Key Considerations for Data Lifecycle Management

Treating data as a product is a necessity these days but the main question is: How do you operationalize it without adding more tools, more silos, and more manual work? There has been some confusion and process gaps around it especially when you're working with Databricks Unity Catalog. From contract to catalog, it's important for us to treat the data journey as a single process. Here, I'd like to talk about a practical user flow that organisations should adopt to create governed, discoverable, and mature data products using UC and contract-first approach. But before I begin with the flow, it's important to make sure that: ✅ Producers clearly define what they're offering (table schema, metadata, policies); ✅ Consumers know what to expect (quality, access, usage); ✅ Governance and lifecycle management are enforced automatically. That's why to do this, I'd like to divide the architecture into 3 parts: 👉 Data Contract Layer: To define expectations and ownership; 👉 UC Service Layer: API-driven layer to enforce contracts as code; 👉 UC Layer: Acting as Data & Governance plane. ☘️ The Ideal flow: 🙋 Step 1: Producer would define the schema of the table (columns, dtypes, descriptions) including ownership, purpose and intended use. 👨💻 Step 2: Producer would add table descriptions, table tags, column-level tags (e.g., PII, sensitive) and domain ownership rules. 🏌♂️ Step 3: Behind the scenes, the API service would trigger the table creation process in the right catalog/schema. Metadata would also be registered. 🥷 Step 4: Producer would include policies like: Who can see what? Which columns require masking? What's visible for which role? etc.. 😷 Step 5: Row/column filters and masking logic would be applied to the table. ⚡ Step 6: Once the table is live, validation would kick-in that would include schema checks, contract compliance, etc. 💡 Step 7: Just-in-Time Access would ensure consumers don't get access by default. Instead, access would be granted on demand based on Attribute Based Access Control (ABAC). The process, again, would be managed by APIs and no ad-hoc grants via UI. 👍 Step 8-9: All access and permission changes would be audited and stored. As soon as the consumer requests access to the table, SELECT permission would be granted based on approvals ensuring right data usage and compliance. 🔔 Step 10-11: Upon consumer request and based on the metrics provided, a Lakehouse Monitoring would be hooked-in to the table to monitor freshness, completeness, and anomalies. Alerts would also be configured to notify consumers proactively. ☑️ Step 12: The Lakehouse monitoring dashboard attached to the table would be shared with the stakeholders. 🚀 What do you get⁉️ -A fully governed & discoverable data product. -Lifecycle polices enforced for both producer and consumer. -Decoupled producer and consumer responsibilities. -Quality monitoring observability built-in. #Databricks #UnityCatalog #DataGovernance #DataContract #DataProducts

Data quality isn't a single check, it's a lifecycle. 🔄 Most data pipelines struggle to guarantee quality because they lack end-to-end control. dlt bridges this gap by owning the entire runtime, from ingestion to staging to production. dlt ensures quality across 5 core dimensions: 1️⃣ Structural Integrity Does the data fit? dlt automatically normalizes column names and types to prevent SQL errors. For stricter control, use Schema Contracts to reject undocumented fields. 2️⃣ Semantic Validity Does it make business sense? Attach Pydantic models to your resources to enforce logic like "age > 0" or email validation in-stream. 3️⃣ Uniqueness & Relations Is the dataset consistent? Handle deduplication automatically using primary keys and merge dispositions. 4️⃣ Privacy & Governance Is the data safe? Hash PII or drop sensitive columns in-stream before they ever touch the disk. 5️⃣ Operational Health Is the pipeline reliable? Monitor volume metrics and set up alerts to catch schema drift the moment it happens. It’s time to move beyond simple "null checks" and treat data quality as a comprehensive lifecycle. Here are the docs to help you implement some of this: 📌 Alerting on Schema Changes: https://lnkd.in/d8dGX-2b 📌 Data Normalization & Type Management: https://lnkd.in/dsSr3CPf 🚀 Commercial Early Access: dltHub Data Quality Checks https://lnkd.in/dCjcug_F #DataEngineering #DataQuality #Python #dlt #DataGovernance #ETL #SchemaEvolution

Can You Trust Your Data the Way You Trust Your Best Team Member? Do you know the feeling when you walk into a meeting and rely on that colleague who always has the correct information? You trust them to steer the conversation, to answer tough questions, and to keep everyone on track. What if data could be the same way—reliable, trustworthy, always there when you need it? In business, we often talk about data being "the new oil," but let’s be honest: without proper management, it’s more like a messy garage full of random bits and pieces. It’s easy to forget how essential data trust is until something goes wrong—decisions are based on faulty numbers, reports are incomplete, and suddenly, you’re stuck cleaning up a mess. So, how do we ensure data is as trustworthy as that colleague you rely on? It starts with building a solid foundation through these nine pillars: ➤ Master Data Management (MDM): Consider MDM the colleague who always keeps the big picture in check, ensuring everything aligns and everyone is on the same page.     ➤ Reference Data Management (RDM): Have you ever been in a meeting where everyone uses a different term for the same thing? RDM removes the confusion by standardising key data categories across your business. ➤ Metadata Management: Metadata is like the notes and context we make on a project. It tracks how, when, and why decisions were made, so you can always refer to them later.     ➤ Data Catalog: Imagine a digital filing cabinet that’s not only organised but searchable, easy to navigate, and quick to find exactly what you need.     ➤ Data Lineage: This is your project’s timeline, tracking each step of the data’s journey so you always know where it has been and is going.     ➤ Data Versioning: Data evolves as we update project plans. Versioning keeps track of every change so you can revisit previous versions or understand shifts when needed.     ➤ Data Provenance: Provenance is the backstory—understanding where your data originated helps you assess its trustworthiness and quality.     ➤ Data Lifecycle Management: Data doesn’t last forever, just like projects have deadlines. Lifecycle management ensures your data is used and protected appropriately throughout its life.     ➤ Data Profiling: Consider profiling a health check for your data, spotting potential errors or inconsistencies before they affect business decisions. When we get these pillars right, data goes from being just a tool to being a trusted ally—one you can count on to help make decisions, drive strategies, and ultimately support growth. So, what pillar would you focus on to make your data more trustworthy? Cheers! Deepak Bhardwaj

Monday Motivation for CFOs, CISOs, and CIOs friends: Good data lifecycle pays for itself. When you manage data end to end, you do not just reduce risk; you unlock measurable savings, audit readiness, and delivery speed. The Executive Case: - Proactive security: Shrink blast radius by knowing what is sensitive, where it lives, who can touch it, and how it moves. Fewer incidents and faster containment. - Enhanced compliance: Retention, deletion, and access are policy driven and auditable. DSARs and RoPAs move from fire drills to workflows. - Business agility: Cloud migrations, AI pilots, and new apps land faster because data is already classified, labeled, and access controlled. Finance-Ready Outcomes: - Storage and SIEM spend: Cut ROT and duplicate events to lower ingestion and storage by 10–25%. - Incident economics: Reduce mean time to detect/contain and the number of noisy alerts; focus analysts on critical events. - Audit efficiency: Time-to-evidence (days → hours) with exportable “found vs. not found” coverage. - DSAR and records management: Automate discovery and defensible deletion to lower per-request cost and backlog. A Simple 5-Metric Scorecard: 1. Percent of data labeled and under policy 2. ROT removed (TB and percent) 3. SIEM ingestion reduced (events and cost) 4. DSAR turnaround time (median) 5. Critical alerts that are actually actionable (ratio) 90-Day Leadership Plan: - Days 1–30: Inventory top repositories, enable default labels, and turn on automated retention for one stale dataset. - Days 31–60: Right size access for two high-risk groups, publish the first coverage report, and track alert reduction. - Days 61–90: Expand to AI use cases: restrict what copilots can see by default, require MFA for sensitive data, and produce board-ready metrics. Takeaway: A structured data lifecycle makes security proactive, compliance predictable, and the business faster. It is one of the rare programs that improves risk, cost, and velocity at the same time. What is the one lifecycle win you will sponsor this quarter? Post it below or come share your tips with others in Nov at DataSecAI in Dallas https://lnkd.in/gA59PvDv Jason Clark Lamont Orange Dr. Adrian M. Mayers Thomas Mazzaferro Hardik Mehta Yotam Segev Tamar Bar-Ilan Aaron Martin Patrick O'Keefe Jason Hayek Daniel May Nick Daruty Lekshmy Sankar, PhD Zohar Vittenberg Nadav Zingerman Paul Chapman Troy Gabel Ralph Loura Dr. Chris Peake Nicole Darden Ford Rich Noonan Tim Rains Mike McGee

How the #DataGovernance Council of #Bank can ensure the #DataArchitecture is robust, secure, compliant, and capable of supporting the bank's strategic objectives: When designing the data architecture, the data governance council should focus on several key points to ensure the architecture is effective, compliant, and sustainable. Here are the most important considerations:   1. Establish Clear Data Governance Framework and Policies:   - Define and document data governance policies, standards, and procedures.   - Ensure policies cover #dataquality, #datasecurity, #dataprivacy, and data usage.   - Communicate these policies effectively to all stakeholders. 2. Define Roles and Responsibilities:   - Clearly delineate roles and responsibilities within the data governance framework.   - Appoint #datastewards, data custodians, and data owners.   - Ensure accountability for data quality, security, and compliance.   3. Ensure #RegulatoryCompliance:   - Stay updated with relevant regulations and ensure the architecture complies with laws such as #GDPR, CCPA, and industry-specific regulations.   - Implement data retention and deletion policies that adhere to legal requirements.   - Maintain detailed audit trails and documentation for compliance purposes.   4. Data Quality Management:   - Establish data quality standards and metrics.   - Implement tools and processes for data profiling, cleansing, and validation.   - Continuously monitor and improve data quality.   5. Data Security and Privacy:   - Define and enforce data security policies, including encryption, access controls, and data masking.   - Implement privacy policies to protect sensitive customer information.   - Conduct regular security audits and risk assessments.   6. Data Accessibility and Usage:   - Ensure data is accessible to authorized users while maintaining security.   - Implement role-based access control (RBAC) and monitor data access.   - Promote #datademocratization while balancing control and security.   7. #DataIntegration and #Interoperability:   - Define standards for data integration, including #APIs, data formats, and protocols.   - Ensure seamless data exchange between different systems and platforms.   - Plan for the integration of legacy systems with modern #technology.   8. #MetadataManagement:   - Implement a robust metadata management system.   - Ensure that all data assets are properly cataloged and documented.   - Use metadata to improve data discoverability and governance.   9. #DataLifecycleManagement:   - Define the lifecycle of data from creation to deletion.   - Implement policies for data archiving, retention, and purging.   - Ensure data lifecycle policies comply with regulatory requirements. (Continue in first comment) Image Source: McKinsey #TransformPartner – Your #DigitalTransformation Consultancy

Almost everything we do in data follows a lifecycle. Below are the main ones in data management. Knowing them is very helpful when designing new processes, frameworks, and operating models. 1. Data Lifecycle - Describes the journey of data from creation to destruction. Stages: ~Creation. Data is created at the source. ~Collection. Data is captured and gathered. ~Processing. Data is transformed and prepared. ~Storage. Data is persisted in systems. ~Analysis. Data is analyzed to generate insights. ~Sharing. Data is distributed to consumers. ~Archiving. Data is retained for long-term or compliance needs. ~Destruction . Data is securely deleted at end of life. 2. Metadata Lifecycle: ~Metadata Collection. Metadata is captured from systems and processes. ~Metadata Storage. Metadata is stored in repositories or catalogs. ~Metadata Access. Metadata is made discoverable. ~Metadata Consumption. Metadata is used for governance and decision-making. ~Metadata Aging. Metadata becomes obsolete and retired. 3. Data Engineering Lifecycle Focuses on building and operating data pipelines and platforms. ~Generation. Data is produced by source systems. ~Storage. Data is stored in appropriate platforms. ~Ingestion. Data is moved into processing environments. ~Transformation. Data is cleaned, enriched, and structured. ~Serving. Data is delivered for analytics, BI, or applications. 4. Data Analytics Lifecycle Describes how data is transformed into insights and actions. ~Problem Definition. Define the problem. ~Data Requirement, Collection & Access. Gather and access relevant data. ~Data Cleaning & Preparation. Prepare data for analysis. ~Exploratory Data Analysis. Explore patterns and anomalies. ~Advanced Analysis & Modeling. Generate insights using models. ~Visualization & Communication. Communicate insights visually. ~Implementation & Monitoring. Operationalize and track results. 5. Data Product Lifecycle Describes how data assets are built and managed as reusable products. ~Design. Define the product vision, users, and requirements. ~Develop. Build pipelines, models, and supporting components. ~Deploy. Release the product for consumption. ~Evolve. Improve and adapt the product based on feedback and usage. Few of my favorites: 6. Continuous Improvement Lifecycle (PDCA) Widely used in data quality management and operational improvement. ~Plan. Identify the problem, define goals, and create a plan. ~Do. Implement the plan on a small scale. ~Check. Analyze results and compare them with goals. ~Act. Standardize improvements or adjust and repeat the cycle. 7. Six Sigma Lifecycle (DMAIC) A data-driven approach to improving existing processes. ~Define. Clarify the problem and goals. ~Measure. Understand current performance. ~Analyze. Identify root causes of defects. ~Improve. Implement solutions. ~Control. Sustain improvements over time. What other major ones did I miss?

80% of the time is spent by Data Analysts just cleansing and transforming the data. ↓ Yet, it is the fuel of any Digital initiative...yes even Gen AI. ↳ A strong but flexible data foundation is vital for building the advanced analytics capacity for any Finance function. ↓ However, you do not need to know the exact architecture but knowing enough to be able to understand the use case data requirement is sufficient. ➤ What is a Data Lake? ➝ A data lake is a centralized data repository that stores structured, semi-structured, or unstructured data in its raw format. It allows organizations to easily access and analyze diverse data types. To maintain efficiency, it's crucial to prioritize storing only the most important data. ➤ Best Practices To maximize the effectiveness of a Data Lake, consider these best practices: ➝ Incremental Scaling: Start with a small, focused implementation. As your understanding and needs evolve, scale your Data Lake incrementally. ➝ Governance Framework: Implement strong data governance from the outset. This includes data lineage tracking, access controls, and ensuring compliance with regulatory requirements. ➝ Data Cataloging: Develop a comprehensive data knowledge catalog that helps users discover, understand, and access the data. This improves data transparency and reduces redundancy. ➝ Performance Optimization: Regularly monitor and optimize the performance of your Data Lake, particularly in how data is ingested, stored, and retrieved. This ensures it remains efficient and effective. ➤ Key Considerations for CFOs    ➝ Understand the Data Landscape: Align data sources to be ingested in Data Lake, with strategic goals and business use cases. Collaborate with CDOs/CIOs to integrate non-financial data for better forecasting and decision-making.  ➝ Data Capture and Transformation: Establish processes for capturing and harmonizing data, starting with relevant data and expanding gradually. ➝ Single Source vs. Multiple Versions of Truth: Balance having a Single Source of Truth (SSoT) and Multiple Versions of Truth (MVoT) to enable tailored usage across the organisation. ➝ Privacy and Security: Prioritize security and privacy, implementing strong governance and ensuring compliance with industry regulations. Download the hi-res PDF ↴ https://lnkd.in/ghvXAa-g #datalake #ai #data — I am Tariq Munir...My mission is to create a Tech-enabled Humanistic future for all through my talks, writings, and content. Follow me to be part of this mission and learn more about Digital Transformation, Data, and AI.

Once you have your hot, medium, and cold data storage requirements, the next set of considerations are as follows: Data Observability, Data Orchestration, Data Monitoring, Data Contracts, and Data Pipeline Automation. 1. Data Observability: Serves as a checkup for your systems. It lets you ensure your data is fresh, accurate, and flowing smoothly, helping you catch potential problems early on. My (Preference) Technologies of Choice: Monte Carlo, OpenTelemetry. Barr Moses and her team at Monte Carlo have an excellent technology medium in the Data Observability space. 2. Data Orchestration: Think of data orchestration as a conductor that ensures all parts of your data processes occur in the correct sequence and at the right time. My (Preference) Technologies of Choice: Mage and Apache Airflow. Tommy Dang and the Mage team have something special going on to challenge the Airflow crown. Check them out. 3. Data Monitoring: Data monitoring sets quality control checkpoints for data to ensure it meets specific standards, enabling trustworthy insights. My (Preference) Technologies of Choice: Great Expectations. Many others exist, but I enjoy Great Expectation's ease of use. 4. Data Contracts: Data contracts are like rulebooks for how different parts of your system should share data. They ensure everyone speaks the same "data language," preventing misunderstandings. My (Preference) Technologies of Choice: Gable and Atlan. Chad Sanderson and the engineering team Gable is building out will deliver a high-quality data contract solution. Check them out. 5. Data Pipeline Automation: Instead of manually creating and maintaining data pipelines, pipeline automation uses tools to save time and prevent errors. My (Preference) Technologies of Choice: The usual suspects are Jenkins, CircleCI, and GitLab CI/CD. Others exist, and laugh all you want, but these tools still wax poetic in production environments worldwide. I will take the rest of this week to break each area down in more detail, but Monday seemed like a good day for a brief introduction to each concept and tool option. I tagged first-degree connections that have built deployable solutions you can leverage now and meet a wide range of industry specifications (Mage, Gable, and Monte Carlo- Please get DoD approval and on some Approved Product List soon 🤣). #dataarchitecture #technology #datascience

The DAMA Wheel evolved. In the last framework in this section of the series, this evolved framework breaks data management into three key layers. Foundational Activities sit at the core. Data protection, metadata management, data quality. These are your non-negotiables. Lifecycle Management sits on the foundation with three parts: - planning activities (architecture, modelling & design) - enablement activities (like master data management and integration) - usage activities (like business intelligence, data science, analytics) This is where value gets created. Data Governance surrounds it all, providing direction through strategy, policy, and stewardship. Remember, you need the foundations that suit your business. The level of security and quality will vary. None of this is a one size fits all approach. Many organisations try to jump straight to the use layer without building the foundation. Then wonder why their analytics are unreliable. AI usage, especially the current ‘throw it at an LLM’ trend, makes strong foundations even more important. (Image from DMBOK v2) Which layer gets the most attention in your organisation? And which one probably needs more focus? --------- Hi, I'm Meagan. I'm reading the Data Management Body of Knowledge (DMBOK) cover to cover and sharing key takeaways, reflections, and practical tips as I go. Follow me to explore ethics, governance, modelling, metadata, security and more... Join the conversation in the comments #DMBOKwithMeagan #DataManagement #DataStrategy #DAMAWheel

Defining the technologies in a data platform is confusing   So what tooling does exist in one? And what should my data stack be?   To achieve its objectives, I’ve defined six foundational technology areas that need to be part of a data platform:   1) 𝐈𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦/ 𝐓𝐨𝐨𝐥𝐢𝐧𝐠 – Tooling that connects to operational technology and other systems to facilitate ingesting varying types of data into the platform. This must be built to consider the kind of ingestion process (batch or real-time) and the maintenance of a certain standard of data quality/ usability 2) 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 – The infrastructure that stores data (either raw or processed), providing a point of accessibility for further transformation or access by end users (after it is processed and curated to their needs)   3) 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 – Can happen before data is ingested into the storage layer through ETL pipelines or after, feeding from a lake to tools/ access layers. This part of the process cleans the data, normalises it, enriches it with additional information, and/ or applies business logic to it   4) 𝐃𝐚𝐭𝐚 𝐀𝐜𝐜𝐞𝐬𝐬 𝐋𝐚𝐲𝐞𝐫 – This technology component is often an add-on to storage solutions, usually query engines or access management within storage solutions or processing platforms, but it might also include data stores or marts, reverse ETL, and security tooling   5) 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐁𝐈 𝐓𝐨𝐨𝐥𝐬 – Facilitate data analysis, science and visualization, turning curated data into actionable insights. Without analysis, the data platform is quite useless and just an expensive repository of numbers and letters   6) 𝐃𝐚𝐭𝐚𝐎𝐩𝐬 & 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐓𝐨𝐨𝐥𝐬 – An emerging technology category within the data platform. It includes data observability, catalogues/ lineage, MDM, quality and security tooling. DataOps and management ensure that the data lifecycle is not one colossal ‘garbage in, garbage out’ exercise Thoughts? How does your data platform look? For more, check out my recent article on this topic in The Data Ecosystem   #datastrategy #technology #dataplatform #technologystrategy #DylanDecodes