DQC

𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐋𝐚𝐝𝐝𝐞𝐫 𝐭𝐨𝐰𝐚𝐫𝐝 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐕𝐚𝐥𝐮𝐞 Companies have high ambitions for their data, but often their data quality doesn't support business value creation. Here are common stages we see: 0️⃣ 𝐒𝐭𝐚𝐠𝐞 0 - 𝐔𝐧𝐝𝐞𝐟𝐢𝐧𝐞𝐝 😕 Activities: Generic complaints and anecdotes about bad data. Value Realized: 0% 1️⃣ 𝐒𝐭𝐚𝐠𝐞 1 - 𝐌𝐞𝐚𝐬𝐮𝐫𝐞 & 𝐅𝐢𝐧𝐝 𝐈𝐬𝐬𝐮𝐞𝐬 🔍 Activities: Creating transparency with data quality dashboards—first manually, then in real-time. Value Realized: 5% 2️⃣ 𝐒𝐭𝐚𝐠𝐞 2 - 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐈𝐬𝐬𝐮𝐞𝐬 𝐢𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 🛠️ Activities: Irregular data improvement projects handled by junior team members using tools like Excel. Value Realized: 10% 3️⃣ 𝐒𝐭𝐚𝐠𝐞 3 - 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐈𝐬𝐬𝐮𝐞𝐬 🖥️ Activities: Implementing observability, lineage, and documentation for company data and pipelines. Value Realized: 20% 4️⃣ 𝐒𝐭𝐚𝐠𝐞 4 - 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐄𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 🔄 Activities: Integrating data improvement into existing processes at the source using familiar tools. Value Realized: 80% 5️⃣ 𝐒𝐭𝐚𝐠𝐞 5 - 𝐏𝐫𝐞𝐯𝐞𝐧𝐭 𝐈𝐬𝐬𝐮𝐞𝐬 𝐢𝐧 𝐃𝐚𝐲-𝐭𝐨-𝐃𝐚𝐲 𝐖𝐨𝐫𝐤 🛡️ Activities: Real-time data validation directly in source systems via SDKs or API calls. Value Realized: 100% Stage 4 and Stage 5 are not easy to achieve but worthwhile. The real transformation happens when we shift from merely finding data issues to fixing and preventing them at the source. That's where the substantial business value is unlocked - and what we at DQC focus on! 💡 𝘚𝘰, 𝘸𝘩𝘢𝘵'𝘴 𝘺𝘰𝘶𝘳 𝘯𝘦𝘹𝘵 𝘮𝘰𝘷𝘦 𝘵𝘰 𝘭𝘦𝘷𝘦𝘭 𝘶𝘱 𝘺𝘰𝘶𝘳 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺?

Thomas, Johannes and I are looking for a 𝐅𝐨𝐮𝐧𝐝𝐞𝐫’𝐬 𝐀𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐞 who is excited about taking DQC to the next level with us! 🚀 Our mission: Help companies so that they can count on their data. You will team up directly with me. Together, we will evolve our 𝐆𝐨-𝐭𝐨-𝐌𝐚𝐫𝐤𝐞𝐭 and work on the most important 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 topics of a young and fast-growing startup. 📈 Besides contributing in a dynamic environment, you can expect to have deep discussions around the perfect espresso, SaaS sales, Italian cuisine and Mafia history, early-stage startups, best pastry places in Munich, sports… and any other topic that you are passionate about. We love to learn from each other and grow together. ☕ If you want to make an impact, we’d be excited to get to know you. ✌🏻 Applications under: 𝘳𝘦𝘤𝘳𝘶𝘪𝘵𝘪𝘯𝘨 𝘢𝘵 𝘥𝘲𝘤 𝘥𝘰𝘵 𝘢𝘪

𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐜𝐡𝐞𝐜𝐤𝐬… 𝐘𝐞𝐬, 𝐰𝐞 𝐜𝐚𝐧! 𝐁𝐮𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞? 🤔 Traditionally, data quality was a collaborative effort: domain experts outlined what a “relevant” data check should look like, and data engineers turned those requirements into SQL/Python scripts or system configurations. Now, with advances in technology, we 𝘤𝘢𝘯 auto-generate data checks. But the question remains: 𝘴𝘩𝘰𝘶𝘭𝘥 we? It depends…🔍 Many automated checks can lead to issue overload and alert fatigue, especially if they target data that’s rarely used or low-value. This is where data quality automation becomes counterproductive. 𝐁𝐞𝐟𝐨𝐫𝐞 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚 𝐝𝐚𝐭𝐚 𝐜𝐡𝐞𝐜𝐤, 𝐚𝐬𝐤 𝐲𝐨𝐮𝐫𝐬𝐞𝐥𝐟: 📊 𝘞𝘩𝘢𝘵 𝘥𝘢𝘵𝘢 𝘪𝘴 𝘨𝘦𝘯𝘶𝘪𝘯𝘦𝘭𝘺 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴-𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵? 🎯 𝘞𝘩𝘦𝘯 𝘪𝘴 𝘵𝘩𝘦 𝘥𝘢𝘵𝘢 𝘧𝘪𝘵-𝘧𝘰𝘳-𝘱𝘶𝘳𝘱𝘰𝘴𝘦? Including these considerations in the automation of DQ checks isn’t trivial. At DQC, we blend fine-tuned GenAI models, Machine Learning and statistics to automate data checks 𝐏𝐋𝐔𝐒 human feedback to generate 𝘉𝘶𝘴𝘪𝘯𝘦𝘴𝘴-𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘤𝘩𝘦𝘤𝘬𝘴. However, no tech is a silver bullet to solve all data issues. Automated checks don’t replace people—they empower them. Collaboration and control are still essential. But with smart tech, we can make the data quality process and work 𝘮𝘰𝘳𝘦 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵, 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘧𝘶𝘭, 𝘢𝘯𝘥 𝘦𝘷𝘦𝘯 𝘦𝘯𝘫𝘰𝘺𝘢𝘣𝘭𝘦. 😊 Let's chat about how you can harness the power of 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘵 𝘥𝘢𝘵𝘢 𝘲𝘶𝘢𝘭𝘪𝘵𝘺 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 for your business—by combining artificial with human intelligence. Reach out!

𝐈𝐬 𝐲𝐨𝐮𝐫 𝐜𝐨𝐦𝐩𝐚𝐧𝐲 𝐛𝐞𝐭𝐭𝐞𝐫 𝐚𝐭 𝐞𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐭𝐡𝐚𝐧 𝐆𝐨𝐨𝐠𝐥𝐞? According to Gartner, 30% 𝘰𝘧 𝘎𝘦𝘯𝘈𝘐 𝘱𝘳𝘰𝘫𝘦𝘤𝘵𝘴 𝘸𝘪𝘭𝘭 𝘣𝘦 𝘢𝘣𝘢𝘯𝘥𝘰𝘯𝘦𝘥 by end of 2025. The #1 reason for failing GenAI projects? Poor data quality. If the AI is trained on bad data, it generates bad results. Example: 𝘎𝘰𝘰𝘨𝘭𝘦 𝘈𝘐 𝘴𝘶𝘨𝘨𝘦𝘴𝘵𝘦𝘥 𝘨𝘭𝘶𝘦 𝘵𝘰 𝘬𝘦𝘦𝘱 𝘤𝘩𝘦𝘦𝘴𝘦 𝘧𝘳𝘰𝘮 𝘴𝘭𝘪𝘥𝘪𝘯𝘨 𝘰𝘧𝘧 𝘢 𝘱𝘪𝘻𝘻𝘢. Because the training data for the AI included a comment from a joking Reddit user. While the internet had a good laugh (and hopefully no one got hurt), companies face the same challenge when they work with unreliable master and transactional data. Impact: ⚠ Reputational damage 💔 Loss of customer trust 📉 Financial losses (revenue, costs) So how can companies intelligently ensure their data quality is fit for purpose? The smart data quality solution by DQC addresses this by combining advanced artificial intelligence with human domain expertise. Reach out if you want to discuss the smart way of ensuring data quality.

With DQC you can have it both ways: Fix your data 𝘢𝘯𝘥 do something with GenAI. 🤗 Thanks Johannes Boyne for sharing.

Working with Andreas Kanz and team! Join uns and solve data quality!

In the new issue of #IDW_LIFE, we have published a case study together with CURACON GmbH: How can auditors use the DQC Excel Add-in “DQ Assistant” to ensure data quality in daily practice and also save time? The article is also available here: https://lnkd.in/eHVfCS9V - enjoy reading and please reach out in case of interest 😊   Many thanks to Alexandra Gabriel from CURACON GmbH for co-authoring the case study!

We are #hiring! If you are looking for a change, or if you know someone who might be a great addition to our team: reach out!