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Data Analytics News
Medium
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120
The Future of Financial Analysis: Key Trends for 2025 and Beyond
- Artificial Intelligence (AI) and machine learning (ML) are transforming how financial analysts forecast trends, detect anomalies, and automate workflows.
- Real-time data is changing how businesses approach financial planning and risk management.
- Sustainability and ESG investing are becoming critical factors in financial decision-making.
- Financial analysts will need to focus on strategic decision-making and develop skills in business strategy, automation tools, and effective communication.
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Cloudblog
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Cloud Composer 3: The next generation of data pipeline orchestration
- Cloud Composer 3 is the latest version of fully managed Apache Airflow service, offering enhanced data pipeline orchestration.
- Cloud Composer 3 introduces simplified networking, evergreen versioning, hidden infrastructure management, enhanced performance and reliability, per-task CPU and memory control, and strengthened security.
- The benefits of Cloud Composer 3 include increased productivity, reduced operational overhead, and faster time-to-value for data teams.
- To upgrade to Cloud Composer 3, visit the documentation for detailed instructions.
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Medium
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331
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The Evolution and Impact of Radio.
- Radio is a powerful communication medium that has revolutionized information and entertainment.
- Despite the emergence of digital media, radio remains relevant in connecting communities and serving as an important medium for emergency communication.
- The radio technology has its origins in the work of researchers such as James Clerk Maxwell and Guglilmo Marconi.
- The first licensed radio station, KDKA, started operating in 1920 and marked the rapid growth of radio technology.
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TechBullion
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5 Signs Your Forecasting Model Needs an Upgrade
- Outdated forecasting methods hinder business progress in supply chain management.
- Signs that your forecasting model needs an upgrade include inaccurate demand predictions and reliance on manual processes.
- Adapting to market changes requires real-time data and modern supply chain planning tools.
- Data fragmentation and insufficient inventory indicate the need for an upgraded forecasting model.
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Pymnts
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Real-Time Data Gap Puts Gaming Platforms on Competitive Notice
- Only 18% of gaming platforms track and have access to market data in real-time.
- Real-time data access is shockingly low in critical areas such as sales and performance tracking.
- Companies that close the data gap could gain market share, while those that don't risk stagnation.
- Outdated infrastructure, fragmentation, and cost barriers contribute to the lack of real-time data access in gaming platforms.
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Semiengineering
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Accelerating Digital Transformation With Tight Integration Of Manufacturing Data
- Semiconductor companies are integrating manufacturing data into their digital transformation programs to enhance operational efficiency and profitability.
- A new Manufacturing Hub has been introduced to integrate factory floor data with ERP and business planning solutions, addressing real-time product costing, supply chain visibility, and order status monitoring.
- Key solutions and capabilities of the Manufacturing Hub include real-time product costing, enhanced supply chain visibility, and improved order status and yield monitoring.
- The Manufacturing Hub simplifies integration between the shop floor and the top floor, ensuring accurate data transformation, improved supply chain predictability, and increased customer satisfaction.
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Medium
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340
Data Analy in Product Management
- Analytics has become foundational in product management, providing valuable insights for decision-making and growth opportunities.
- Different types of analytics in product management include descriptive, diagnostic, predictive, prescriptive, and behavioral analytics.
- Descriptive analytics focuses on historical data, diagnostic analytics delves into reasons behind patterns, and predictive analytics forecasts future trends.
- Prescriptive analytics offers recommendations for optimizing outcomes, while behavioral analytics helps refine user experiences.
- Analytics guides product managers throughout the product lifecycle, from ideation and design to launch, optimization, and growth strategies.
- Best practices for effective analytics in product management include defining clear KPIs, focusing on actionable insights, fostering a data-driven culture, and combining quantitative and qualitative data.
- Iteration through A/B testing and experimentation is essential for continuous improvement and refinement of product features and strategies.
- Analytics revolutionizes product management by enabling data-driven decisions, improving user satisfaction, and fueling growth in a competitive market.
- By leveraging diverse analytics tools, product managers can adapt to market changes confidently and deliver long-term value to users.
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Medium
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390
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When Tableau Met LookML: My Brain’s Identity Crisis
- Transitioning from Tableau to LookML can be challenging due to the shift in visual vs. logical reasoning.
- Tableau's visual processing contrasts with LookML's verbal reasoning, engaging different parts of the brain.
- The struggle with LookML, especially nested IF statements, led to breakthroughs like adopting CASE syntax.
- Initial confusion with LookML parameters evolved into understanding their dynamic effect on SQL logic based on user inputs.
- Understanding Level of detail (LOD) expressions in Tableau and LookML proved to be a significant revelation.
- Balancing table calculations between Tableau's add-ins and LookML's window functions required adapting to different approaches.
- Integrating Tableau's visualization with LookML's modeling can lead to more actionable insights and solutions.
- Being able to switch between visual and structural thinking enhances proficiency in both Tableau and LookML.
- Flexibility in adapting thinking processes based on context is essential in the data world.
- It's normal to feel challenged when transitioning between Tableau and LookML; perseverance leads to valuable skill development.
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Designveloper
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What is IoT Data Analytics? A Detailed Guide for Beginners
- IoT data analytics involves converting raw IoT data into useful insights for businesses by analyzing and processing the data collected from connected devices.
- The components behind IoT data analytics include sensors, gateways, and cloud platforms, which work together to detect, process, and analyze IoT data.
- There are four types of IoT data analytics: descriptive, diagnostic, predictive, and prescriptive analytics, each serving different purposes in analyzing IoT data.
- Businesses conduct IoT data analytics to reduce costs, improve operational efficiency, enhance customer experiences, drive innovation, and make informed decisions.
- Real-life applications of IoT data analytics include smart manufacturing, smart cities, healthcare, logistics, supply chain management, and precision farming.
- Key considerations before analyzing IoT data include defining clear business goals, ensuring data quality and integrity, and selecting the right IoT analytics platforms.
- The implementation of IoT data analytics involves five steps: data collection and integration, data processing and storage, data analysis, data visualization and actionable reporting, and establishing a feedback loop.
- By embracing IoT data analytics, businesses can optimize operations, improve efficiency, drive innovation, enhance customer experiences, and make data-driven decisions across various industries.
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Pymnts
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Levelpath Integrates Procurement Automation Capabilities With Coupa’s Spend Management Platform
- Levelpath announced integration to bring procurement automation capabilities to Coupa’s Spend Management Platform.
- The integration can help streamline operations and reduce procurement cycle times by up to 60%.
- Levelpath's platform is certified as a Coupa App Marketplace solution, validating its compatibility.
- The integration aims to optimize spend management processes and deliver greater value across organizations and platforms.
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22 Likes
Medium
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193
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Streamlit: The Data Science Superpower You Didn’t Know You Had
- Streamlit is an open-source Python library that allows easy building of interactive web apps for data science using just a few lines of code.
- No JavaScript, HTML, or CSS is required, making it a powerful tool for transforming data into interactive web apps.
- Compared to Jupyter Notebooks, Streamlit provides a full-fledged interactive app that responds to user inputs in real-time.
- Streamlit simplifies the process of creating interactive dashboards with customizable features like filters, sliders, and data uploads.
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Medium
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391
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From SQL to AI: My Journey in Data Analytics
- Over the years, data analytics has evolved from SQL queries and OLAP cubes to interactive dashboards in Tableau and Power BI.
- AI is now rewriting the rules in data analytics, going beyond visualizations to understanding and providing fast answers to questions.
- Previously, analytics required manual cleaning, modeling, and explaining the numbers, but with AI, data analysis has become more efficient and automated.
- AI enables users to ask questions and receive immediate insights, making data analysis easier and faster.
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Siliconangle
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Business intelligence startup Omni closes $69M funding round
- Business intelligence startup Omni has closed a $69 million funding round led by ICONIQ Growth.
- Omni's sales grew eightfold in the past year and the company generates nearly $10 million in annualized revenue.
- Omni provides a business intelligence platform that allows companies to turn their data into graphs and dashboards to monitor ad campaign performance.
- The funding will be used for product development, embedding graphs in other applications, and expanding the company's workforce.
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TechCrunch
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Omni raises $69M to design tools that help companies better analyze their data
- Omni, a business intelligence platform, has raised $69 million in a Series B funding round.
- The platform aims to simplify how organizations work with their data and offers ad-hoc data analysis, chart building, and visualization tools.
- Omni's customer base has grown to over 200 companies, and annual recurring revenue is nearly $10 million.
- The funding will help Omni focus on embedded analytics, spreadsheet functionality, and expand its team.
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TechBullion
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3.8k
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The Role of Data Analytics in Smarter Financial Forecasting
- Data analytics is revolutionizing financial forecasting by offering real-time insights, predictive modeling, and automated analysis.
- Analytics tools integrate real-time financial data, enabling businesses to create more precise forecasts and adapt to changing market conditions.
- Predictive modeling powered by AI and ML helps anticipate future cash flow, revenue fluctuations, and expense trends, reducing forecasting errors and enabling proactive decision-making.
- The use of data analytics in financial forecasting improves agility, detects anomalies, and enables companies to optimize resource allocation and make informed decisions for long-term goals.
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