Deepak Bhardwaj’s Post

Understanding data management is crucial for maximising the value of data as an asset.

I’ve never seen a company that executes against the data lifecycle correctly   Why? Because a simplistic view of source to analysis is not real life   So for my upcoming newsletter this weekend I’m digging into what I see as a to-be ideal data lifecycle (let’s call it the data ecosystem data lifecycle). Here is a sneak peak and a visual into how I’ve started to think about it:   1️⃣ 𝐃𝐚𝐭𝐚 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Sourcing, prioritising and integrating the data into the organisation’s data lifecycle 2️⃣ 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 & 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Storing, modelling, processing and encrypting your data once it is integrated 3️⃣ 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: An underlying current to manage metadata, master data and understand how the data flows and where breaks may occur 4️⃣ 𝐃𝐚𝐭𝐚 𝐒𝐞𝐫𝐯𝐢𝐧𝐠: Other definitions often skip this step or lump it into storage & processing. But this step is essential to ensure access through data residency, the creation of well-defined contracts and designing the solution architecture for scalable development 5️⃣ 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: This is where the rubber meets the road with data being used for analytics, AI/ ML, visualisation and actual business decision-making 6️⃣ 𝐃𝐚𝐭𝐚 𝐑𝐞𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧/ 𝐃𝐢𝐬𝐩𝐨𝐬𝐚𝐥: Finally, teams need to understand what to do with their data after it goes through the rest of the lifecycle (e.g., reverse ETL, disposal, archiving data) Sign up for my newsletter and read more into this on Sunday. Having a well mapped out data lifecycle is honestly a great first step to get everybody on the same page about how data should flow through the organisation   Follow along for daily data, career and consulting advice by hitting the 🔔 on my profile and commenting away   #data #datanalytics #datamanagement #datalifecycle #DylanDecodes

This picture is the way data is done correctly... I've been working with data for 20+ years. This is the general logical model I try to follow. I've never seen it put so simply as Dylan Anderson has it here. The biggest challenge I see is each company I've been to thinks there is special snowflake in this dozen apply to them. Your business can be special while the way you process your data is generally the same. When C Suite come at me asking "Hey Rob, how do we use AI to increase foot traffic" I reply "We get good at tracking and analyzing the business basics. Then we use AI to drive those" #ai #data #analytics

🔍 Data Cleaning: Your First Step Towards Analytics Excellence In the intricate dance of data analytics, every step counts, but it's the first step – data cleaning – that truly sets the rhythm. As I engage with more data projects, the more I realize its pivotal role in unlocking the full potential of analytics. Here's why data cleaning is indispensable: 𝐒𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐭𝐚𝐠𝐞 𝐟𝐨𝐫 𝐑𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐈𝐧, 𝐆𝐚𝐫𝐛𝐚𝐠𝐞 𝐎𝐮𝐭: The adage holds true. The accuracy of your analytics is directly tied to the quality of your data. Proper preparation ensures that your data is clean, consistent, and reliable. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐚𝐧𝐝 𝐀𝐧𝐨𝐦𝐚𝐥𝐢𝐞𝐬: During data preparation, you get the first glimpse of patterns and potential anomalies. This early insight is crucial for guiding your analytical approach. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐁𝐨𝐨𝐬𝐭𝐞𝐫: Well-prepared data streamlines the entire analytics workflow. It reduces the time spent on data wrangling in later stages, allowing analysts to focus on deriving insights. 𝐑𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: A well-structured dataset simplifies complex analytical tasks. It makes data more accessible and understandable, not just for data scientists but for all business users. 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐓𝐡𝐚𝐭 𝐌𝐚𝐤𝐞 𝐚 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬: Whether through imputation or removal, addressing missing data is crucial to prevent skewed analysis. 𝐄𝐫𝐫𝐨𝐫 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧: Identifying and fixing inaccuracies, such as typos or incorrect entries, is essential for maintaining data integrity. 𝐎𝐮𝐭𝐥𝐢𝐞𝐫 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧: Spotting and assessing outliers help in understanding data anomalies, which could be critical insights or errors needing correction. 𝐓𝐡𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐨𝐟 𝐂𝐥𝐞𝐚𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐌𝐨𝐝𝐞𝐥 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: For advanced analytics and machine learning, clean data means more accurate and reliable models. 𝐈𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠: Clean data leads to clearer insights, enabling businesses to make informed decisions with confidence. Data cleaning might not be the most glamorous part of data science, but it's undoubtedly one of the most important. By dedicating the necessary time and resources to clean your data, you're laying the groundwork for all the analytics success that follows. #DataConsulting #DataArchitecture #DataCleaning

𝑾𝒉𝒂𝒕 𝒂𝒓𝒆 𝒕𝒉𝒆 7𝑽'𝒔 𝒐𝒇 𝑩𝒊𝒈 𝑫𝒂𝒕𝒂 ? 𝑽𝒐𝒍𝒖𝒎𝒆: - As the term implies, big data analytics entails handling and analyzing vast amounts of data. To effectively work with such massive datasets, specialized tools and infrastructure are necessary for capturing, storing, managing, cleaning, transforming, analyzing, and reporting the data. 𝑽𝒆𝒍𝒐𝒄𝒊𝒕𝒚: - Velocity denotes the speed at which data is generated. To keep up with the rapid generation of data, systems for processing and analyzing data must possess sufficient capacity to handle the influx of data and deliver timely, actionable insights. 𝑽𝒂𝒓𝒊𝒆𝒕𝒚: - Variety refers to the diversity of data types and sources. Data can manifest in various forms, originate from diverse sources, and exist in structured or unstructured formats. Understanding the types of data and their sources, as well as the interrelationships within the datasets, is vital for generating meaningful insights from big data. 𝑽𝒂𝒓𝒊𝒂𝒃𝒊𝒍𝒊𝒕𝒚: - Big data often contains noisy and incomplete data points, which can obscure valuable insights. Addressing this variability typically involves data cleaning and validation processes to ensure data quality. 𝑽𝒆𝒓𝒂𝒄𝒊𝒕𝒚: - Veracity pertains to the accuracy and authenticity of the data. Data must undergo validation to ensure that it accurately represents essential business functions and that any data manipulation, modeling, and analysis does not compromise the data's accuracy. 𝑽𝒂𝒍𝒖𝒆: - A successful big data analytics strategy must generate value. The insights derived from the analysis should provide meaningful guidance for improving operations, enhancing customer service, or creating other forms of value. An integral part of developing a big data analytics strategy is distinguishing between data that can contribute value and data that cannot. 𝑽𝒊𝒔𝒖𝒂𝒍𝒊𝒛𝒂𝒕𝒊𝒐𝒏: - Visualization plays a vital role in data analytics, as it involves presenting the analyzed data in a visually comprehensible manner. When planning data visualization, it is essential to consider the end user and the decisions the visualizations aim to support. Well-executed data visualization facilitates swift and well-informed decision-making. #BigDataAnalytics #DataInsights #DataVelocity #DataVariety #DataQuality #DataVeracity #DataValue #DataVisualization #DataDrivenDecisions #BigDataStrategy

Unlock the Power of Data: Data Lakes vs. Data Warehouses In today's data-driven world, companies are swimming in an ocean of information. Yet, many struggle to harness its potential, often due to a lack of a clear data strategy. Imagine this scenario: a company has heaps of data scattered across various systems and platforms, but lacks the means to effectively organize, analyze, and derive insights from it. The result? 𝗠𝗶𝘀𝘀𝗲𝗱 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀, 𝗶𝗻𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝗶𝗲𝘀, 𝗮𝗻𝗱 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹𝗹𝘆 𝗹𝗼𝘀𝗶𝗻𝗴 𝗴𝗿𝗼𝘂𝗻𝗱 𝘁𝗼 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿𝘀 𝘄𝗵𝗼 𝗵𝗮𝘃𝗲 𝗺𝗮𝘀𝘁𝗲𝗿𝗲𝗱 𝘁𝗵𝗲 𝗮𝗿𝘁 𝗼𝗳 𝗱𝗮𝘁𝗮 𝘂𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻. data lake and the data warehouse, two distinct approaches to managing and analyzing data. Each has its strengths and use cases, offering companies different paths to unlocking the power of their data. 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲: it is capable of storing raw, unstructured data in its native format. It's a flexible, scalable solution that allows organizations to ingest data from various sources without the need for upfront structuring. This raw data can then be processed, transformed, and analyzed as needed. 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲: On the other hand, a data warehouse is like a carefully curated library, housing structured, processed data optimized for analysis and reporting. It's designed for querying and retrieving specific information quickly and efficiently. Data warehouses typically follow a schema-on-write approach, meaning data is structured before being loaded into the warehouse. Which one should you use? it depends on your needs 𝗨𝘀𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 𝗜𝗳: • You have diverse, unstructured data sources. • You need flexibility to explore and analyze data in its raw form. • You're focused on exploratory analysis and experimentation. 𝗨𝘀𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 𝗜𝗳: • You require fast, reliable access to structured data for reporting and analysis. • You prioritize data consistency and accuracy. • You have well-defined business requirements and use cases. 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿,  𝗜𝘁'𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗻𝗴 𝗱𝗮𝘁𝗮—𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗵𝗮𝗿𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗶𝘁𝘀 𝗽𝗼𝘄𝗲𝗿 𝘁𝗼 𝗱𝗿𝗶𝘃𝗲 𝗶𝗻𝗳𝗼𝗿𝗺𝗲𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, 𝗳𝗼𝘀𝘁𝗲𝗿 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗴𝗮𝗶𝗻 𝗮 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗲𝗱𝗴𝗲 𝗶𝗻 𝘁𝗼𝗱𝗮𝘆'𝘀 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗺𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲. 𝗜 𝗵𝗼𝗽𝗲 𝘁𝗵𝗶𝘀 𝘄𝗮𝘀 𝘂𝘀𝗲𝗳𝘂𝗹 ❤️. #datawarehouse #datalake #datascience #bigdata

Data Mesh: A Transformative Data Management Approach Exciting developments in the world of data management! The concept of "data mesh," introduced by Zhamak Dehghani in 2019, has gained traction as a means to achieve distributed data access. But what exactly is it? Is it a new tech, a replacement for existing repositories, or a theoretical idea? McKinsey defines data mesh as a game-changing data-management approach. It organizes data by domains, treats it as a product, enables self-service access, and backs it with federated governance. Let's break it down! 1️⃣ Organizing data by domains allows business users to access specific areas more efficiently, reducing reliance on central teams. This paves the way for quicker insights and fact-based decisions. 2️⃣ Treating data as a product boosts self-service access, empowering non-tech users. They can create value from data, driving innovation and ROI. 3️⃣ Federated governance ensures that the data ecosystem operates smoothly, supporting data consumers and producers alike. A well-executed data mesh offers remarkable advantages. It accelerates analytics app launches, gives users self-service access, and enhances data IQ. Real-world examples show incredible outcomes. A mining company reduced data-engineering time, achieving faster use-case development and greater data stability. Remember, adopting a data mesh is a journey, not a leap. It's about gradual steps, like a biotech company's success story. Start small, work with users, improve data quality, and scale up! Centralized standards for data quality and architecture are crucial. Whether adapting existing ones or creating new ones, consistency is key. Are you intrigued by the potential of data mesh? Let's discuss how this paradigm shift can revolutionize data management and drive innovation in your organization! #DataMesh #DataManagement #Innovation #DigitalTransformation #BusinessInsights

How to Build a Robust Data Strategy with a Well-Defined Data Governance Framework Why is data governance essential? In today's data-driven business landscape, a well-defined data governance framework is essential for ensuring the accuracy, availability, and reliability of data within organizations. Data governance plays a critical role in making data-driven decisions, complying with regulations, and fostering innovation. Benefits of data governance: Trustworthiness: Ensures that the data your business relies on is accurate and reliable. Compliance: Helps meet regulatory requirements, minimizing risks. Decision-making: Provides a strong foundation for analytics and business intelligence. How Xpecta Datalab can help: With use cases spanning from football (soccer) to logistics, and from media to oil & gas, Xpecta has witnessed firsthand the transformative power of well-governed data. Our team of PhD-level experts and industry veterans can help you: Assess: Understand your current data landscape, identify gaps and opportunities. Strategize: Craft a bespoke data governance framework tailored to your business needs and industry nuances. Implement: Embed data governance practices in your operations, ensuring long-term success and adaptability. Educate: Empower your team with the knowledge and tools they need to champion data governance internally. Are you ready to take the plunge into better data governance? Let's elevate your data strategy together! Connect with us for a deeper conversation. www.xpecta.co Christian Saffon Sedan Francisco Baquero Vergara #datagovernance #datastrategy #xpectadatalab #data #ai

𝐃𝐞𝐜𝐨𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐒𝐜𝐡𝐞𝐦𝐚𝐬: 𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 📊 In the intricate landscape of data management, the schema is more than just a blueprint. It's a strategic decision that impacts performance, scalability, and adaptability. Let's demystify three distinct schema approaches to better inform our storage decisions: 1. 𝐒𝐜𝐡𝐞𝐦𝐚 𝐀𝐠𝐧𝐨𝐬𝐭𝐢𝐜:   - 𝐖𝐡𝐚𝐭 𝐈𝐭 𝐈𝐬 : A hands-off approach where data is ingested without a predefined structure, and its organization is determined only when accessed.   - 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: Tremendous flexibility, accommodating varied and unpredictable datasets. This approach reduces the upfront design effort and facilitates easier integration of diverse data sources.   - 𝐈𝐝𝐞𝐚𝐥 𝐅𝐨𝐫: Environments with constantly evolving data models or those integrating multiple heterogeneous data sources, like in some Big Data applications. 2. 𝐅𝐥𝐞𝐱𝐢𝐛𝐥𝐞 𝐒𝐜𝐡𝐞𝐦𝐚:   - 𝐖𝐡𝐚𝐭 𝐈𝐭 𝐈𝐬: A predefined but adaptable schema. As data evolves, fields can be seamlessly added or removed, ensuring agility without compromising entirely on structure.   - 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: Strikes a balance by offering the adaptability to accommodate growth while maintaining a level of data consistency.   - 𝐈𝐝𝐞𝐚𝐥 𝐅𝐨𝐫: Growing businesses anticipating frequent changes in data requirements or those migrating from rigid to more flexible systems, like startups scaling operations. 3. 𝐑𝐢𝐠𝐢𝐝 𝐒𝐜𝐡𝐞𝐦𝐚:   - 𝐖𝐡𝐚𝐭 𝐈𝐭 𝐈𝐬: Data is beholden to a strict, predefined structure. Any variation means revisiting and modifying the schema directly.   - 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: Guarantees data uniformity, streamlines query performance, and reduces anomalies. It's predictable, which can be invaluable for systems that prioritize data consistency.   - 𝐈𝐝𝐞𝐚𝐥 𝐅𝐨𝐫: Established enterprises or applications in industries where data consistency and integrity are paramount, like finance or healthcare. Choosing the right schema type isn't just a technical decision—it's a strategic one. As you plot your data journey, reflect on your current needs, anticipate future challenges, and prioritize what matters most: flexibility, predictability, or a blend of both. #SchemaEvolution #StrategicDataManagement #data #dataengineering #learning #DataModeling #DataArchitecture

Are scattered data sources hindering your business decisions? Enterprises today are drowning in a sea of disorganised data spread across multiple sources and formats. Extracting actionable insights from this fragmented data has become a complex and time-consuming endeavour, making it challenging to make data-driven decisions swiftly. When data is scattered and unstructured, it becomes nearly impossible to trust the information, let alone use it to drive critical business decisions. This data chaos leads to missed opportunities, delayed strategies, and lost revenue. It’s time to break free from this vicious cycle! In today’s fast-paced world, DataOps combined with a Lakehouse Architecture enables enterprises to drive data-driven decisions rapidly. Here’s how: 🔹 Organise Your Data: 🔘 Raw Data: Freshly ingested, unprocessed data. 🔘 Bad Data: Data needing review or correction. 🔘 Metadata: Information about your data. 🔘 Standardised Data: Cleaned and formatted data. 🔘 Curated Data: Ready-to-use, high-quality data. 🔹 Create a Metadata-Driven Pipeline: 🔘 Validate: Ensure data accuracy and integrity. 🔘 Clean: Remove errors and inconsistencies. 🔘 Standardise: Convert data into a uniform format. 🔘 Curate: Prepare data for final use. 🔹 Leverage Delta Lake: 🔘 Utilise Delta Lake for ACID properties to ensure data reliability and consistency in your Data Lake. 🔹 Serve the Curated Data: 🔘 Provide high-quality, ready-to-use data to your stakeholders, enabling informed decision-making. Say goodbye to data chaos and embrace the future of data-driven success with DataOps and Lakehouse Architecture! 🌟📊 ℹ️ I used Cloudairy's Cloudchart to create the visual for this post. I invite your to register and try it yourself - 𝐒𝐢𝐠𝐧𝐢𝐧𝐠 𝐮𝐩 𝐭𝐨𝐝𝐚𝐲: https://lnkd.in/gaNV3mPf #DataOps #LakehouseArchitecture #DataDriven #DataDrivenDecision #DataLake #DeltaLake #DataManagement #BusinessIntelligence #DataCentric