Data Management Innovations to Explore

😬 Many companies rush to adopt AI-driven solutions but fail to address the fundamental issue of data management first. Few organizations conduct proper data audits, leaving them in the dark about: 🤔 Where their data is stored (on-prem, cloud, hybrid environments, etc.). 🤔 Who owns the data (departments, vendors, or even external partners). 🤔 Which data needs to be archived or destroyed (outdated or redundant data that unnecessarily increases storage costs). 🤔 What new data should be collected to better inform decisions and create valuable AI-driven products. Ignoring these steps leads to inefficiencies, higher costs, and poor outcomes when implementing AI. Data storage isn't free, and bad or incomplete data makes AI models useless. Companies must treat data as a business-critical asset, knowing it’s the foundation for meaningful analysis and innovation. To address these gaps, companies can take the following steps: ✅ Conduct Data Audits Across Departments 💡 Create data and system audit checklists for every centralized and decentralized business unit. (Identify what data each department collects, where it’s stored, and who has access to it.) ✅ Evaluate the lifecycle of your data; what should be archived, what should be deleted, and what is still valuable? ✅ Align Data Collection with Business Goals Analyze business metrics and prioritize the questions you want answered. For example: 💡 Increase employee retention? Collect and store working condition surveys, exit interview data, and performance metrics to establish a baseline and identify trends. ✅ Build a Centralized Data Inventory and Ownership Map 💡 Use tools like data catalogs or metadata management systems to centralize your data inventory. 💡 Assign clear ownership to datasets so it’s easier to track responsibilities and prevent siloed information. ✅ Audit Tools, Systems, and Processes 💡 Review the tools and platforms your organization uses. Are they integrated? Are they redundant? 💡 Audit automation systems, CRMs, and databases to ensure they’re being used efficiently and securely. ✅ Establish Data Governance Policies 💡 Create guidelines for data collection, access, storage, and destruction. 💡 Ensure compliance with data privacy laws such as GDPR, CCPA, etc. 💡 Regularly review and update these policies as business needs and regulations evolve. ✅ Invest in Data Quality Before AI 💡 Use data cleaning tools to remove duplicates, handle missing values, and standardize formats. 💡 Test for biases in your datasets to ensure fairness when creating AI models. Businesses that understand their data can create smarter AI products, streamline operations, and ultimately drive better outcomes. Repost ♻️ #learningwithjelly #datagovernance #dataaudits #data #ai

What's AI’s Secret Weapon.. Data isn’t just a byproduct of business anymore—it’s the fuel driving AI innovation. Think about it: AI relies on data to power everything from smarter recommendations to game-changing predictions. But with unstructured data growing faster than ever, managing it has become a real challenge. That’s where Unstructured Data ETL comes in. The Data Explosion: Challenges and Opportunities By 2025, the world’s data will hit a staggering 175 zettabytes, according to IDC. Yet, only 10% of this data will be stored, and even less will be analyzed. 📊 What’s driving this growth? Enterprise data is predicted to double between 2020 and 2022, reaching 2 petabytes per organization (Seagate). Mobile and WiFi transmissions now account for over 60% of global IP data traffic (Cisco). Despite this growth, managing unstructured data—emails, PDFs, images, videos—remains a monumental challenge. Without proper tools, this untapped goldmine of information becomes a liability instead of an asset. Building Data Muscle: The Foundation for AI Innovation In a world where AI thrives on data, quality is as critical as quantity. Capital One’s approach highlights three principles to tackle data challenges: 1️⃣ Standardization: Clear rules for metadata and data governance ensure consistency. 2️⃣ Automation: Reduce manual tasks like metadata management to focus on innovation. 3️⃣ Centralization: Create modular tools that streamline data management across platforms. Without these pillars, scaling data for AI becomes unsustainable. 📌 What is Unstructured Data ETL? Unstructured Data ETL (Extract, Transform, Load) : 1️⃣ Data Sources: Pull data from PDFs, emails, presentations, or websites. 2️⃣ Extract: Automate the extraction of relevant content from these diverse formats. 3️⃣ Transform: Clean and structure the data for downstream use. 4️⃣ Load: Deliver the transformed data into databases, APIs, or BI tools. Why It Matters Traditional ETL processes were built for structured data—rows and columns neatly stored in databases. But today’s challenges demand tools that can handle the messiness of unstructured data. 🔑 Key Benefits of Unstructured Data ETL: Scalability: Process vast amounts of data with minimal human intervention. Accuracy: Improve data quality through automated cleaning and transformation. Speed: Reduce time-to-insight by delivering ready-to-use data for AI and BI tools. Looking Ahead: A Data-Driven Future Unstructured Data ETL isn’t just a tool—it’s a strategic enabler for businesses navigating the complexities of the data explosion. 💡 What’s Next? Seamless integration with AI to generate insights in real-time. Adoption of cloud-native ETL pipelines for greater flexibility and scalability. The question isn’t whether you’ll adopt Unstructured Data ETL—it’s how soon you’ll realize its potential to unlock the next wave of innovation. Let’s shape the future of data together. ♻️ Share 👍 React 💭 Comment

There's a major evolution coming in data management that I argue will reshape our entire industry-- it's Shift Left Data. Over the past few years, I've watched data contracts move from being a fringe idea in LinkedIn posts to becoming a real driver of organizational change at global enterprises (check out our Shift Left Data Conference recordings). Specifically, I'm noticing that the lines that differentiate the workflows of different teams are being rewritten and bringing stakeholders from various disciplines together in a way we haven't seen before. This is especially true among software and data teams. Yes... AI is a huge catalyst for these shifts (more attention, budget, and scrutiny), but I argue we have been moving towards this direction even before ChatGPT gained global traction. In particular, DevOps and DevSecOps teams have already gone through their "shift left" moment and found success. I firmly believe it's now the data industry's turn. I'm going to be writing more heavily on this on LinkedIn in the coming weeks but here are a few resources from myself and others in the industry that I think are a great start: 1. Shift Left Data Manifesto (https://lnkd.in/gU36qr54) 2. Glassdoor: Data Quality at Petabyte Scale: Building Trust in the Data Lifecycle (https://lnkd.in/gbEApwzD) 3. Shifting Left with Data DevOp (https://lnkd.in/g5G57f9T) 4. Wayfair’s Multi-year Data Mesh Journey (https://lnkd.in/g2YpAdXW) 5. Creating source-aligned data products in Adevinta Spain (https://lnkd.in/gjdE5Dgf) Good luck!

Embracing Modern Solutions for Big Data 👩💻 As a data engineer, I've seen how data management has evolved over the years, moving from traditional systems to modern architectures. Here's a simple breakdown of the key developments in managing today's data explosion: 1. Data Warehouse Traditional data warehouses have been the go-to for business intelligence. They’re great for structured data and reporting but have some limitations. Strengths: Fast querying, reliable for structured data, and consistent reporting. Limitations: Struggles with unstructured data, and scaling can get expensive. 2. Data Lake Data lakes emerged to handle unstructured and semi-structured data that warehouses couldn't manage well. Strengths: Stores raw data, highly scalable, and flexible. Challenges: Can turn into a "data swamp" without governance and requires strong metadata management. 3. Data Lakehouse This hybrid combines the best of data warehouses and data lakes, offering a unified solution for analytics and machine learning. Strengths: Handles multiple data workloads, better performance than lakes, supports SQL and ML. Considerations: Still a new concept, and teams might need training to adapt. 4. Data Mesh Data mesh introduces a decentralized, domain-focused approach to data. It's as much about culture as it is about technology. Strengths: Decentralized ownership, treats data as a product, and supports self-service. Challenges: Requires major organizational changes and robust governance. 🔑 Key Steps for Transitioning Assess your current setup: Identify pain points in your existing architecture. Define your goals: Align data strategies with business objectives. Understand your data: Look at the volume, variety, and sources of your data. Evaluate your team: Address skill gaps through training or hiring. Start small, scale fast: Test with pilot projects and expand based on results. Adopt hybrid solutions: Combine tools like a data lake for raw storage and a lakehouse for analytics. 💡 What’s Your Story? Have you faced unique challenges or found creative solutions while working with big data? Share your experiences below! ➖ Image Credits: Brij Kishore Pandey