Data BI LLC’s Post

🚀 Optimizing Data Processing: Understanding Prediction Pushdown and Projection Pushdown🚀 In the world of data processing, efficiency is key. Two powerful techniques that help optimize queries are Prediction Pushdown and Projection Pushdown 🔍 Prediction Pushdown: Prediction pushdown involves pushing the filtering criteria (the WHERE clause) as close to the data source as possible. This means only the necessary data is retrieved, reducing the amount of data that needs to be processed later. Example: Imagine you have a large dataset of customer transactions in a data warehouse. Instead of retrieving all transactions and then filtering for those from the last month, prediction pushdown ensures only last month's transactions are fetched from the database, significantly speeding up the query. 🎯 Projection Pushdown: Projection pushdown optimizes queries by ensuring only the required columns are retrieved from the data source (the SELECT clause). This minimizes the amount of data transferred and processed. Example: If you need only the customer ID and purchase amount from a table containing 50 columns of transaction details, projection pushdown will ensure only these two columns are retrieved, reducing the data volume and increasing query performance. 💡 Real-Time Example: A retail company analyzing sales data to forecast inventory needs might use both techniques. By using a prediction pushdown, they can filter data for the current quarter's sales directly at the database level. Then, applying projection pushdown, they can select only the relevant columns like product ID and sales quantity. This streamlined process accelerates their data analytics workflow, enabling faster and more accurate decision-making. Embracing these techniques not only improves query performance but also enhances overall data processing efficiency, making them indispensable in modern data management. Tagging for better reach and follow them for Data related content TrendyTech Sumit Mittal Shashank Mishra 🇮🇳 Ankur Bhattacharya Ankur Ranjan Manish Kumar Sagar Prajapati Deepak Goyal Shubham Wadekar #DataProcessing #BigData #DataOptimization #DataEngineering #QueryOptimization #ETL #DataWarehousing #DatabaseManagement #TechTrends #SQLPerformance #Analytics #DataScience #CloudComputing #TechInnovation #BusinessIntelligence #DataStrategy #DataAnalytics #MachineLearning #RetailTech #DataManagement Understanding and leveraging these techniques can significantly boost your data operations. What challenges have you faced in optimizing data queries? Share your experiences in the comments! 🌐

If you run a data team there are four levers of business value you can pull: -> Data ingestion and quality: Getting the data and turning it into accurate, reliable sources that can used. Arguable if this is a direct lever of value but certainly it's the soil in which all other good things can grow. -> Data science: Building predictive models and automations that deliver internal efficiencies or direct customer value within your organization’s core product. -> Data as a product: Actually selling data or analytics to external customers. Not something that's possible for business but it's a lever which more are considering. -> Decision support: Good old fashioned BI or analytics. Providing operational clarity to the business and the data and analytics to drive decision making. Which of these can drive the most value for your business is going to be context dependent but my observation is the lever which is common to every business Decision Support is the also the one where there's the most uncertainty. Most people struggle to measure the value it brings and very few people think they do it well. Any levers I've missed? Does anyone disagree that Decision Support is both the most crucial lever and also the hardest to measure?

Data confusion got you down? Don't worry! We've got you covered. This post dives into the key differences between data lakes and data warehouses, helping you choose the right one for your business needs. Swipe to learn more! ➡️ Data Lake: Perfect for exploring unknown data, discovering hidden patterns, and feeding machine learning models. Stores ALL your data, structured and unstructured (raw format) More flexible and scalable but requires data engineering expertise. ➡️ Data Warehouse: Ideal for generating reports, analyzing trends, and supporting informed decision-making. Stores CLEAN and PROCESSED data specifically tailored for analysis. Optimized for fast querying and reporting, providing clear insights. Less flexible than data lakes, but easier to access and analyze. . . . Which one resonates with your business goals? . Follow @probyto_ai . #datalake #datawarehouse #bigdata #businessintelligence #analytics #datadrivendreams #warehouse #logistics #retail

📊 Understanding Discrete Data in Data Analysis 📊 In the realm of data Analysis , we often encounter different types of data, one of which is discrete data. Let's dive into what discrete data is and its significance in analysis! 🔍 What is Discrete Data? Discrete data refers to information that can only take specific values and cannot be measured or subdivided into smaller units. Examples include the number of products sold, the number of website visitors, or the number of cars in a parking lot. These data points are distinct and separate, often represented as whole numbers. 💡 Why is Discrete Data Important? Counting and Quantifying: Discrete data allows us to count and quantify items or occurrences, providing valuable insights into patterns and trends. Statistical Analysis: It forms the basis for various statistical analyses such as frequency distribution, probability distributions, and hypothesis testing. Decision Making: Understanding discrete data helps in making informed decisions, optimizing processes, and identifying opportunities for improvement. 📈 Applications of Discrete Data in Business Inventory Management: Tracking the number of products in stock. Customer Analytics: Analyzing the number of purchases per customer. Website Traffic: Monitoring the number of visitors per day. 🔗 Linking Discrete Data to Action By harnessing the power of discrete data, businesses can enhance their decision-making processes, improve resource allocation, and tailor strategies to meet specific objectives. It's a fundamental aspect of data science that drives insights and drives results. Let's continue exploring the diverse facets of data science together! Share your thoughts and experiences with discrete data in the comments below. #DataScience #Dataanalyst #IBM #Googledatascientist #Googledataanalyst #DiscreteData #Analytics #DataDrivenDecisions #bigdata #smalldata

DATA STORING IS NOT IMPORTANT????? Anyone who tells you that collecting and storing data is not important probably has never made data driven decisions based on facts and numbers “Numbers don’t Lie”. According to businesswire’s forecast organizations are set to spend $215 billion on business analytics solutions. If top organizations are willing to spend such amounts just to collect, organize, store and interprete these data then you should understand the important role that data plays in the continuous success of businesses. But you don’t have to be a Fortune 500 company to invest in business analytics. You can start small by collecting and storing your data in a simple database such as excel, create dashboards and visualizations using PowerBI to track key metrics and provide insights that can help stakeholders make data driven decisions. The image below is an example of a simple dashboard I created which helps an organization track thier Incidents over a period of time. This can be spread across things like inbound and outbound logistics audit, Scrap reporting, Financial audits and reports, supplier performance audits and so much more! COLLECT YOUR DATA!! STORE YOUR DATA!! INTERPRETE YOUR DATA AND GROW YOUR BUSINESS!!!! #dataanalytics #datascience #businessintelligence #Dax #continiousimprovement #Growth #Business #Supplychain

We build multiple custom analytics solution and help companies scale with data One of the solution brought way over 10m in pure revenue The others decreased churn and uncovered truth bottleenceks they weren't able to see before All this happened because they done these 3 steps 1) Deep data strategy Data strategy hence the name is assessing how to use data to align with your business goals Realizing what is priority what is not comparing to business value vs feasibility This can determine if the solution gone generated you ROI or be useless Only possible to do this is by deep expertise in business and data 2) Unification Breaking data silos and having data organizad, accurate in one central place called data warehouse.  This is crucial if want to generate insights and be data driven you need to see and get cohesive view. For example if want to determine a problem in churn you won’t just look at CS metrics but you will look at sales, marketing and finance. 3)Adoption & Iteration Business changes fast, thats why your data has to adapt to your needs. Not possible to scale to 10m with data maturity being at level 1. This is not a one time thing and its done. If you think it is you can try it and will probably stop using the solution or stop being data-driven in 2 weeks which will lead to not good things. Iterative approach sprint > 4) Actionable Insights All of these results were able to achieve because they had metrics, custom metrics presented and created in way that forced them to be better everywhere. To create actionable insights you need •Story - Why is this important?  •Context - What is main topic to focus on?  •KPI - Provides additional context Only able to achieve that by deeply understanding the business, user, industry and of course the data (technical part) If looking to fill out that gap covering both of those worlds-> Check aztela(dot)co

There’s plenty of busy work in the “left” side of Malcolm Hawker ‘s scale — but you can get all the left side right and end up with a technically right, effectively wrong technology artifact in the end. Or you can blame it on the people who do the “stuff on the right.” Your end users and stakeholders really don’t care about the left side of this unless the right stuff is happening — and even then, they still don’t really care. Data people, and developer/integrators might care about the left — but you can’t whine and complain that key end users, customers or business stakeholders “don’t see the value of our work” or that you “struggle to quantify the value of our data products” if you’re hanging out on the left. It’s on the right (I hope Malcolm) because it’s right :-) Yes I have strong opinions on this. #dataproducts

A great way to describe data products, in particular the benefits to both the producer and consumer.

Dear Professionals, Data analytics plays a crucial role in optimizing transportation costs by the follows:.... -#Identifying inefficiencies: Analyzing data to identify areas of waste and inefficiency in transportation operations. -#Predictive modeling: Using statistical models to forecast demand, capacity, and costs. -#Route optimization: Analyzing traffic patterns, road conditions, and weather to optimize routes. 1. Carrier selection: Evaluating carrier performance, rates, and service levels. 2. Load optimization: Maximizing cargo capacity and minimizing empty miles. 3. Cost allocation: Accurately assigning transportation costs to specific shipments or customers. 4. Benchmarking: Comparing transportation costs to industry benchmarks. 5. Supply chain visibility: Monitoring shipments in real-time. 6. Freight audit and payment: Ensuring accurate billing and payment. #Types of Data Used: 1. GPS and telematics data 2. Transportation management system (TMS) data 3. Enterprise resource planning (ERP) data 4. Weather and traffic data 5. Customer and supplier data 6. Carrier performance data 7. Freight audit and payment data #Analytics Techniques: 1. Descriptive analytics (reporting, dashboards) 2. Predictive analytics (forecasting, regression) 3. Prescriptive analytics (optimization, simulation) 4. Machine learning (clustering, decision trees) 5. Data mining (pattern discovery) #Benefits: 1. Reduced transportation costs (5-15% savings) 2. Improved supply chain efficiency 3. Enhanced customer satisfaction 4. Increased visibility and transparency 5. Better carrier relationships 6. Data-driven decision-making #Tools and Technologies: 1. Transportation management systems (TMS) 2. Data analytics platforms (e.g., Tableau, Power BI) 3. Business intelligence tools (e.g., SAP, Oracle) 4. Machine learning libraries (e.g., TensorFlow, PyTorch) 5. Cloud-based logistics platforms By leveraging data analytics, transportation professionals can make informed decisions, optimize operations, and reduce costs. However, I will appreciate more of expert contributions on this data analytics in logistics and distributions space. Would you like more information on data analytics in transportation or specific tools/techniques?