, keeping insights fresh and relevant.

Iterations will provide a fresh perspective on already "solved" problems. Very often, a business analysis provides insights into a business decision that might change with time and historical context. Iterating on your analysis and interpretation of results will help you see insights and outputs differently.

Small bites and iterate often... Do not attempt to tackle a huge amount in one step. Begin with key initial insights that can support continued analysis and further insight. Share these insights with the sponsors of the work you are undertaking, to both prove there is value coming from your work and also to achieve their mental investment in the process. If your sponsor feels valued by you and involved in the process, the full body of work will run so much more smoothly, even in the event of unexpected challenges. Adapting and refining approaches in light of feedback, as the project matures, will be a sure sign you are doing this well.

Actually I feel that the existing consumers voice is the best for analysing or data inputting. Capture the pain points of the existing usage , compare with the competitor , understand the difference between the challenger and the competitor and pitch in the points what the consumer needs and match with our products offering would be the best way to interpret data .

Consider the broader aspects that can enhance your approach. Foster a collaborative environment by engaging with cross-functional teams to gain diverse perspectives and insights. Stay updated on the latest advancements in data analytics and AI technologies to continually refine your methodologies. Emphasize the importance of data ethics and privacy, ensuring compliance with relevant regulations. Invest in continuous learning and skill development for your team to keep pace with the evolving data landscape. By adopting a holistic approach, you can navigate and solve complex data challenges more effectively.

Continuously refine data validation rules. Conduct regular reviews and updates to the data cleansing strategy based on feedback and new findings.

Simplicity > Complexity... Even with the most complex of tasks, it is in your interest and the interest of every member of the consuming audience, to make the outputs as simple and easily digestible as possible. Understanding the #DataLiteracy of each individual, or group of the consuming audience can help you optimise their solution. This may be very different depending upon their role. i.e. An executive CXO, will not need (or want) the same amount of detail an Engineer will, even if relating to the same Data.

Documenting the process is a key element to detect cognitive biases. Document every step of the process including assumptions used, decisions made around the data and the methods used. Documenting the steps taken not only facilitates traceability, critical review of conclusions and whether there may have been any unconscious bias, a good record can help detect where objectivity may have deviated and help identify points of improvement for future analysis.

I agree with the points above including the importance of asking the right questions and leveraging analytics. But I'd like to emphasize the importance and the value of collaboration and diverse perspectives in addressing data analysis challenges. While advanced analytics tools and techniques are crucial for understanding data, we must remember that data represents people, processes, and systems. This interconnectedness calls for collaboration between data scientists, subject matter experts, and business stakeholders. By pooling diverse expertise, we can develop comprehensive solutions that address problems at their root and create lasting impact. Let us not neglect the importance of working together and seeking diverse perspectives.

As an internal auditor, I tackle complex data problems by: - Collaborating with stakeholders to ensure a comprehensive understanding of data implications - Assessing risk appetite and tolerance to align my analysis - Evaluating and improving data governance frameworks for quality and integrity - Communicating findings and recommendations clearly using data visualization and storytelling This structured approach enables me to drive business improvement, mitigate risks, and provide valuable insights that inform decision-making.

Alongside all those points, I want to add one critical skill, and that is prioritizing effective communication. When you encounter a complex problem, don't hesitate to ask clarifying questions and engage with stakeholders to fully understand their needs and expectations.  Clear communication ensures you're solving the right problem and aligns your efforts with business objectives.