Datameer’s Post

A Non-Tech Employee who neglects Data Analytics risks falling behind. With Data Democratization, each department will have access to data, which they would analyze to drive decisions. Does that mean there would be no need for data analysts in the future? Absolutely not. You will always need Data Analysts for more advanced analytics. #businessintelligence #datademocratization #dataanalytics #analytics

When we talk to data analysts, a common concern arises - the struggle of duplicating queries in different data explorations. If you don't want to spend time doing the same transformations over and over, now you can use Models in Latitude to reuse the same logic across various projects. Catch the complete video here → https://lnkd.in/e4VjuAvG Tag someone in the comments who might find this valuable! #dataanalyst #data #reporting

Here are five ways poor data quality affects businesses. The cost of poor #dataquality could be as high $15 million per year to an average organization. Most organizations don't even know how much poor data costs them because they have never even thought about their data quality. The impact of poor data quality can be unseen in terms of lost revenue, lost opportunities for efficiencies, or lost opportunities for growth and diversification. Next step in your journey as a data analyst or as a data driven organization will be to think about the quality of your data. I will be writing about data quality over the next week. Would love to hear about your experience with data quality. #datamanagement #data Link: https://lnkd.in/gtnWVH4f https://lnkd.in/gXWy5KsH

Data cleaning is the cornerstone of robust data analysis, yet its significance often goes unnoticed. ✨ Simply put, it's the process of identifying and rectifying errors, inconsistencies, and inaccuracies in datasets. 🛠️ From removing duplicate entries to handling missing values, data cleaning ensures that our analyses are based on reliable, high-quality data. It's akin to tidying up before a crucial meeting; it sets the stage for informed decision-making and actionable insights. 💡 As a data analyst, mastering the art of data cleaning is non-negotiable. It's not just about scrubbing data; it's about uncovering hidden gems amidst the noise. 💎 #DataCleaning #DataAnalysis #DataQuality #DataManagement #AnalyticsInsights #DataPreparation #DataScrubbing #DataAccuracy #DataIntegrity #BusinessIntelligence #DataScience #BigData

The role of a Data Analyst is crucial in driving informed business decisions. #DataAnalysts play a key role in transforming raw data into actionable insights through data reporting, cleaning, and modeling, ensuring accuracy and reliability. #DataAnalytics #BusinessIntelligence #DataDriven #DataAnalyst #datavisualization #bigdata

If I were to give just one piece of advice to all data analysts, it would be this: 𝑭𝒐𝒄𝒖𝒔 𝒐𝒏 𝒖𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅𝒊𝒏𝒈 𝒕𝒉𝒆 '𝒘𝒉𝒚' 𝒃𝒆𝒉𝒊𝒏𝒅 𝒕𝒉𝒆 𝒅𝒂𝒕𝒂, 𝒏𝒐𝒕 𝒋𝒖𝒔𝒕 𝒕𝒉𝒆 '𝒘𝒉𝒂𝒕.' While it's important to be skilled in data tools and techniques, the true value of a data analyst lies in their ability to uncover meaningful insights and provide actionable recommendations. To do this effectively, you must dig deeper and strive to understand the underlying reasons and context behind the data trends and patterns you observe. This requires collaboration with domain experts, asking the right questions, and constantly seeking to connect data findings to the larger business goals and objectives. By understanding the 'why,' you can deliver more valuable and relevant insights that drive positive impact for your organization. #Copiedpost

Data analyst? He who transforms a disorganized dataset into a clean, digestible format so that informations and insights can be maximized for business decision-making #data #dataanalyst #datastorytelling

Just released my latest blog post on the role of Analytics Engineer. For anyone interested in working in the data field, this is a very cool path to consider! #analytics #engineering #data https://lnkd.in/eADkePmq

** Let's clarify what business analytics and data analytics mean in terms of skills and what you need to know+t I often hear the two used interchangeably when they are not the same. They certainly have some overlap with manipulating and extracting insights form data, but there are fundamental differences. 1. Overall goal 2. Core skillsets (e.g. data analytics involves more hands-on development skills, business analytics is more analysis driven to support recommendations to the client to support business outcomes) for each capability that require may more attention. 3. Types of roles and responsibilities for these 2 capabilities would vary (e.g data analytics: data engineer, business analytics: data strategist) 4. How data is used #businessanalyst #dataanalytics