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This overlooked insight from dbt Labs got me emotional 🫠 The 2024 State of Analytics Engineering report is a goldmine of insights. Data practitioners spending 55% of their time organizing data sets? This fact alone is worth a series of posts! But one statistic, tucked away at the bottom, hit me right in the heart. The survey posed a fundamental question to data teams: how do they define success, or as dbt Labs eloquently puts it: what does "good" look like for data teams? With 42%, nearly half of all respondents highlighted ‘Enablement of other teams' as their primary measure of success! In my countless interviews with data team leaders, a recurring theme echoes. We want business analysts to embrace BI tools like Tableau and Looker, flex those SQL and self-serve muscles, and build their dashboards. We want our business users to think of the data team as a partner and enabler, helping them access the data they need, not as an obstacle or gatekeeper to be worked around.  In other words, free analysts from the “philosophy” of data and focus more on performing the actual analyses. The thing is… There are only so many analytics engineers and hours in a day to keep data models up to date and consistent. Striking a balance means giving autonomy to analysts while ensuring a level of control and visibility for the central data teams. Perhaps the most crucial principle in modern analytics is recognizing that self-service and governance aren't mortal enemies -- governance is what makes self-serve possible 🖐️🎤 This humble 42% statistic underscores the beauty inherent in data teams, reminding me why I founded Euno in the first place. Every action undertaken by data teams serves a singular objective: enabling everyone to ask data-driven questions and make informed decisions. When data teams reach this delicate balance of fostering creative freedom while upholding data reliability and nurturing a data culture that encourages collaboration between business analysts and data teams, that's true success. And for data teams, this clarity couldn't be more apparent 💜 *** What are your thoughts on this report finding? Would you define success differently? Full report in the first comment!

Day 3: Diving Deeper into Data: Types, Importance, and the Role of Data Analytics 📊 Hello LinkedIn community! Continuing our exploration of Data Analytics, today I want to delve into the types of data, why data is crucial, and the role of data analytics in transforming this data into actionable insights. Let's get started! Types of Data 1. Structured Data: - Organized in a fixed format. Examples: Databases, spreadsheets. > Think of a customer database with names, addresses, and purchase history. 2. Unstructured Data: - Not organized in a predefined manner. Examples: Emails, social media posts, videos. > Imagine analyzing customer feedback from various social media platforms. 3. Semi-Structured Data: - A mix of both structured and unstructured data. Examples: XML and JSON files. > Consider the metadata and tags within an email or social media post. Why is Data Important? - Data drives decision-making in today’s world. It helps businesses: - Understand customer behavior. - Optimize operations. - Uncover new opportunities. Data analytics transforms raw data into meaningful insights that can guide strategic decisions, enhancing efficiency and effectiveness in various business processes. The Role of Data Analytics -Data analytics is the process of examining datasets to draw conclusions about the information they contain. Here’s an overview of the data analytics process: 1. Problem Statement: Define the problem and objective of analysis. 2. Data Collection: Gathering data from various sources. 3. Data Cleaning: Removing errors and inconsistencies. 4. Data Exploration: Understanding data through summary statistics and visualizations. 5. Data Analysis: Applying statistical methods and algorithms to extract insights. 6. Data Interpretation/Visualization: Making sense of the results and forming actionable recommendations. Challenges in Data Analytics 1. Data Quality: Ensuring data is accurate and consistent. 2. Data Privacy: Protecting sensitive information. 3. Scalability: Managing and processing large datasets efficiently. 4. Interpretation: Accurately interpreting results to make informed decisions. By leveraging data analytics, the company can make informed decisions, leading to improved customer experiences and better business outcomes. Questions for You: 1. What aspects of data analytics are you most interested in? 2. What challenges have you faced in working with data? 3. Any specific topics you’d like me to cover in future posts? Thank you for reading! Stay tuned for more insights as we continue this journey into data analytics. #DataAnalytics #DataScience #Excel #Python #SQL #LearningJourney #CareerGrowth

I want to highlight this: "They (stakeholders/users) aren't really problems to begin with, but rather symptoms of a much deeper issue." Most of the time, we (and our stakeholders, of course) don't know exactly what the problem is. We all have limitations in understanding our problems, which is why brainstorming is needed at the beginning of the project. It is also fine if brainstorming the problem scopes takes up almost 80% of our time. Why? If we identify the exact and true cause of the problem, we can achieve what we want without drama that makes us work more

A Beginners Guide to Data Analysis In our increasingly data-driven world, the ability to extract meaningful insights from raw data has become a highly sought-after skill. Data analysis is the process of inspecting, cleansing, transforming, and modeling data to uncover patterns, trends, and relationships that can inform decision-making and propel business growth. At its core, data analysis involves leveraging various statistical and computational techniques to make sense of complex datasets. It's a powerful tool that enables organizations to gain a deeper understanding of their customers, operations, and markets, ultimately leading to more informed and data-driven decisions that drive success. However, data analysis isn't solely reserved for big corporations or tech giants. Whether you're a small business owner, a marketer, a researcher, or simply someone who wants to make better sense of the world around you, data analysis can provide invaluable insights that can shape your understanding and decision-making processes. Here are a few compelling reasons why data analysis has become an indispensable part of today's landscape: 1.Gain a Competitive Edge: By embracing data analysis, businesses can identify market opportunities, optimize products and services, and stay ahead of their competitors in an ever-evolving market. 2.Make Data-Driven Decisions: Rather than relying on gut instincts or anecdotal evidence, data analysis empowers you to base your decisions on solid, quantifiable data, ensuring a more reliable and informed approach. 3.Improve Efficiency and Productivity: By analyzing operational data, organizations can streamline processes, reduce waste, and maximize resource utilization, ultimately enhancing overall efficiency and productivity. 4.Enhance Customer Experience: By understanding customer behavior and preferences through data analysis, companies can deliver personalized experiences and improve customer satisfaction, fostering loyalty and driving business growth. As you embark on your data analysis journey, you'll explore various techniques such as descriptive statistics, exploratory data analysis, data visualization, and predictive modeling. Armed with the right tools and skills, you'll be able to transform raw data into actionable insights that drive business growth and informed decision-making processes. So, whether you're a data enthusiast, a business professional, or someone who simply wants to harness the power of data, get ready to unlock a world of insights through the fascinating realm of data analysis. Embrace the opportunity to gain a deeper understanding of the world around you and make data-driven decisions that propel you toward success.

There are several gems in the post. One thing I relate to most is that, before trying to get to the root cause of a problem, first need to ask the correct question.

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There are many challenges that the Data Analyst and Data Scientist face Here are the common challenges and tips for overcoming them based on real life experience: 1) 𝐃𝐢𝐫𝐭𝐲 𝐃𝐚𝐭𝐚: Not all data is clean and ready for analysis. Datasets often come with missing values, duplicates, or incorrect formatting. It’s essential to spend time cleaning your data, as the quality of results directly depends on it. Building robust ETL processes to handle and clean messy datasets is crucial for consistency and accuracy. 2) 𝐔𝐧𝐜𝐥𝐞𝐚𝐫 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬: Often, Business stakeholders may not have a clear understanding of what they want from data analysis. This can lead to misaligned goals and wasted effort. To avoid this, always clarify the problem at the beginning. Spend time with stakeholders, ask detailed questions, and work together to define measurable objectives. 3) 𝐃𝐞𝐚𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐋𝐚𝐫𝐠𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬: Big data can be overwhelming, and inefficient processes can make analysis slow. Optimize your data pipeline and utilize cloud platforms like AWS or Google Cloud for faster processing. Utilize data partitioning and tools such as Spark to handle big data efficiently. 4) 𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐌𝐨𝐝𝐞𝐥: Start with simple models than the complex ones. Focus on the business problem and choose models that offer simplicity, interpretability, and accuracy. A more interpretable model can often provide deeper insights, even if it's not the most sophisticated one 5) 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐢𝐧𝐠 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐭𝐨 𝐍𝐨𝐧-𝐓𝐞𝐜𝐡𝐢𝐞𝐬: Non-Tech Stakeholders don't understand complex algorithms or statistical methods, it’s our job to translate technical findings into business language. Use data visualizations to break down your results and explain how they impact the business. The clearer the insights, the bigger the impact! 6) 𝐃𝐚𝐭𝐚 𝐒𝐢𝐥𝐨𝐬: If your data is scattered across different departments. Advocate for centralized data storage breaking down silos ensures a smoother analysis and a data-driven culture and bridge gaps between departments! 7) 𝐌𝐨𝐝𝐞𝐥 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: Building a model is great, but getting it into production can be a headache. Collaborate with IT and business units early on to ensure your model integrates smoothly into real-world workflows. ➡️ Follow for daily tips and insights! #DataScience #DataChallenges #MachineLearning #DataAnalytics #DataAnalyst #DataVisualization #DataScience #LearningJourney #Careertips #Insights

Building a great data product is hard.   As data engineers, we’re often behind the scenes, providing the foundation for data science, analytics, and strategic initiatives. We design the data pipelines and warehouses that fuel decision-making and enable innovation across the business. While this role might not always be the most visible or glamorous, the reality is that we are the custodians of one of the most valuable assets—information!   But what makes the process of building a great data product so difficult? How can we ensure our work truly delivers value? Here are some key principles I've found to be essential for success: ✅Understand Your Audience: Know who you’re building for. Are your users internal teams, external clients, or a mix? Are they technical or non-technical? For example, you might build an incredibly accurate machine learning model, but if no one understands the output or how to apply it, the product becomes useless. It's crucial to Identify your 'power users' and understand how they’ll interact with your product differently than standard users.   ✅Deliver Timely, Accessible, and High-Quality Information: Once you know your audience, focus on making your product as user-friendly as possible. Users should be able to get the information they need without friction. Clarity and actionability are key—present the most valuable and actionable insights first. It can be tempting to include every possible data point, but too much information can lead to clutter and overwhelm. More importantly, accuracy is non-negotiable. A sleek, visually impressive product won’t succeed if the data itself can’t be trusted.   ✅Sustain the Data Product: Gaining user trust is only the beginning. Once your product has traction, the next step is education and advocacy. Business users often don’t realize the full potential of a well-built data product. Take this opportunity to demonstrate how it can drive smarter decisions and create new efficiencies. This kind of education can create a virtuous cycle, leading to broader adoption and possibly additional investment to further develop and refine the product. Sustaining trust is equally important—continuity, accuracy, and adaptability are critical. The data product must run smoothly, stay current, and be flexible enough to evolve with the business. Easier said than done, but the reward is enormous when you get it right.   Executing these steps is no small task, but it’s what makes data engineering so exciting and transformative. Each piece—understanding the users, delivering the right insights, and maintaining trust—brings you closer to a data product that not only functions but drives real, measurable business impact. Forget one piece, and it all begins to fall apart. But when you get it right, you have something that can fundamentally change how a business operates and thinks. #DataEngineering #DataStrategy

Data Analyst vs. Data Science: What's the Difference? Data Analysts interpret and analyze data to help businesses make informed decisions. They focus on cleaning and organizing data. Data Scientists, on the other hand, use advanced algorithms and predictive modeling to extract insights, often handling big data and machine learning for a broader understanding, forecasting, and complex problem-solving. #dataanalyst #dataanalystcertification #dataanalysttraining

Think you know everything about Data Analytics? It’s time to rethink! Let’s uncover and debunk the most common myths surrounding data analytics. ⛌ 𝗠𝘆𝘁𝗵 𝟭: 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗶𝘀 𝗧𝗲𝗱𝗶𝗼𝘂𝘀 𝗮𝗻𝗱 𝗧𝗶𝗺𝗲-𝗖𝗼𝗻𝘀𝘂𝗺𝗶𝗻𝗴 This was true in the past when manual data processing was cumbersome. Modern tools and automated processes have significantly reduced the time required. ⛌ 𝗠𝘆𝘁𝗵 𝟮: 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗛𝘂𝗴𝗲 𝗩𝗼𝗹𝘂𝗺𝗲𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮 While big data handles large amounts, analysis can also be effective on micro levels. The quality of questions asked about data sets is more important than the quantity of data. ⛌ 𝗠𝘆𝘁𝗵 𝟯: 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝘀 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 This myth is often fueled by fictional narratives like those in movies. In reality, data analytics cannot predict the future with certainty due to the many variables and unpredictable human behavior. ⛌ 𝗠𝘆𝘁𝗵 𝟰: 𝗢𝗻𝗹𝘆 𝗕𝗶𝗴 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗡𝗲𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 This is not true. Small and medium-sized enterprises (SMEs) can also benefit from data analytics, especially with the availability of cost-effective solutions like self-service business intelligence (SSBI). ⛌ 𝗠𝘆𝘁𝗵 𝟱: 𝗔𝗹𝗹 𝗗𝗮𝘁𝗮 𝗥𝗼𝗹𝗲𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗦𝗮𝗺𝗲 Data analysts, data engineers, and data scientists have different roles and responsibilities. While they work under the big data umbrella, their tasks involve distinct processes, such as building scalable data pipelines and transforming raw data into meaningful insights. By debunking these myths, we can better understand the true potential and applications of data analytics in various business settings. Everything is up for debate, let’s discuss the facts #DataAnalytics #MythBusting #BigData #DataScience #DataDriven #BusinessIntelligence #DataManagement #Analytics #SmallBusiness #SME #DataInsights #FutureOfWork