Top trending Linkedin posts about data

In my journey as a Data Analyst, I have often come across the notion that one must specialize in a specific area. While specialization is indeed vital, I firmly believe that it's equally important to cultivate a broad understanding of various aspects in your field. From working on supply chain problems at a pharmaceutical company to working as a data analyst resolving data quality issues at RBC Capital Markets, I've learned that versatility is key. It's not about doing everything but having an appreciation for all facets of your work and being able to connect the dots. So if you're just starting out or contemplating a career shift into data analytics, remember - you can do it all! Don't limit yourself. Embrace learning opportunities and never shy away from challenges. A well-rounded skill set coupled with continuous learning is the pathway towards success in this dynamic field! What was your journey like into data analytics? Do you come from a pure technical background or from a business background? #dataanalytics #career #dataachievers --- P.S: I've noticed that people coming from a business background turn to succeed a lot more in data analytics than people with a pure technical background

DATA, DATA, DATA In SAP implementations, the complex end-to-end data workstreams connecting various business processes and software systems often fail to meet expectations. This shortfall can be attributed to several key factors: Inadequate Planning and Scoping: SAP projects tend to focus heavily on software configuration while neglecting data integration requirements. Critical details such as legacy system dependencies, data cleansing, and validation are often overlooked, leading to cost overruns. Underestimating Data Complexity: SAP deployments involve consolidating data from multiple sources with varying formats and quality levels. Mapping, transforming, and loading heterogeneous data is often underestimated, resulting in unforeseen issues during implementation. Integration Challenges: Integrating third-party systems like CRMs and payroll introduces complexities that project teams struggle to manage. These interconnectivity requirements are often underestimated during project planning. Testing Shortcuts: Aggressive timelines lead to shortcuts in SAP testing, with a focus on functionality rather than end-to-end data testing. Rushed data validation checks can result in errors and incomplete migrations. Weak Data Governance: Many SAP clients lack strong data governance, leading to poor data quality and inconsistencies across systems. Attempting to consolidate fragmented datasets into SAP becomes challenging. Operational Disruptions: Migration missteps can disrupt business processes, leading to conservative cutover plans. However, this can result in insufficient validation and reconciliation of migrated data. Legacy Decommissioning Delays: Legacy applications often remain in parallel due to migration challenges, increasing costs and overhead. Insufficient Training: Tight timelines leave little room for training resources new to SAP data methodologies, slowing down problem diagnosis and rework. Specialized Skill Gaps: Project teams often lack specialized skills needed for SAP data migrations, such as interface protocols and data consolidation. In summary, SAP data migration efforts fall short due to inadequate planning, underestimation of data complexity, integration challenges, testing shortcuts, weak data governance, operational disruptions, legacy decommissioning delays, insufficient training, and specialized skill gaps. Addressing these challenges is crucial for successful SAP data workstream execution and outcomes. Connect with Ricardo Rosales from Syniti for a in depth discussion about SAP Data implementations.

Have you been doing this for a while? ❌ You've been watching reels and YouTube videos on data careers for some time now. ❌ You've even downloaded some free roadmaps. ❌ You may have enrolled in some data courses. ❌ Now you're looking to understand what the different data roles entail so that you can get Clarity on what role you can pursue. ❌ You must have researched, asked for advice, and stalked Instagram influencers to know 'What is the difference between a data analyst and a data scientist?' ✅ ✅ This course will exactly answer these questions. ✅ ✅ https://lnkd.in/g-it_vMY #data #dataanalytics #datascience #ai #genai #dataanalyst

We all have lot of data but ❓ Do you have the problem Of not being able to find the data? Or let's say the right data? 🎯 📌 You have a data catalog tool or built inhouse catalog solution, but still data cannot be easily discovered by stakeholders. 📌 Data team spends most of the time helping others locate and understand the data. This is not a value add of data team capabilities. Are you experiencing similar challenges? 

Unlock the secrets of data 🧵 Checkout this SQL Beginners Guide that helps you in your career. ♻️ Repost this if you learned something new 💡 ➼ For downloadable 𝐃𝐒𝐀/𝐑𝐞𝐚𝐜𝐭𝐉𝐒/ 𝐀𝐈/ 𝐉𝐚𝐯𝐚𝐒𝐜𝐫𝐢𝐩𝐭 learning materials, please check out my previous posts ➼  Aditya Singh 📈 Do "Like" this post 👍🏻 ➼ Share Your Valuable thoughts in the "Comment" ⏬ section. P.S. Join me 🔋 and learn along with 17,000+ members every day ☣️. cc: Vikas Rajput #sqldatabase #sqlserver #sqlqueries #sqlite #sqldeveloper #sql #sqlquery #sqlskills #sqlchallenge #data #dataanalyst #database #databricks #datascience #mysql #oraclesql

Who's ready to nerd out!!! The team at the World Staffing Summit decided we needed some data So Vidur Raj and I had to oblige So join us at 5pm EST tomorrow to talk all things Data and how having a data strategy can help you grow.

𝗜𝘁’𝘀 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗯𝗶𝗴 𝘀𝗵𝗶𝗳𝘁 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝘂𝗻𝗱𝗶𝗻𝗴. As a Chief Data Officer, when you ask for funding - position data as a... *** 500+ data executives are subscribed to the 'Leading with Data' newsletter. Every Friday morning, I'll email you 1 actionable tip to accelerate the business potential of your data & make it an organizational priority. Would you like to subscribe? Click on ‘View My Blog’ right below my name at the start of this post.

The #1 thing I wish I knew when I started with Salesforce Marketing Cloud 8 years ago? Just how the data model works. 😬 --- When I started working with SFMC 8 years (or so?) ago, like many I had been working with Salesforce "Core" (the CRM, Sales Cloud, Service Cloud, force.com...). I also worked with Salesforce Pardot (now called "Salesforce Marketing Cloud Account Engagement" 🤭). However, SFMC works very differently. Here's what I wish I knew... --- 🔄 Relational Data from Core Data flowing in from Salesforce Core is relational. Understanding how this links to SFMC is key to leveraging customer info effectively. Usually, your Salesforce Core (CRM, force.com...) contains way more objects than you need Data Extensions (DEs) in SFMC. As a marketer, you may also not be familiar with SF Core "lookups" (a bit like Excel lookups). Understanding this is key. AND 🔨 Flexibility with Data Extensions SFMC allows you to create custom Data Extensions. This is your playground for structuring data exactly how you need it. This flexibilty comes at a price... many turn their environment in a mess. Take a step back once in a while to review how you think about DEs in light of campaigns and journeys. Use naming conventions and folder structure! AND 🛠️ Define in Data Designer/Contact Builder Use these tools to structure your data model. It pays off, especially for segmentation, personalization, and reporting. For instance, some Journey Builder-driven personalization only works if you have related your DEs in Data Designer. AND 📊 Flatten Tables with SQL To make your data actionable, sometimes you need to flatten it. SQL is your friend here, transforming complex data relationships into a format ready for targeted marketing. AND 🎯 Data-Driven Personalization This is very much linked to the previous point... The more precise your data, the less you rely on complexities like AMPScript. Get the data right, and even your HTML development becomes more straightforward. AND 🔍 Reporting on Data Views SFMC has hidden gems like Data Views (and also the Send Log! 💡). These 'invisible' Data Extensions are crucial for deep-dive analytics. AND 🚀 Consistent Reporting via Journey Builder Launch all sends through Journey Builder for uniform reporting. It makes tracking and comparing campaign performance a breeze. ... Getting to grips with SFMC's data model isn't just about managing data; it’s about transforming it into a powerful marketing tool. #salesforcemarketingcloud --- PS: Of course, during my journey, I also learned that many marketers struggle with SQL. And even if you know SQL (I taught myself), it's not particularly fast, given you need to create DEs manually, debugging is hard, you end up being the bottleneck for non-tech marketers,... That's why we came up with DESelect Segment a few years ago, and been rocking it since! 🚀 More info here: www.deselect.com/segment

I have been working in the data industry for over 5 years. Since starting as a Data Analyst and working my way up to a Power BI Lead, I have learned a lot about what it takes to solve some of the biggest problems people face in our industry concerning the effective use of data visualization and its power to drive decision making. In fact, they are a lot easier to solve than most people think. You just need to change how you think about them. Over the years, I have read countless books on the subject of data visualization and storytelling. One book that has stood out for me and I encourage you to give this book a read. ___________________________ "Storytelling with Data" by Cole Nussbaumer Knaflic I first read this book in 2019. And here's why you'll love it: It demystifies the concept of data visualization for beginners and experts alike, teaching you how to effectively communicate complex data through compelling stories. This book is a practical guide that enhances your skills in turning dry data into engaging narratives, making your analyses not only insightful but also memorable. Here it is, and how to solve them: Problem #1: Overwhelming Data: Learn to distil large datasets into clear, concise visuals that tell a story. Problem #2: Engaging the Audience: Use storytelling techniques to make your data presentations captivating and relatable. Problem #3: Maintaining Accuracy and Ethical Standards: The book emphasizes the importance of presenting data truthfully and responsibly. See? That wasn't so hard. ___________________________ What other books would you recommend that are tool-agnostic? Let's learn from each other. 👇🏼 Repost this with your network and teach the importance of the basics. ✅ Let's together Demystify the Tech. ✅ Keep Learning !! Keep Growing !! --------------- Hi, I'm Neil 👋🏼. I am sharing posts and resources to help you become a better #data #analytics professional and navigate the world of data confidently. 🔔 Join 81 others subscribed to my short FREE newsletter where I break all the technical jargon: https://lnkd.in/gwgc_8WR

Data science relies on working with different types of data systems and architecture. For data scientists or analysts just getting started, some of the terminology can be confusing. In this post, we’ll dive into definitions and examples for five fundamental data science concepts: - 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: A centralized repository that stores huge amounts of raw, unstructured data in native formats. Provides flexible, scalable storage for diverse analytics and ML needs. - 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝘁: A subset of company data focused on the needs of a specific team or use case. Enables access to tailored data without irrelevant info. - 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Automates the flow and transformation of data from sources to destinations. Ingests, processes, and routes data for downstream uses. - 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: Integrates data from multiple sources into one unified repository. Structured for querying, analysis, and reporting. Provides a single source of truth. - 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: The accuracy, completeness, consistency, and relevance of data. Essential for reliable analytics and decisions. - 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲: Combines data lakes and warehouses. Stores raw data like a lake along with preparing and structuring data like a warehouse. Provides flexibility for diverse data and uses. 👉 Follow Gina Acosta Gutiérrez 👩🏻‍💻 for more resources! ➡ Get the latest AI news, tools, tutorials and guides on using popular AI tools for FREE here: www.joinhorizon.ai #python #data #database #datascience #machinelearning #ai #artificialintelligence #programming #dataanalysis #analytics #coding #tech #developer #cloudcomputing #technology

On Monday, we upgraded one of our client's IaC codebases from Terraform 1.5.7 to #OpenTofu v1.6.1!!! 🎉 🥳 💥 🍜 Here are some interesting stats about this client's infrastructure + IaC Codebase: 1. In this repo, there is 39,764 lines of Terraform code across 485 `*.tf` files. 2. There are 53 root modules in the codebase. 3. There are 90 root module instances (read "state files") 4. If we add up all the resources in those state files, there is ~2400 resources under management. How many errors did we run into across all that terraform? How many changes to the actual terraform code did we need to make to accommodate tofu? NONE. ZERO. 🎉 ❤️ I'm stoked on these stats! Tofu went GA and successfully delivered on their mission: A zero-changes-required alternative to Terraform that is fully open source. That's awesome and props to the hardworking community and contributors who made it happen! 🙌 We'll have a post on this upgrade hitting the Masterpoint blog coming soon! If you're interested in doing this at your org so you're not stuck on TFC then reach out and we'd be happy to chat! #terraform #iac #infrastructure #platforms #platformengineering #tofu #infra

Excited to share this fun conversation on Data as a Service Businesses with Howard Koenig! The data as a service business model is a big opportunity for companies looking to commercialize their data, and Howard has a lot of insights from his many years leading companies in the space.