Elder Research

Today is #InternationalJokeDay and we couldn’t miss sharing a joke from our very own Evan Wimpey. 😂 𝗤: How many data scientists does it take to screw in a light bulb? 𝗔: Just 1, but he needs thousands of already-screwed-in light bulbs for training. 😆 Keep the laughter going by sharing a joke below! 👇 Check out Evan’s book, “Predictable Jokes,”  at predictablejokes.com, and be sure to catch his interviews with data leaders on our Mining Your Own Business podcast.

The difference between effective analytics solutions and ones that miss the mark: The time invested in change management. 🎯 Analytics solutions work best when everyone is on board. But real change only happens when people are ready for it. Change management is a structured way to help teams plan for and adjust to change. It involves a lot of communication, getting everyone aligned, providing learning opportunities, and measuring the impact of the change. That way new analytics solutions don’t just end up collecting dust—they transform the way teams work. If you’re not sure where to get started with change management, our team is happy to chat. You can also check out the change management resources on our website. #DataDriven #PeopleCentered #ChangeManagement

Collaboration sparks innovation. 💡 Last week, our senior leadership team gathered offsite to talk through the latest wins, challenges, and goals for the future. We love investing time in meetings like these because they help keep us aligned and focused on the path ahead. ↗️ How does your team stay in sync on projects and goals? #DataDriven #PeopleCentered

Thankful for our data engineers and all the work they do to make data accessible and useable. 💪 Benjamin Huang shares what he likes about this work and his top tip for other data engineers: 𝗙𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗣𝗮𝗿𝘁 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 My favorite part of being a data engineer is how current and cutting edge my work is. I get to work with the newest tools and methods, constantly exploring and implementing the latest advancements. It’s exciting to see the impact these technologies can have and be part of shaping the future. 𝗧𝗼𝗽 𝗧𝗶𝗽 𝗳𝗼𝗿 𝗢𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 Stay curious and keep learning! Our field is always changing, so keep up with the latest and greatest. And don’t be shy about asking questions. Sometimes you just need another perspective. 𝗦𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗡𝗲𝘄 𝗬𝗼𝘂 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 I’m really excited to learn more about AI technology and what it takes to build and deploy AI models. Understanding the full lifecycle of AI—from development to deployment—is something I want to dive into. It’s an area that’s constantly evolving and has so much impact across various industries. 𝗙𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗣𝗮𝗿𝘁 𝗼𝗳 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗮𝘁 𝗘𝗹𝗱𝗲𝗿 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 The best part of working at Elder Research is the supportive environment. Everyone here is always ready to help and encourage each other. Plus, there’s an incredible culture of learning. Whether it’s new technologies, innovative methods, or professional growth, Elder Research encourages continuous learning and development, making it an amazing place to grow and thrive. 𝗙𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗛𝗼𝗯𝗯𝘆 I like to be active as much as possible. I’m currently training for a triathlon, which is a new challenge for me. Besides that, I love lifting weights and rock climbing. 𝗙𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗪𝗼𝗿𝗸 𝗦𝗻𝗮𝗰𝗸 Beef jerky! 👀 Interested in a job as a data engineer? We have several openings on our team. Check out our Jobs tab to learn more and apply: https://lnkd.in/gpPJUkTT #DataEngineer #DataEngineerJobs  

Detecting fraud starts with having a clear picture. And traditional fraud detection methods aren’t cutting it, says Data Scientist Garrett Pedersen: “We need to change the way that we are solving problems in order to stay ahead of fraud and abuse.” The typical approach: Try to spot fraud on relational tables in Excel or SQL. A better approach: Graph data. Whether you’re familiar with graph data or new to it, Garrett breaks down the basics in this video. 𝗔 𝗙𝗲𝘄 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗚𝗿𝗮𝗽𝗵 𝗗𝗮𝘁𝗮 ⛛ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Relational tables can be complex and expensive, but graph data simplifies the process of spotting connections. ⛛ 𝗖𝗹𝗲𝗮𝗿 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Graphs show how entities (nodes) are connected through relationships (edges) more clearly than relational tables, allowing for better visualization and understanding of data. ⛛ 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Graph data can reveal potential fraud scenarios that might be hidden in traditional relational tables. If you want to learn more about fraud detection methods, check out our articles at elderresearch.com/blog. #DataScience #DataAnalytics #FraudDetection

Today we celebrate a proclamation of freedom that came nearly 160 years ago. On June 19, 1865, more than 250,000 people in Texas finally had a glimpse of life beyond slavery when the Army arrived to ensure the Emancipation Proclamation was carried out. In 2021, Juneteenth became a federal holiday. The Juneteenth flag, designed by activist Ben Haith and artist Lisa Jeanne Graf, has a lot of meaning. Star: This represents Texas, the Lone Star State, where this final proclamation of freedom was made. Burst Outline: The burst around the star is inspired by a nova, representing a new beginning for African Americans. Arc: The red arc behind the star represents a new horizon of opportunities and promise. Colors: The colors tie to the American flag and stand as a reminder that slaves and their descendants were and are Americans. #Juneteenth

Hitting a wall with a data analytics project? It may be time to think differently. 💡 Nearly 30 years in, we’re still learning new ways to tackle problems. It’s about being willing to experiment and being okay with failing. Here’s how our CEO Gerhard Pilcher puts it: “Big challenges that seem impossible to meet require you to think very differently. Instead of just saying, ‘That’s impossible,’ think ‘What are the possible ways I might achieve this?’ It’s taking really hard things and trying to be innovative at how we approach them, not letting them defeat us.” Inventing is a series of failures leading to success. Onward and upward. ↗️ #DataScience #Innovation

One of the best ways to learn? Surround yourself with other learners. 💡 Our team is at the Databricks #DataAISummit in sunny San Francisco, connecting with data leaders from around the globe. The summit is covering everything from generative AI and machine learning to data strategy and governance. We love events like this because there’s always room to hone our craft and learn something new … And there’s always room for a little bit of baseball in the downtime. ⚾ Stay curious and have fun while you’re at it! #DataScience #MachineLearning

Can AI help fight food insecurity? That’s what researchers at the Georgia Institute of Technology are exploring right now. 𝗧𝗵𝗲 𝗳𝗼𝗰𝘂𝘀: helping policymakers and community organizations take faster and more focused actions to address hunger in Africa. 𝗪𝗵𝗼’𝘀 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝗼𝗻 𝗶𝘁? Ioanna Maria Spyrou, a Ph.D. candidate in Georgia Tech’s School of Economics, and her advisor, Professor Shatakshee Dhongde. The reality is that about 150 million people in sub-Saharan Africa are facing hunger. Much of it related to armed conflicts, droughts, natural disasters, and other challenges. 𝗛𝗼𝘄 𝘁𝗵𝗲 𝘁𝗼𝗼𝗹 𝘄𝗼𝗿𝗸𝘀: Spyrou and Dhongde’s approach uses recent data and additional factors, such as conflict data and weather patterns, to predict food insecurity. “By identifying which factors contribute most to food insecurity in different regions, we can adapt agricultural systems, try new strategies, and build stronger social networks and support systems,” says Spyrou. 𝗥𝗲𝗮𝗱 𝗺𝗼𝗿𝗲 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗮𝗿𝘁𝗶𝗰𝗹𝗲: https://lnkd.in/eN5kFWDb We’re wishing the Georgia Tech team success in their efforts. What are some innovative applications of AI that you’ve heard about recently? #DataScience #AIforGood #DataforGood #AI #MachineLearning

You’re in a canoe when you hear a noise. You quickly discover the tent you thought was secured is now floating down river. 🌊 ⛺ Fun times. While you may not have had that exact experience, you probably know what it feels like to be up a creek without a paddle. 🛶 Maybe it’s a project that’s come to a sudden halt. Or maybe you wish you had a few extra helping hands. Michael Fowler Ph.D. Fowler has been there—both in the great outdoors and in the data world. And the problem-solving skills he’s used in the wild have also been essential in his career. 💡 𝗛𝗲 𝘄𝗿𝗶𝘁𝗲𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗼𝘀𝗲 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗯𝗹𝗼𝗴: https://lnkd.in/egsxCG8Q “I learned from an early age that spending a single day to multiple weeks in the wild requires a systematic approach to preparation, teamwork to execute, and the ability to ‘work the problem and not have the problem work you’ through innovation and creativity,” says Michael.   “This is also how I’ve come to approach R&D in the ever-changing world of artificial intelligence and machine learning.”   𝟯 𝗧𝗶𝗽𝘀 𝗳𝗼𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴   1️⃣ Be prepared but stay flexible. 2️⃣ Work closely with your team and share ideas freely. 3️⃣ Treat failures as valuable learning opportunities.   As Michael puts it, “Accomplishment in the face of adversity is the fuel that drives a researcher to push the boundaries of innovation despite all the obstacles and failures they experience.”   Keep paddling. 🚣   𝗣.𝗦. This Saturday is National Get Outdoors Day. Hope you get an opportunity to relax and enjoy the great outdoors. You never know what bright ideas may come from that. #DataScience #ResearchandDevelopment #Innovation