Bardess Group, a Cleartelligence Company’s Post

▶ Book Review "Elements of Data Strategy" by Boyan Angelov about "A Framework for Data and AI-Driven Transformation" 📖 https://lnkd.in/eUPaiawr Finally there are not so many good books available describing how data strategy looks like and how it is done. I appreciate writers like Tiankai Feng from my last review (https://lnkd.in/euNY6j_m) and Boyan Angelov, who put down their thoughts and share there knowledge and experiences. Because in general it opens the discussion about data strategy and enhance quality, meaning and understanding of making better use of data. And indeed, possibly this books would complement each other well. I see a certain focus on main topics and a data science touch in many aspects which should be considered. This book is like a collection of important elements of a data strategy today, delivering a good overview of many methods, tools and systems. Therefore the name of the book is a good description of the content. 🎯 As every book, it has its target group and is more or less interesting, depending on your backround. Therefore I wrote my perception down in a review to give you an idea if it could be right for you. 🗣 Have you read the book? What is your view about? #DataStrategy #BookReview #DataArchitecture

7 Critical Success Factors for Big Data Analytics Seven critical success factors are essential for effective big data analytics (Sharda et al., 2019): 1. Clear Business Need: There must be a clear business rationale for using big data analytics, which should align with the organization’s vision and strategy. 2. Executive Support: Big data analytics needs to be backed by an executive champion to ensure the organization’s commitment. 3. Alignment Between Business and IT Strategy: The business strategy should be aligned with IT strategy, with IT supporting the business through big data initiatives. 4. Fact-Based Culture: For big data analytics to succeed, organizations need a fact-based culture where decisions are driven by data rather than intuition. 5. Robust Data Infrastructure: A solid and evolving data infrastructure is essential for handling big data. It must be capable of reflecting changes in the data landscape and act as a strong backbone for analysis. 6. Right Analytical Tools: The correct tools must be chosen to analyze big data effectively. 7. Skilled Personnel: Competent and qualified individuals with advanced analytical skills are necessary to ensure the success of big data initiatives. Key Challenges in Implementing Big Data Analytics When implementing big data analytics, several challenges must be addressed, often related to the critical success factors: 1. Handling Large Volumes of Data: The organization must be capable of handling the vast amounts of data flowing in at high speed to ensure optimal use. 2. Data Integration: Systems should ensure that data from various formats and structures can be combined quickly and efficiently. 3. Processing Capability: Processing systems need to be robust enough to store, analyze, and process large datasets in real-time to avoid bottlenecks. 4. Data Governance - Storage and Accuracy: Storing data securely and ensuring privacy is maintained at all times, while ensuring that it is accurate, reliable, and accessible, is crucial to making informed decisions. 5. Skilled Talent: Qualified professionals with the necessary technical and analytical expertise are key to driving big data initiatives successfully. 6. Cost Considerations: Developing and implementing big data solutions can be costly, so organizations must factor in these costs and seek optimal solutions for resource management. References Sharda, R., Delen, D., & Turban, E. (2019). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (11th ed.). Pearson.

Seattle Data Guy recently dropped an article detailing why database modeling is so difficult. A few keys details he points out: 1️⃣ Data integration can be complex when dealing with multiple data sources 2️⃣ Translating human requirements into a data model requires experience 3️⃣ Creating a future proof model is easier said than done What other aspects around data modeling are difficult? #dataarchitecture #dataengineering #databasemodeling https://lnkd.in/eyGU3_AY

Organizations are ramping up their efforts to truly become data-driven and empower every business unit to utilize data and take real-time decisions. A data management architecture such as a data lakehouse allows data analysts and data scientists to apply their data sets for business intelligence and data analytics initiatives. Uncover more insights about data lakehouse in this blog https://lnkd.in/gKFAW7uS #datalakehouse #datamanagement #datanalytics #cloudanalytics

Data Science vs. Big Data vs. Data Analytics https://lnkd.in/egdQ2PiE #datascience #bigdata #dataanalytics #mrkassociates

🔍Sharing with the community what I have learned in the Data Engineer journey!🚀 Here is the second article 📄 on the core topic, Data Modeling: https://lnkd.in/gJ_dY_-6 I'd love to hear your thoughts and any experiences you have with data modeling! #DataEngineering #DataModeling #MediumArticle #TechCommunity #LearningJourney

Data has become the key (if not the most important) part in business strategy and decision-making. The process of extracting values from data often requires the following components 👉 Data Collection: this is the first component. As we need data, we have to collect it. In data collection, we may have certain types of data which may not be utilized at the moment. But, we should not ignore them as data always has value (and we may want these types of data sooner or later). Remember that we can store all data that we have in a cheap storage (e.g., in a form of Data Lake). On the other hand, there could be other types of data we want to have, but they are not currently available. In this case, we need to find a way to get them. 👉 Data Cleaning and Aggregation: once we have the data, we need to clean it. It's because bad data does not bring any value to us (garbage in garbage out). In some extreme cases, bad data even damages our values. In addition to cleaning the data, if the data come from multiple sources, it's important to also aggregate the data so that later we have a single source of references when we use the data. The cleaned and integrated data can be stored in a normal Database or even better in a well designed Data Warehouse (for query optimization). 👉 Data Exploration and Visualization: given the cleaned and well integrated data, it comes to the point where we want explore and visualize the data to look for data values, which can be hidden patterns, trends, clusters, etc. This is the time when we examine relationships and build hypotheses according to the data. The easiest way to fulfill this component is by leveraging reporting and visualization tools such as Power Bi, Tableau... to display different types of graphs from scatter plots, line graphs, stacked bar charts, box-plots to heat-maps, area maps, histogram, etc. 👉 Data Utilization and Value Extraction: while the results from the previous component can be manually used to support business strategy and decision-making, it's better if they can be used automatically to generate values. This is when machine learning models can be brought in. Note that as we have a wide range of models from the traditional, basic models to the advanced, deep learning ones, each of which has its own advantages and disadvantages and there could be more than one model that works for a problem, it's important to select and design a model that works best for a given use-case with certain limitations/requirements. 👉Finally, while the above components are main components to extract values from data, there are other supporting components that go along with these ones. They are Data Monitoring and Data Governance components. These components play a role to guarantee that the data are exactly as what they should be and are used according to their terms and conditions. #datacomponents #datacollection #datacleaning #dataintegration #dataexploration #datavisualization #machinelearning

You don't need #datavault to understand the importance of many-to-many relationships in data models. Many-to-many relationships often get a bad rap, and people often blame data vault for this when in fact, it's one part of a good data modeling practice dating back to Peter Chen. In case you missed it (again, it's not just about Data Vault) please check out this article that explains a few of the business reasons, and technical reasons why Many-to-Many relationships (in analytics and operational systems) have their appropriate place in design and architecture. People who attack many-to-many may just be missing a bit of knowledge on the subject. Yes - it makes queries more difficult to write, but when applied appropriately in #datawarehousing and #analytics it enables data driven discovery: we can answer the questions: what was the relationship across entities? and when was it enforced? we can also ask questions about the impact of relationship changes over time. By the way, "foreign keys" and relationships at a conceptual and business level are actually soft business rules! They change over time, they are not set in stone, understanding this means to understand near-zero re-engineering in your analytics solution while allowing the data to flow. Again, yes it means in order to achieve proper performance on specific platforms, the data engineer MUST upskill their knowledge, and of course leverage join tables (which we call PITS and BRIDGES in the data vault landscape). Anything less and the data engineer is doing a disservice to the organization by "locking relationships in to the design" which forces re-engineering when thosee relationships change in the source systems. Here's the article: https://lnkd.in/ez8hSwfR

Here's a brief comparison of a Data Lake, Data Lakehouse, and Data Warehouse: ### Data Lake: - **Purpose**: Store large volumes of raw, unstructured, semi-structured, and structured data. - **Structure**: Typically schema-on-read, meaning data is ingested without a predefined schema. - **Flexibility**: Highly flexible, allowing for the storage of various data types. - **Use Cases**: Ideal for data exploration, big data analytics, and machine learning. - **Cost**: Often lower cost for storage but can require significant processing power to analyze data. ### Data Warehouse: - **Purpose**: Store processed and structured data optimized for querying and reporting. - **Structure**: Schema-on-write, meaning data is structured according to a predefined schema before being stored. - **Performance**: Optimized for read-heavy operations and complex queries. - **Use Cases**: Ideal for business intelligence, reporting, and operational analytics. - **Cost**: Typically higher storage costs due to optimized storage formats and performance tuning. ### Data Lakehouse: - **Purpose**: Combine the benefits of data lakes and data warehouses. - **Structure**: Supports both schema-on-read and schema-on-write, allowing for flexibility in data ingestion and processing. - **Performance**: Aims to provide performance and query optimization similar to data warehouses while maintaining the flexibility of data lakes. - **Use Cases**: Suitable for both exploratory data analysis and operational analytics, catering to a wide range of data processing needs. - **Cost**: Balances cost by leveraging inexpensive storage options from data lakes while providing performance optimizations from data warehouses. ### Summary: - **Data Lake**: Best for flexible storage of diverse data types, supporting exploratory and big data analytics. - **Data Warehouse**: Best for structured data storage, optimized for performance in querying and reporting. - **Data Lakehouse**: Best for combining flexibility with performance, supporting a wide range of analytics and data processing needs.

🌟 From Data Chaos to Strategic Insights: How Big Data Analytics is Reshaping Business Operations 🌟 [1] [3] 📊 In today's fast-paced and data-driven world, businesses are drowning in a sea of information. The sheer volume of data can hinder decision-making processes and impede business growth. But fear not, because big data analytics is here to save the day! 🚀 🔍 Big data analytics is the process of uncovering hidden patterns, correlations, market trends, and customer preferences from massive amounts of data. It helps organizations make informed business decisions and gain a competitive edge. But how can we transform this raw data into actionable insights? 🤔 💡 Data visualization is the key! By using data visualization tools, we can turn complex data into easy-to-understand visuals. This enables businesses to spot trends, patterns, and anomalies at a glance. It's like having a superpower that allows you to see the future of your business! 📈 [2] 🔥 But not all big data analytics tools are created equal. To truly harness the power of data, you need the right set of features. Here are the 12 must-have features for big data analytics tools: 1️⃣ Real-time analytics: Get instant insights to support on-the-fly decision-making. #RealTimeInsights 2️⃣ Scalability: Handle massive amounts of data without breaking a sweat. #ScalabilityMatters 3️⃣ Data integration: Combine data from multiple sources for a holistic view. #DataIntegration 4️⃣ Data cleansing: Ensure data accuracy and reliability. #DataCleansing 5️⃣ Advanced analytics: Uncover hidden patterns and correlations. #AdvancedAnalytics 6️⃣ Predictive modeling: Forecast future trends and outcomes. #PredictiveModeling 7️⃣ Machine learning: Let AI do the heavy lifting for you. #MachineLearning 8️⃣ Data visualization: Turn complex data into beautiful visuals. #DataVisualization 9️⃣ Collaboration: Share insights and collaborate with team members. #Collaboration 🔟 Security: Protect your data from unauthorized access. #DataSecurity 1️⃣1️⃣ User-friendly interface: Make data analysis accessible to everyone. #UserFriendly 1️⃣2️⃣ Customizability: Tailor the tool to fit your unique business needs. #Customizable 🌐 These features empower businesses to make data-driven decisions in real-time, even while using other applications. It's like having a personal data scientist by your side, guiding you towards success! 💪 🚀 So, embrace the power of big data analytics and transform your business operations. Say goodbye to data chaos and hello to strategic insights! 🌟 #BigDataAnalytics #DataVisualization #BusinessIntelligence #DataDrivenDecisions #DigitalTransformation References: [1] From Data Chaos to Strategic Insights: How Big Data Analytics is Reshaping Business Operations: https://lnkd.in/dxjYpVsi [2] Transforming raw data into actionable insights using data visualization: https://lnkd.in/dRdDHVvn [3] big data analytics: https://lnkd.in/ddtUf5x7