Nature’s calling! 🏕️ In this blog, Michael Fowler Ph.D. talks about triumphs … and trials 😅 he’s had in the great outdoors and what it’s taught him about data science and innovation: “It’s the forgetting to pack your raincoat because the weather looks so nice but ends up pouring hours later; forgetting to bring the sauce for spaghetti night while hiking the Appalachian Trail; or creating a shelter from natural materials because someone didn’t secure the tent in the canoe well, and you helplessly watched it float down river when your canoe flipped on a rock,” shares Michael. “These stories are passed on as fun anecdotes but also as important lessons learned for the next adventurer, a culture often missing from the realm of research.” Read Michael’s blog: https://lnkd.in/egsxCG8Q P.S. Hope your weekend includes plenty of fresh air and sunshine! 🌤️ #DataScience #ResearchandDevelopment #Innovation
Elder Research
Data Infrastructure and Analytics
Charlottesville, VA 5,686 followers
Data Driven. People Centered.
About us
Elder Research is a recognized leader in data science, machine learning, and artificial intelligence consulting. Founded in 1995 by Dr. John Elder, Elder Research has helped government agencies and Fortune Global 500® companies solve real-world problems in diverse industry segments. Our goal is to transform data, domain knowledge, and algorithmic innovations into world-class analytic solutions. When we combine the business domain expertise of our clients with our deep understanding of advanced analytics, we create a team that can extract actionable value from the data. Our areas of expertise include data science, text mining, data visualization, scientific software engineering, and technical teaching. Experience with diverse projects and algorithms, advanced validation techniques, and innovative model combination methods (ensembles) enables Elder Research to maximize project success for a continued return on analytics investment. In 2020 we acquired the Institute for Statistics Education at Statistics.com to provide focused data science, analytics, and statistics training for corporations and individuals. The Institute’s certificates and degrees are certified by the State Council of Higher Education for Virginia, and its courses are approved by the American Council on Education. Elder Research’s Analytics Services are designed to scale based on the unique requirements of each organization and can maximize the client’s return on analytic investment. Elder Research is also a leader in advanced analytic training and offers a variety of training services directed at each of the key stakeholders within an organization. Training builds a common foundation and vision for analytics across business units and lead to the successful adoption, deployment, and maintenance of analytic models within an organization.
- Website
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https://www.elderresearch.com/
External link for Elder Research
- Industry
- Data Infrastructure and Analytics
- Company size
- 51-200 employees
- Headquarters
- Charlottesville, VA
- Type
- Privately Held
- Founded
- 1995
- Specialties
- Model construction, text mining, predictive analytics, sentiment analysis, data science, analytics training, outcome-based modeling, fraud detection, cross-selling/up-selling, customer segmentation, anomaly detection, investment modeling, threat detection, and training
Locations
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Primary
701 E Water St
Suite 103
Charlottesville, VA 22902, US
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2107 Wilson Blvd
Suite 850
Arlington, Virginia 22201, US
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1362 Mellon Road
Suite 130
Hanover, Maryland 21076, US
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14 E Peace St.
Suite 302
Raleigh, NC 27604, US
Employees at Elder Research
Updates
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We all hit roadblocks on projects, and it can be frustrating. 😩 When it comes to data projects, there are 2 common pitfalls: 1️⃣ Disconnected Data: Building the “perfect” data analytics environment without aligning it to real business challenges. 2️⃣ One-Stop Software: Searching for an “easy button” solution that promises to fix everything but often falls short. “If you’re not starting with that business problem in the beginning, you’re likely to make some bad decisions,” says our CEO Gerhard Pilcher. A good way forward: Before diving into an analytics project, evaluate the business problem you’re really trying to solve. It may take more time to do so, but it’ll save you a lot of headaches in the long run. For more tips on this topic, check out this article: https://lnkd.in/eG647H8A
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Hey Leaders, Is training a priority for your team? 💡 In this blog, Veronica Blackburn, our Director of Learning and Development, shares why building in-house AI and data science expertise is so important ... Especially with technology evolving so rapidly. ⚙ 𝗔 𝗳𝗲𝘄 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘁𝗼 𝗶𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴: ✅ Empowers employees to effectively use data and gain insight ✅ Helps employees build skills that directly impact their work ✅ Fosters a sense of ownership, loyalty, and pride in work 𝗙𝗶𝗻𝗱 𝗼𝘂𝘁 𝗺𝗼𝗿𝗲 𝗶𝗻 𝗩𝗲𝗿𝗼𝗻𝗶𝗰𝗮’𝘀 𝗯𝗹𝗼𝗴: https://lnkd.in/gQTSh-kp #DataScience #AI #Empowerment
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This photo was taken during our 20th anniversary celebration in 2015. Since that time our team has grown from about 60 team members to more than 160. And that’s not the only growth we’ve seen. We’ve witnessed team members grow sharper in their skills, grow as leaders, and grow as colleagues and friends—all while serving our clients to the best of their ability. Looking forward to what’s next and looking forward to even more talented lifelong learners joining the team! If you’re interested in joining our team or know someone who might be, check out our Jobs tab: https://lnkd.in/gpPJUkTT
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If you work with customer data, you know it comes with a lot of complexity. Find out how a leading consumer packaged goods (CPG) company partnered with us to gain better insights. 💡 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 The client was dealing with the challenge of aligning customer growth and retention goals with diverse data. They didn’t have a clear way to integrate historical customer data with future strategies, making it hard to set clear goals and optimize resources across accounts and brands. 𝗢𝘂𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 📊 Immerse ourselves in their business to understand strategic objectives and KPIs. 🧩 Apply advanced segmentation to categorize customers by performance and product objectives. 💭 Deliver insights to directly inform their strategic planning and trade strategies. 𝗙𝗶𝗻𝗱 𝗼𝘂𝘁 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗰𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝘆: https://lnkd.in/ewnGNxcV
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Work in hospitality or know someone who does? 👀 We’re excited to announce the release of our latest guide, “Hospitable Data,” designed for hospitality leaders looking to take their data strategies to the next level. 📈 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗲𝘅𝗽𝗲𝗰𝘁 𝗳𝗿𝗼𝗺 “𝗛𝗼𝘀𝗽𝗶𝘁𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮”: ☑ An analytics assessment to identify roadblocks and opportunities ☑ Real-world examples to illustrate effective data use ☑ Tips on how to grow your organization’s level of analytics maturity ☑ Helpful questions to ask ahead of data projects 👉 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/eugCN9Kc In the fast-paced world of hospitality, it’s not just about gathering data; it’s about using it to make better, faster, and more confident decisions. Explore our guide for some helpful tips and share it with your friends in the industry! #DataAnalytics #HospitalityIndustry #DataStrategy #CustomerExperience #BusinessGrowth
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Predicting failures before they happen is what predictive maintenance is all about, but it’s not always easy. In this whiteboard video, Ramon Perez, AI Solutions Portfolio Director, shares why predictive maintenance can be so hard and some ways to address those challenges. 𝗦𝗼𝗺𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: 🔎 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗟𝗮𝗯𝗲𝗹𝗲𝗱 𝗖𝗮𝘀𝗲𝘀: Trying to predict rare events with sparse data is like finding a needle in a haystack. 🏭 𝗦𝗶𝗴𝗻𝗮𝗹-𝘁𝗼-𝗡𝗼𝗶𝘀𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀: The large amounts of data generated by equipment create a lot of data noise that can be hard to sift through to pinpoint potential failures. 💭 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗶𝗻𝗴 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: It’s not just about predicting; it’s about guiding maintenance teams on where to focus their efforts. 𝗛𝗼𝘄 𝘁𝗼 𝗧𝗮𝗰𝗸𝗹𝗲 𝗧𝗵𝗲𝘀𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: 🧩 𝗥𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗙𝗮𝗶𝗹𝘂𝗿𝗲: Are there any unusual patterns in the data that can potentially be redefined as early warning signs? 🔄 𝗖𝗿𝗲𝗮𝘁𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀: Introducing human expertise into the loop helps refine models. By feeding back insights from the field, you enhance the model’s accuracy over time. ⚙️ 𝗨𝘀𝗲 𝗗𝗶𝘃𝗲𝗿𝘀𝗲 𝗠𝗼𝗱𝗲𝗹 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: There’s no one-size-fits-all model. Combining local and global models, anomaly detection, and subject-specific insights builds a robust predictive framework. 🧑💻 𝗔𝗶𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: Visual tools and summary stats help decision-makers act quickly and decisively. Ultimately, it’s about combining human expertise with machine intelligence to tackle predictive maintenance problems effectively. Interested in learning more? Check out elderresearch.com/blog.
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Fun Fourth of July fact: In July 1776, there were 2.5 million people living in the newly formed United States. 🇺🇸 In July 2023, the U.S. Census Bureau estimated there were 334.9 million people living in the nation. This holiday, we hope you have an amazing time celebrating with your favorite people. Happy Fourth of July from all of us here at Elder Research! 🎆 #FourthofJuly #4thofJuly #DataDriven #PeopleCentered
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Hey Data Leaders, Don’t let perfect data be the enemy of progress. “In our experience the mistake of ‘waiting for perfect data’ probably kills more projects than any other,” says Jeff Deal, our president and COO. Here’s a typical scenario. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝘀𝘁𝗮𝗿𝘁𝘀 𝘀𝘁𝗿𝗼𝗻𝗴: Goals defined ✅ Potential ROI calculated ✅ Project plan developed ✅ Budget approved ✅ Team assembled ✅ Project launched ✅ 𝗧𝗵𝗲𝗻 𝘁𝗵𝗲 𝘁𝗿𝗼𝘂𝗯𝗹𝗲 𝘀𝘁𝗮𝗿𝘁𝘀 ... The desire for “perfect” data creeps in. Unrealistic expectations about data preparation time and costs delay projects. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆? Even relatively clean data requires significant preparation. And of course you need engineers to parse, cleanse, and transform data into a format suitable for analytical modeling and visualization. Also, experienced data scientists expect to work with imperfect data, and they have tools and techniques to get around the most challenging data problems. The truth is no organization has perfect data. And your data doesn’t have to be perfect to deliver valuable insights. 💡 Jeff shares more in this blog: https://lnkd.in/ekpktvex #DataScience #DataAnalytics #DataPrep
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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.
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