#use custom chart data label values

[marcoluther]

How Crypto Exchange Development Services Are Transforming Digital Trading

In the ever-evolving digital economy, cryptocurrency trading has emerged as a revolutionary force, redefining the way assets are exchanged, stored, and valued. Central to this transformation is the rise of crypto exchange development services — specialized solutions designed to build secure, scalable, and feature-rich trading platforms. These services have not only enabled the growth of thousands of new exchanges worldwide but have also significantly enhanced the security, efficiency, and accessibility of digital trading.

This blog explores how crypto exchange development services are transforming the digital trading landscape and what it means for entrepreneurs, investors, and the global financial ecosystem.

The Foundation: What Are Crypto Exchange Development Services?

Crypto exchange development services refer to the end-to-end process of building digital platforms that facilitate the trading of cryptocurrencies like Bitcoin, Ethereum, and other tokens. These services typically include:

Custom exchange platform development (CEX/DEX/hybrid)

Integration of crypto wallets and payment gateways

Smart contract development and auditing

Liquidity management and order book systems

Security infrastructure (2FA, KYC, AML, encryption)

Admin dashboards and user interface design

Ongoing maintenance and upgrades

With these services, entrepreneurs and businesses can launch exchanges tailored to their target markets and business models without starting from scratch.

Enabling Decentralization and Financial Inclusion

One of the most profound impacts of crypto exchange development services is the democratization of financial access. In traditional finance, intermediaries like banks, brokers, and clearinghouses control access to markets. Crypto exchanges — especially decentralized ones (DEXs) — remove these middlemen, giving users direct control over their assets.

This decentralized architecture allows:

Unbanked populations to engage in digital finance using only internet-connected devices.

Lower fees and faster transactions by eliminating institutional overhead.

Peer-to-peer (P2P) trading models that foster trustless exchanges.

In this way, crypto exchange development is not just a technological advancement but a movement toward global financial inclusivity.

Customization and Innovation at Scale

Today’s trading environment demands tailored experiences. From institutional investors to casual traders, users seek platforms that meet their specific needs — whether it's low latency, mobile-first trading, NFT integrations, or staking features.

Crypto exchange developers respond to this need by offering highly customizable platforms. This includes:

White-label crypto exchange software for faster deployment

Cross-platform compatibility (web, Android, iOS)

Multi-currency and multi-language support

Advanced trading tools like AI-powered bots, charting libraries, and real-time analytics

DeFi integrations such as yield farming, lending, and liquidity pools

This scalability and adaptability are accelerating the spread of digital trading across industries and geographies.

Security as a Cornerstone

Security concerns have long plagued the crypto space. Hacks, scams, and data breaches have cost billions in losses. As a result, security has become a core component of modern crypto exchange development services.

Top-tier developers implement a multi-layered security strategy, including:

End-to-end encryption

Multi-signature wallets

Cold wallet storage for user funds

KYC (Know Your Customer) and AML (Anti-Money Laundering) compliance tools

DDoS protection and regular penetration testing

Smart contract auditing to eliminate vulnerabilities

By embedding robust security from the start, developers foster trust and regulatory compliance — both essential for mainstream adoption.

Driving Liquidity and Market Depth

A thriving exchange needs liquidity — the ability to buy or sell assets without causing major price swings. Without liquidity, even the most well-designed platform can falter.

Crypto exchange development services often incorporate liquidity aggregation tools and market-making bots that automatically balance order books and attract volume. Some developers offer partnerships or integrations with global liquidity providers to help new exchanges hit the ground running.

Increased liquidity leads to:

Tighter spreads and more accurate pricing

Higher user retention and trading volume

Improved scalability and profitability for platform owners

Thus, liquidity management is not just a technical issue — it’s a critical success factor.

Regulatory Alignment and Compliance Support

As governments and financial authorities increase scrutiny on crypto operations, compliance has become non-negotiable. Crypto exchange development services now embed tools and workflows to help businesses stay ahead of regulatory requirements.

This includes:

Automated KYC/AML integrations

Geo-restriction systems for regulatory segmentation

Tax reporting and transaction auditing modules

Support for GDPR and data privacy protocols

By building compliance into the DNA of the exchange, developers help reduce legal risks and make exchanges more attractive to institutional players and regulators alike.

Speeding Up Time-to-Market

In the fast-moving world of crypto, time is money. A project that takes 12 months to launch may miss the market window entirely. To address this, many developers offer white-label crypto exchange solutions — pre-built platforms that can be customized, branded, and launched in a fraction of the time.

Benefits of white-label solutions include:

Faster deployment (as little as a few weeks)

Reduced development costs

Battle-tested performance and security

Ongoing technical support and upgrades

This has dramatically lowered the barrier to entry for startups, allowing more players to enter and compete in the digital trading arena.

Paving the Way for the Future of Finance

As blockchain and Web3 evolve, crypto exchange development services are integrating new trends and technologies:

AI and machine learning for predictive analytics and automated trading

Tokenization of real-world assets (stocks, real estate, art)

NFT trading platforms and marketplaces

Cross-chain bridges for asset interoperability

Modular architectures for DAO-based governance

These advancements are helping shape the next generation of financial infrastructure — open, programmable, and community-driven.

Final Thoughts

Crypto exchange development services are not merely about building trading platforms — they are about redefining how the world interacts with value. From empowering the unbanked to enabling new business models and promoting financial sovereignty, these services are at the heart of the blockchain revolution.

As crypto adoption continues to grow, the demand for sophisticated, secure, and scalable exchange platforms will only increase. Businesses and entrepreneurs looking to stake their claim in the digital economy would do well to understand the transformative potential of these services — and partner with the right development team to bring their vision to life.

In a digital age where control, access, and innovation matter more than ever, crypto exchange development services are building the bridges to the future of trading.

[korshubudemycoursesblog]

Transform Your Skills in 2025: Master Data Visualization with Tableau & Python (2 Courses in 1!)

When it comes to storytelling with data in 2025, two names continue to dominate the landscape: Tableau and Python. If you’re looking to build powerful dashboards, tell data-driven stories, and break into one of the most in-demand fields today, this is your chance.

But instead of bouncing between platforms and tutorials, what if you could master both tools in a single, streamlined journey?

That’s exactly what the 2025 Data Visualization in Tableau & Python (2 Courses in 1!) offers—an all-in-one course designed to take you from data novice to confident visual storyteller.

Let’s dive into why this course is creating buzz, how it’s structured, and why learning Tableau and Python together is a smart move in today’s data-first world.

Why Data Visualization Is a Must-Have Skill in 2025

We’re drowning in data—from social media metrics and customer feedback to financial reports and operational stats. But raw data means nothing unless you can make sense of it.

That’s where data visualization steps in. It’s not just about charts and graphs—it’s about revealing patterns, trends, and outliers that inform smarter decisions.

Whether you're working in marketing, finance, logistics, healthcare, or even education, communicating data clearly is no longer optional. It’s expected.

And if you can master both Tableau—a drag-and-drop analytics platform—and Python—a powerhouse for automation and advanced analysis—you’re giving yourself a massive career edge.

Meet the 2-in-1 Power Course: Tableau + Python

The 2025 Data Visualization in Tableau & Python (2 Courses in 1!) is exactly what it sounds like: a double-feature course that delivers hands-on training in two of the most important tools in data science today.

Instead of paying for two separate learning paths (which could cost you more time and money), you’ll:

Learn Tableau from scratch and create interactive dashboards

Dive into Python programming for data visualization

Understand how to tell compelling data stories using both tools

Build real-world projects that you can show off to employers or clients

All in one single course.

Who Should Take This Course?

This course is ideal for:

Beginners who want a solid foundation in both Tableau and Python

Data enthusiasts who want to transition into analytics roles

Marketing and business professionals who need to understand KPIs visually

Freelancers and consultants looking to offer data services

Students and job seekers trying to build a strong data portfolio

No prior coding or Tableau experience? No problem. Everything is taught step-by-step with real-world examples.

What You'll Learn: Inside the Course

Let’s break down what you’ll actually get inside this 2-in-1 course:

✅ Tableau Module Highlights:

Tableau installation and dashboard interface

Connecting to various data sources (Excel, CSV, SQL)

Creating bar charts, pie charts, line charts, maps, and more

Advanced dashboard design techniques

Parameters, filters, calculations, and forecasting

Publishing and sharing interactive dashboards

By the end of this section, you’ll be comfortable using Tableau to tell stories that executives understand and act on.

✅ Python Visualization Module Highlights:

Python basics: data types, loops, functions

Data analysis with Pandas and NumPy

Visualization libraries like Matplotlib and Seaborn

Building statistical plots, heatmaps, scatterplots, and histograms

Customizing charts with color, labels, legends, and annotations

Automating visual reports

Even if you’ve never coded before, you’ll walk away confident enough to build beautiful, programmatically-generated visualizations with Python.

The Real-World Value: Why This Course Stands Out

We all know there’s no shortage of online courses today. But what makes this one worth your time?

🌟 1. Two for the Price of One

Most courses focus on either Tableau or Python. This one merges the best of both worlds, giving you more for your time and money.

🌟 2. Hands-On Learning

You won’t just be watching slides or lectures—you’ll be working with real data sets, solving real problems, and building real projects.

🌟 3. Resume-Boosting Portfolio

From the Tableau dashboards to the Python charts, everything you build can be used to show potential employers what you’re capable of.

🌟 4. Taught by Experts

This course is created by instructors who understand both tools deeply and can explain things clearly—no confusing jargon, no filler.

🌟 5. Constantly Updated

As Tableau and Python evolve, so does this course. That means you’re always learning the latest and greatest features, not outdated content.

Why Learn Both Tableau and Python?

Some people ask, “Isn’t one enough?”

Here’s the thing: they serve different purposes, but together, they’re unstoppable.

Tableau is for quick, intuitive dashboarding.

Drag-and-drop interface

Ideal for business users

Great for presentations and client reporting

Python is for flexibility and scale.

You can clean, manipulate, and transform data

Build custom visuals not possible in Tableau

Automate workflows and scale up for big data

By learning both, you cover all your bases. You’re not limited to just visuals—you become a full-spectrum data storyteller.

Data Careers in 2025: What This Course Prepares You For

The demand for data professionals continues to skyrocket. Here’s how this course sets you up for success in various career paths: RoleHow This Course HelpsData AnalystBuild dashboards, analyze trends, present insightsBusiness Intelligence AnalystCombine data from multiple sources, visualize it for execsData Scientist (Junior)Analyze data with Python, visualize with TableauMarketing AnalystUse Tableau for campaign reporting, Python for A/B analysisFreelancer/ConsultantOffer complete data storytelling services to clients

This course can be a launchpad—whether you want to get hired, switch careers, or start your own analytics agency.

Real Projects = Real Confidence

What sets this course apart is the project-based learning approach. You'll create:

Sales dashboards

Market trend analysis charts

Customer segmentation visuals

Time-series forecasts

Custom visual stories using Python

Each project is more than just a tutorial—it mimics real-world scenarios you’ll face on the job.

Flexible, Affordable, and Beginner-Friendly

Best part? You can learn at your own pace. No deadlines, no pressure.

You don’t need to buy expensive software. Tableau Public is free, and Python tools like Jupyter, Pandas, and Matplotlib are open-source.

Plus, with lifetime access, you can revisit any lesson whenever you want—even years down the road.

And all of this is available at a price that’s far less than a bootcamp or university course.

Still Not Sure? Here's What Past Learners Say

“I had zero experience with Tableau or Python. After this course, I built my own dashboard and presented it to my team. They were blown away!” – Rajiv, Product Analyst

“Perfect combo of theory and practice. Python sections were especially helpful for automating reports I used to make manually.” – Sarah, Marketing Manager

“Loved how everything was explained so simply. Highly recommend to anyone trying to upskill in data.” – Alex, Freelancer

Final Thoughts: Your Data Career Starts Now

You don’t need to be a programmer or a math wizard to master data visualization. You just need the right guidance, a solid roadmap, and the willingness to practice.

With the 2025 Data Visualization in Tableau & Python (2 Courses in 1!), you’re getting all of that—and more.

This is your chance to stand out in a crowded job market, speak the language of data confidently, and unlock doors in tech, business, healthcare, finance, and beyond.

Don’t let the data wave pass you by—ride it with the skills that matter in 2025 and beyond.

[whatsdash]

Benefits of Automatic Client Reporting: Save Time and Impress Clients

In the fast-paced world of digital marketing and data analytics, efficiency and client satisfaction are paramount. One of the most effective ways to achieve both is by leveraging automatic client reporting. This powerful tool can transform how agencies and businesses manage their reporting process—saving hours of manual work and enhancing the client experience.

In this article, we’ll explore the top benefits of automatic client reporting, how it helps you save time, and how it enables you to impress clients with consistent, professional reports.

1. Save Hours of Manual Work

Creating client reports manually is a time-consuming task. From gathering data from multiple platforms to formatting and organizing the content, traditional reporting can eat up valuable hours each week. With this tool, you can:

Pull data automatically from platforms like Google Analytics, Facebook Ads, LinkedIn, and more.

Generate ready-to-send reports on a schedule (daily, weekly, or monthly).

Eliminate repetitive copy-paste tasks and reduce human error.

Result: Your team gains more time to focus on strategic initiatives instead of tedious reporting tasks.

2. Deliver Reports Faster and More Consistently

Clients expect timely updates on their campaign performance. Delayed or inconsistent reporting can lead to confusion or dissatisfaction. Automatic reporting tools ensure:

Reports are sent on time, every time.

You maintain a consistent format that’s easy for clients to understand.

No risk of forgetting to send updates or missing a report deadline.

Result: You build trust and reliability with your clients, keeping them informed and confident in your services.

3. Enhance Report Accuracy

Manual reporting increases the risk of errors—whether it’s a wrong data point, a mislabeled chart, or an outdated figure. Automation reduces these risks by:

Pulling real-time data directly from integrated platforms.

Keeping dashboards and metrics up-to-date.

Minimizing human involvement in data entry.

Result: Clients receive accurate insights, and you avoid embarrassing corrections or misunderstandings.

4. Customize Reports to Match Client Needs

Automatic reporting doesn’t mean one-size-fits-all. Modern reporting tools allow for full customization so you can:

Tailor dashboards and KPIs to match each client’s goals.

Use white-label branding to present reports with your (or the client’s) logo and color scheme.

Include insights and recommendations directly in the report.

Result: Clients get reports that feel personalized and valuable, not generic and templated.

5. Impress Clients with Professional Presentation

First impressions matter, and so do recurring ones. An automatic reporting platform ensures your reports:

Are visually appealing with charts, graphs, and data visualizations.

Highlight wins and opportunities clearly.

Demonstrate your expertise and the value you provide.

Result: Clients see you as organized, data-driven, and highly professional.

6. Scale Your Agency with Ease

As your client list grows, manual reporting becomes unsustainable. Automatic client reporting enables scalability by:

Letting you create reusable report templates.

Supporting unlimited or multiple client accounts in one platform.

Reducing the need for additional staff to manage reporting.

Result: You grow your agency without growing your overhead.

Final Thoughts

Automatic client reporting is no longer a luxury—it’s a necessity for modern agencies and consultants who want to streamline operations and exceed client expectations. By automating your reports, you save time, ensure accuracy, and deliver a polished, consistent client experience that sets you apart from the competition.

If you’re looking to boost productivity and impress your clients, investing in an automatic reporting solution is one of the smartest moves you can make.

[mediamonarchy]

https://mediamonarchy.com/wp-content/uploads/2025/05/20250521_MorningMonarchy.mp3 Download MP3 Solar farm kill switches, Morrisey’s executive order and mailing weed + this day in history w/dining with a bunch of mannequins and our song of the day by Jesse Welles on your #MorningMonarchy for May 21, 2025. Notes/Links: ♬ Now You Can Stream Monarchy Country 24/7! ♬ https://mediamonarchy.com/country/ Make one-off PayPal donations of any size to the Monarchy https://www.paypal.com/paypalme/mediamonarchy Last Monday Hangout #29 // Mon May 26, 2025 @ 9AM MT https://discord.com/events/311625265627529216/1373118975126409286 Coinbase says thieves stole user data and tried to extort $20M; The company indicated that some customers had been tricked and their money stolen https://www.nbcnews.com/tech/security/coinbase-says-thieves-stole-user-data-tried-extort-20m-rcna207017 Bitcoin Inches Closer to All-Time High—Here’s What’s Driving Its Price; Bitcoin nears record high as ETF inflows, macro uncertainty, and trade-driven inflation risks draw investor interest. https://decrypt.co/320772/bitcoin-inches-closer-to-all-time-high-heres-whats-driving-its-price AM 860 The Answer KPAM – Portland, OR https://theanswerportland.com/listenlive Scientists mimicking the Big Bang accidentally turn lead into gold; Physicists have made an unexpected breakthrough https://www.independent.co.uk/news/science/large-hadron-collider-lead-gold-b2749928.html Chinese ‘kill switches’ found in US solar farms https://www.yahoo.com/news/chinese-kill-switches-found-us-122555351.html Video: Prepare for Lights Out! ROGUE Devices Found Hidden In Chinese Solar Panels Could “Destroy The Grid” (Audio) https://www.youtube.com/watch?v=V0S8tgu-flk Black Keys – “When the Lights Go Out” (Audio) ‘Rubber Factory’ Fat Possum – September 7, 2004 *Black Keys – “When the Lights Go Out” https://www.youtube.com/watch?v=07eUUPih34Y // https://en.wikipedia.org/wiki/Rubber_Factory // Their last full-length for an indie before heading into the gaping Warner maw, the Ohio boys still bring the thickfreakness on this album I added to rotation at the college radio station. ‘Rubber Factory’ was their third studio album and it was self-produced by the band, recorded in an abandoned tire-manufacturing factory in the group’s hometown of Akron, Ohio. ‘Rubber Factory’ received positive reviews and was the band’s first album to chart on the Billboard 200 in the United States, reaching number 143.* Dahboo’s music https://youtube.com/playlist?list=PLAwob-06lqjWEclOb3IKLL90QX0GTjpmL speaking of gold, Adam is told by his maker in the ole bible about gold in chapter 2 of genesis, kind of an odd thing to bring up fore any peoples or trade or anything like that especially when there was enough of everything for everyone https://biblehub.com/genesis/2-11.htm // https://biblehub.com/genesis/2-12.htm In what world is someone like a private person apparently messing with the food chain? we have labelling and laws, restrictions and inspections: Yet Billy gets a free pass to f*ck everything up and kill people. How is this possible? https://x.com/RBBL_ROUZR/status/1922528266415718589 Bill Gates Unveils New mRNA-Lipid Nanoparticle Jabs for Billions of Cows https://thepeoplesvoice.tv/bill-gates-unveils-new-mrna-lipid-nanoparticle-jabs-for-billions-of-cows/ H5 influenza virus mRNA-lipid nanoparticle (LNP) vaccination elicits adaptive immune responses in Holstein calves (May 6, 2025) https://www.biorxiv.org/content/10.1101/2025.05.01.651548v3.full FDA Says No Licenses for COVID-19 Vaccines for Many Americans Without Trial Data; The new policy ‘balances competing values,’ agency leaders said. https://archive.is/1wybJ Morrisey’s executive order on immunization exemptions is wrong on multiple levels By Hoppy Kercheval https://wvmetronews.com/2025/05/08/morriseys-executive-order-on-immunization-exemptions-is-wrong-on-multiple-levels/ Video: Funniest president ever – “Nobody ever asks me what flavor ice cream I’m eating” (Audio) h...

[karthikumar1212]

Unlock Business Insights Effortlessly with Power BI: The Ultimate Guide

In today’s data-driven world, businesses rely heavily on technology that transforms raw data into meaningful insights. Power BI is a standout tool, especially for business analysts, helping them visualize data, generate reports, and make smart decisions. This guide will introduce you to Power BI, its benefits for analysts, and key skills you’ll gain from a Power BI Course in Bangalore like creating dynamic pie charts, connecting multiple data sources, building effective reports, and overcoming challenges to increase productivity.

What Makes Power BI Essential for Business Analysts?

Power BI is a robust business intelligence tool that empowers professionals to get the most out of their data. Business analysts rely on it for collecting, analyzing, and visualizing data to help organizations make informed decisions and operate efficiently.

Business analysts are vital to companies because they deliver insights that drive strategic choices. With over 6 million users worldwide and trusted by 97% of Fortune 500 companies, Power BI is a go-to platform for data analysis.

Top 5 Power BI Features You Should Know

Data Transformation & Modeling: Power BI allows you to import data from various sources—databases, cloud services, Excel, and more. It lets you shape and refine raw data to fit your analysis needs, much like a sculptor crafting a masterpiece.

Interactive Visualizations: Creating interactive charts, tables, and graphs is simple with Power BI. These tools enable you to explore data from different angles, making it easier for analysts to communicate complex insights effectively.

Data Integration: Data often comes from diverse sources. Power BI lets you merge all that data into a single report or dashboard, simplifying your analysis with our Power Bi Certification.

Data Analysis Expressions (DAX): DAX is Power BI’s powerful formula language that helps you create custom calculations and metrics, giving you deeper insights through advanced data manipulation.

Collaboration & Sharing: Power BI makes it easy to publish reports and share findings with your team or stakeholders, ensuring everyone is aligned when making data-driven decisions.

Creating Dynamic Pie Charts for Better Visual Analysis

According to the Wharton School of Business, effective data visualization can reduce meeting times by 24%. Pie charts are especially useful for presenting numerical data clearly. Power BI offers an intuitive way to create interactive pie charts that help analysts highlight key findings with ease.

With customizable colors, labels, and interactive features, these pie charts can be tailored to suit any audience or presentation style.

Conclusion

At Arreoblue, we specialize in delivering quick and efficient data solutions to help businesses become truly data-driven. Focusing on sectors like retail, manufacturing, and finance, we provide the foundation for success by delivering fast results and measurable value.

[jcmarchi]

Top 10 AI Tools for Embedded Analytics and Reporting (May 2025)

New Post has been published on https://thedigitalinsider.com/top-10-ai-tools-for-embedded-analytics-and-reporting-may-2025/

Top 10 AI Tools for Embedded Analytics and Reporting (May 2025)

Embedded analytics refers to integrating interactive dashboards, reports, and AI-driven data insights directly into applications or workflows. This approach lets users access analytics in context without switching to a separate BI tool. It’s a rapidly growing market – valued around $20 billion in 2024 and projected to reach $75 billion by 2032 (18% CAGR).

Organizations are embracing embedded analytics to empower end-users with real-time information. These trends are fueled by demand for self-service data access and AI features like natural language queries and automated insights, which make analytics more accessible.

Below we review top tools that provide AI-powered embedded analytics and reporting. Each tool includes an overview, key pros and cons, and a breakdown of pricing tiers.

AI Tools for Embedded Analytics and Reporting (Comparison Table)

AI Tool Best For Price Features Explo Turnkey, white-label SaaS dashboards Free internal · embed from $795/mo No-code builder, Explo AI NLQ, SOC 2/HIPAA ThoughtSpot Google-style NL search for data in apps Dev trial free · usage-based quote SpotIQ AI insights, search & Liveboards embed Tableau Embedded Pixel-perfect visuals & broad connectors $12–70/user/mo Pulse AI summaries, drag-drop viz, JS API Power BI Embedded Azure-centric, cost-efficient scaling A1 capacity from ~$735/mo NL Q&A, AutoML visuals, REST/JS SDK Looker Governed metrics & Google Cloud synergy Custom (≈$120k+/yr) LookML model, secure embed SDK, BigQuery native Sisense OEMs needing deep white-label control Starter ≈$10k/yr · Cloud ≈$21k/yr ElastiCube in-chip, NLQ, full REST/JS APIs Qlik Associative, real-time data exploration $200–2,750/mo (capacity-based) Associative engine, Insight Advisor AI, Nebula.js Domo Everywhere Cloud BI with built-in ETL & sharing From ~$3k/mo (quote) 500+ connectors, alerts, credit-based scaling Yellowfin BI Data storytelling & flexible OEM pricing Custom (≈$15k+/yr) Stories, Signals AI alerts, multi-tenant Mode Analytics SQL/Python notebooks to embedded reports Free · Pro ≈$6k/yr Notebooks, API embed, Visual Explorer

(Source: Explo)

Explo is an embedded analytics platform designed for product and engineering teams to quickly add customer-facing dashboards and reports to their apps. It offers a no-code interface for creating interactive charts and supports white-labeled embedding, so the analytics blend into your product’s UI.

Explo focuses on self-service: end-users can explore data and even build ad hoc reports without needing developer intervention. A standout feature is Explo AI, a generative AI capability that lets users ask free-form questions and get back relevant charts automatically.

This makes data exploration as easy as typing a query in natural language. Explo integrates with many databases and is built to scale from startup use cases to enterprise deployments (it’s SOC II, GDPR, and HIPAA compliant for security).

Pros and Cons

Drag-and-drop dashboards—embed in minutes

Generative AI (Explo AI) for NLQ insights

Full white-label + SOC 2 / HIPAA compliance

Young platform; smaller community

Costs rise with large end-user counts

Cloud-only; no on-prem deployment

Pricing: (Monthly subscriptions – USD)

Launch – Free: Internal BI use only; unlimited internal users/dashboards.

Growth – from $795/month: For embedding in apps; includes 3 embedded dashboards, 25 customer accounts.

Pro – from $2,195/month: Advanced embedding; unlimited dashboards, full white-label, scales with usage.

Enterprise – Custom: Custom pricing for large scale deployments; includes priority support, SSO, custom features.

Visit Explo →

ThoughtSpot is an AI-driven analytics platform renowned for its search-based interface. With ThoughtSpot’s embedded analytics, users can type natural language queries (or use voice) to explore data and instantly get visual answers.

This makes analytics accessible to non-technical users – essentially a Google-like experience for your business data. ThoughtSpot’s in-memory engine handles large data volumes, and its AI engine (SpotIQ) automatically finds insights and anomalies.

For embedding, ThoughtSpot provides low-code components and robust REST APIs/SDKs to integrate interactive Liveboards (dashboards) or even just the search bar into applications. It’s popular for customer-facing analytics in apps where end-users need ad-hoc querying ability.

Businesses in retail, finance, and healthcare use ThoughtSpot to let frontline employees and customers ask data questions on the fly. The platform emphasizes ease-of-use and fast deployment, though it also offers enterprise features like row-level security and scalability across cloud data warehouses.

Pros and Cons

Google-style NL search for data

SpotIQ AI auto-surfaces trends

Embeds dashboards, charts, or just the search bar

Enterprise-grade pricing for SMBs

Limited advanced data modeling

Setup needs schema indexing expertise

Pricing: (Tiered, with consumption-based licensing – USD)

Essentials – $1,250/month (billed annually): For larger deployments; increased data capacity and features.

ThoughtSpot Pro: Custom quote. Full embedding capabilities for customer-facing apps (up to ~500 million data rows).

ThoughtSpot Enterprise: Custom quote. Unlimited data scale and enterprise SLA. Includes multi-tenant support, advanced security, etc.

Visit ThoughtSpot →

Tableau (part of Salesforce) is a leading BI platform known for its powerful visualization and dashboarding capabilities. Tableau Embedded Analytics allows organizations to integrate Tableau’s interactive charts and reports into their own applications or websites.

Developers can embed Tableau dashboards via iFrames or using the JavaScript API, enabling rich data visuals and filtering in-app. Tableau’s strength lies in its breadth of out-of-the-box visuals, drag-and-drop ease for creating dashboards, and a large user community.

It also has introduced AI features – for example, in 2024 Salesforce announced Tableau Pulse, which uses generative AI to deliver automated insights and natural language summaries to users. This augments embedded dashboards with proactive explanations.

Tableau works with a wide range of data sources and offers live or in-memory data connectivity, ensuring that embedded content can display up-to-date info. It’s well-suited for both internal embedded use (e.g. within an enterprise portal) and external customer-facing analytics, though licensing cost and infrastructure must be planned accordingly.

Pros and Cons

Market-leading visual library

New “Pulse” AI summaries & NLQ

Broad data connectors + massive community

License cost balloons at scale

Requires Tableau Server/Cloud infrastructure

Styling customization via JS API only

Pricing: (Subscription per user, with role-based tiers – USD)

Creator – $70 per user/month: Full authoring license (data prep, dashboard creation). Needed for developers building embedded dashboards.

Explorer – $35 per user/month: For users who explore and edit limited content. Suitable for internal power users interacting with embedded reports.

Viewer – $12 per user/month: Read-only access to view dashboards. For end viewers of embedded analytics.

Visit Tableau →

Microsoft Power BI is a widely-used BI suite, and Power BI Embedded refers to the Azure service and APIs that let you embed Power BI visuals into custom applications. This is attractive for developers building customer-facing analytics, as it combines Power BI’s robust features (interactive reports, AI visuals, natural language Q&A, etc.) with flexible embedding options.

You can embed full reports or individual tiles, control them via REST API, and apply row-level security for multi-tenant scenarios. Power BI’s strengths include tight integration with the Microsoft ecosystem (Azure, Office 365), strong data modeling (via Power BI Desktop), and growing AI capabilities (e.g. the Q&A visual that allows users to ask questions in plain English).

Pros and Cons

Rich BI + AI visuals (NL Q&A, AutoML)

Azure capacity pricing scales to any user base

Deep Microsoft ecosystem integration

Initial setup can be complex (capacities, RLS)

Devs need Power BI Pro licenses

Some portal features absent in embeds

Pricing: (Azure capacity-based or per-user – USD)

Power BI Pro – $14/user/month: Enables creating and sharing reports. Required for developers and any internal users of embedded content.

Power BI Premium Per User – $24/user/month: Enhanced features (AI, larger datasets) on a per-user basis. Useful if a small number of users need premium capabilities instead of a full capacity.

Power BI Embedded (A SKUs) – From ~$735/month for A1 capacity (3 GB RAM, 1 v-core). Scales up to ~$23,500/month for A6 (100 GB, 32 cores) for high-end needs. Billed hourly via Azure, with scale-out options.

Visit Power BI →

Looker is a modern analytics platform now part of Google Cloud. It is known for its unique data modeling layer, LookML, which lets data teams define business metrics and logic centrally.

For embedded analytics, Looker provides a robust solution: you can embed interactive dashboards or exploratory data tables in applications, leveraging the same Looker backend. One of Looker’s core strengths is consistency – because of LookML, all users (and embedded views) use trusted data definitions, avoiding mismatched metrics.

Looker also excels at integrations: it connects natively to cloud databases (BigQuery, Snowflake, etc.), and because it’s in the Google ecosystem, it integrates with Google Cloud services (permissions, AI/ML via BigQuery, etc.).

Pros and Cons

LookML enforces single source of truth

Secure embed SDK + full theming

Tight BigQuery & Google AI integration

Premium six-figure pricing common

Steep LookML learning curve

Visuals less flashy than Tableau/Power BI

Pricing: (Custom quotes via sales; example figures)

Visit Looker →

Sisense is a full-stack BI and analytics platform with a strong focus on embedded analytics use cases. It enables companies to infuse analytics into their products via flexible APIs or web components, and even allows building custom analytic apps.

Sisense is known for its ElastiCube in-chip memory technology, which can mash up data from multiple sources and deliver fast performance for dashboards. In recent years, Sisense has incorporated AI features (e.g. NLQ, automated insights) to stay competitive.

A key advantage of Sisense is its ability to be fully white-labeled and its OEM-friendly licensing, which is why many SaaS providers choose it to power their in-app analytics. It offers both cloud and on-premises deployment options, catering to different security requirements.

Sisense also provides a range of customization options: you can embed entire dashboards or individual widgets, and use their JavaScript library to deeply customize look and feel. It’s suited for organizations that need an end-to-end solution – from data preparation to visualization – specifically tailored for embedding in external applications.

Pros and Cons

ElastiCube fuses data fast in-memory

White-label OEM-friendly APIs

AI alerts & NLQ for end-users

UI learning curve for new users

Quote-based pricing can be steep

Advanced setup often needs dev resources

Pricing: (Annual license, quote-based – USD)

Starter (Self-Hosted) – Starts around $10,000/year for a small deployment (few users, basic features). This would typically be an on-prem license for internal BI or limited OEM use.

Cloud (SaaS) Starter – ~$21,000/year for ~5 users on Sisense Cloud (cloud hosting carries ~2× premium over self-host).

Growth/Enterprise OEM – Costs scale significantly with usage; mid-range deployments often range $50K-$100K+ per year. Large enterprise deals can reach several hundred thousand or more if there are very high numbers of end-users.

Visit Sisense →

Qlik is a long-time leader in BI, offering Qlik Sense as its modern analytics platform. Qlik’s embedded analytics capabilities allow you to integrate its associative data engine and rich visuals into other applications.

Qlik’s differentiator is its Associative Engine: users can freely explore data associations (making selections across any fields) and the engine instantly updates all charts to reflect those selections, revealing hidden insights.

In an embedded scenario, this means end-users can get powerful interactive exploration, not just static filtered views. Qlik provides APIs (Capability API, Nebula.js library, etc.) to embed charts or even build fully custom analytics experiences on top of its engine. It also supports standard embed via iframes or mashups.

Qlik has incorporated AI as well – the Insight Advisor can generate insights or chart suggestions automatically. For developers, Qlik’s platform is quite robust: you can script data transformations in its load script, use its security rules for multi-tenant setups, and even embed Qlik into mobile apps.

Pros and Cons

Associative engine enables free exploration

Fast in-memory performance for big data

Robust APIs + Insight Advisor AI

Unique scripting → higher learning curve

Enterprise-level pricing

UI can feel dated without theming

Pricing: (USD)

Starter – $200 / month (billed annually): Includes 10 users + 25 GB “data for analysis.” No extra data add-ons available.

Standard – $825 / month: Starts with 25 GB; buy more capacity in 25 GB blocks. Unlimited user access.

Premium – $2,750 / month: Starts with 50 GB, adds AI/ML, public/anonymous access, larger app sizes (10 GB).

Enterprise – Custom quote: Begins at 250 GB; supports larger app sizes (up to 40 GB), multi-region tenants, expanded AI/automation quotas.

Visit Qlik →

Domo is a cloud-first business intelligence platform, and Domo Everywhere is its embedded analytics solution aimed at sharing Domo’s dashboards outside the core Domo environment. With Domo Everywhere, companies can distribute interactive dashboards to customers or partners via embed codes or public links, while still managing everything from the central Domo instance.

Domo is known for its end-to-end capabilities in the cloud – from data integration (500+ connectors, built-in ETL called Magic ETL) to data visualization and even a built-in data science layer.

For embedding, Domo emphasizes ease of use: non-technical users can create dashboards in Domo’s drag-and-drop interface, then simply embed them with minimal coding. It also offers robust governance so you can control what external viewers see.

Pros and Cons

End-to-end cloud BI with 500+ connectors

Simple drag-and-embed workflow

Real-time alerts & collaboration tools

Credit-based pricing tricky to budget

Cloud-only; no on-prem option

Deeper custom UI needs dev work

Pricing: (Subscription, contact Domo for quote – USD)

Basic Embedded Package – roughly $3,000 per month for a limited-user, limited-data scenario. This might include a handful of dashboards and a moderate number of external viewers.

Mid-size Deployment – approximately $20k–$50k per year for mid-sized businesses. This would cover more users and data; e.g., a few hundred external users with regular usage.

Enterprise – $100k+/year for large-scale deployments. Enterprises with thousands of external users or very high data volumes can expect costs in six figures. (Domo often structures enterprise deals as unlimited-user but metered by data/query credits.)

Visit Domo →

Yellowfin is a BI platform that has carved a niche in embedded analytics and data storytelling. It offers a cohesive solution with modules for dashboards, data discovery, automated signals (alerts on changes), and even a unique Story feature for narrative reporting.

For embedding, Yellowfin Embedded Analytics provides OEM partners a flexible licensing model and technical capabilities to integrate Yellowfin content into their applications. Yellowfin’s strength lies in its balanced focus: it’s powerful enough for enterprise BI but also streamlined for embedding, with features like multi-tenant support and white-labeling.

It also has NLP query (natural language querying) and AI-driven insights, aligning with modern trends. A notable feature is Yellowfin’s data storytelling – you can create slide-show style narratives with charts and text, which can be embedded to give end-users contextual analysis, not just raw dashboards.

Yellowfin is often praised for its collaborative features (annotations, discussion threads on charts) which can be beneficial in an embedded context where you want users to engage with the analytics.

Pros and Cons

Built-in Stories & Signals for narratives

OEM pricing adaptable (fixed or revenue-share)

Multi-tenant + full white-label support

Lower brand recognition vs. “big three”

Some UI elements feel legacy

Advanced features require training

Pricing: (Custom – Yellowfin offers flexible models)

Visit Yellowfin →

Mode is a platform geared towards advanced analysts and data scientists, combining BI with notebooks. It’s now part of ThoughtSpot (acquired in 2023) but still offered as a standalone solution.

Mode’s appeal in an embedded context is its flexibility: analysts can use SQL, Python, and R in one environment to craft analyses, then publish interactive visualizations or dashboards that can be embedded into web apps. This means if your application’s analytics require heavy custom analysis or statistical work, Mode is well-suited.

It has a modern HTML5 dashboarding system and recently introduced “Visual Explorer” for drag-and-drop charting, plus AI assist features for query suggestions. Companies often use Mode to build rich, bespoke analytics for their customers – for example, a software company might use Mode to develop a complex report, and then embed that report in their product for each customer with the data filtered appropriately.

Mode supports white-label embedding, and you can control it via their API (to provision users, run queries, etc.). It’s popular with data teams due to the seamless workflow from coding to sharing insights.

Pros and Cons

Unified SQL, Python, R notebooks → dashboards

Strong API for automated embedding

Generous free tier for prototyping

Analyst skills (SQL/Python) required

Fewer NLQ/AI features for end-users

Visualization options less extensive than Tableau

Pricing: (USD)

Studio (Free) – $0 forever for up to 3 users. This includes core SQL/Python/R analytics, private data connections, 10MB query limit, etc. Good for initial development and testing of embedded ideas.

Pro (Business) – Starts around ~$6,000/year (estimated). Mode doesn’t list fixed prices, but third-party sources indicate pro plans in the mid four-figure range annually for small teams.

Enterprise – Custom pricing, typically five-figure annually up to ~$50k for large orgs. Includes all Pro features plus enterprise security (SSO, advanced permissions), custom compute for heavy workloads, and premium support.

Visit Mode →

How to Choose the Right Embedded Analytics Tool

Selecting an embedded analytics solution requires balancing your company’s needs with each tool’s strengths. Start with your use case and audience: Consider who will be using the analytics and their technical level. If you’re embedding dashboards for non-technical business users or customers, a tool with an easy UI could be important. Conversely, if your application demands highly custom analyses or you have a strong data science team, a more flexible code-first tool might be better.

Also evaluate whether you need a fully managed solution (more plug-and-play, e.g. Explo or Domo) or are willing to manage more infrastructure for a potentially more powerful platform (e.g. self-hosting Qlik or Sisense for complete control). The size of your company (and engineering resources) will influence this trade-off – startups often lean towards turnkey cloud services, while larger enterprises might integrate a platform into their existing tech stack.

Integration and scalability are critical factors. Look at how well the tool will integrate with your current systems and future architecture. Finally, weigh pricing and total cost of ownership against your budget and revenue model. Embedded analytics tools vary from per-user pricing to usage-based and fixed OEM licenses. Map out a rough projection of costs for 1 year and 3 years as your user count grows.

FAQs (Embedded Analytics and Reporting)

1. What are the main differences between Tableau and Power BI?

Tableau focuses on advanced visual design, cross-platform deployment (on-prem or any cloud), and a large viz library, but it costs more per user. Power BI is cheaper, tightly integrated with Microsoft 365/Azure, and great for Excel users, though some features require an Azure capacity and Windows-centric stack.

2. How does Sisense handle large datasets compared to other tools?

Sisense’s proprietary ElastiCube “in-chip” engine compresses data in memory, letting a single node serve millions of rows while maintaining fast query response; benchmarks show 500 GB cubes on 128 GB RAM. Competing BI tools often rely on external warehouses or slower in-memory engines for similar workloads.

3. Which embedded analytics tool offers the best customization options?

Sisense and Qlik are stand-outs: both expose full REST/JavaScript APIs, support deep white-labeling, and let dev teams build bespoke visual components or mashups—ideal when you need analytics to look and feel 100 % native in your app.

4. Are there any free alternatives to Tableau and Sisense?

Yes—open-source BI platforms like Apache Superset, Metabase, Redash, and Google’s free Looker Studio deliver dashboarding and basic embedded options at zero cost (self-hosted or SaaS tiers), making them good entry-level substitutes for smaller teams or tight budgets.

[souhaillaghchimdev]

Data Analysis and Visualization Using Programming Techniques

Data analysis and visualization are crucial skills in today’s data-driven world. With programming, we can extract insights, uncover patterns, and present data in a meaningful way. This post explores how developers and analysts can use programming techniques to analyze and visualize data efficiently.

Why Data Analysis and Visualization Matter

Better Decisions: Informed decisions are backed by data and its interpretation.

Communication: Visualizations make complex data more accessible and engaging.

Pattern Recognition: Analysis helps discover trends, anomalies, and correlations.

Performance Tracking: Measure progress and identify areas for improvement.

Popular Programming Languages for Data Analysis

Python: Rich in libraries like Pandas, NumPy, Matplotlib, Seaborn, and Plotly.

R: Designed specifically for statistics and visualization.

JavaScript: Great for interactive, web-based data visualizations using D3.js and Chart.js.

SQL: Essential for querying and manipulating data from databases.

Basic Workflow for Data Analysis

Collect Data: From CSV files, APIs, databases, or web scraping.

Clean Data: Handle missing values, duplicates, and inconsistent formatting.

Explore Data: Use descriptive statistics and visual tools to understand the dataset.

Analyze Data: Apply transformations, groupings, and statistical techniques.

Visualize Results: Create charts, graphs, and dashboards.

Interpret & Share: Draw conclusions and present findings to stakeholders.

Python Example: Data Analysis and Visualization

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Load data data = pd.read_csv('data.csv') # Analyze print(data.describe()) # Visualize sns.histplot(data['sales'], bins=10) plt.title('Sales Distribution') plt.xlabel('Sales') plt.ylabel('Frequency') plt.show()

Common Visualization Types

Bar Chart: Comparing categories

Line Chart: Time series analysis

Pie Chart: Proportional distribution

Scatter Plot: Correlation and clustering

Heatmap: Matrix-like data comparisons

Best Practices for Data Visualization

Keep it simple and avoid clutter.

Use colors to enhance, not distract.

Label axes, legends, and titles clearly.

Choose the right chart type for your data.

Ensure your visualizations are responsive and interactive if web-based.

Useful Libraries and Tools

Pandas & NumPy: Data manipulation

Matplotlib & Seaborn: Static visualizations

Plotly & Dash: Interactive dashboards

D3.js: Custom web-based visualizations

Power BI & Tableau: Business-level dashboarding (non-programming)

Real-World Use Cases

Sales Analysis: Visualize revenue trends and top-selling products.

Marketing Campaigns: Analyze click-through rates and conversions.

Healthcare: Monitor patient data, diagnostics, and treatment outcomes.

Finance: Analyze stock performance and predict market trends.

Conclusion

Combining data analysis with programming unlocks powerful insights and allows you to communicate results effectively. Whether you’re a beginner or an experienced developer, mastering data visualization techniques will significantly enhance your ability to solve problems and tell compelling data stories.

[visual-sculptors]

The Versatility of Think-Cell: Customizing Charts for Effective Data Representation in Presentations

Think Cell: Common Questions Answered

1. How to make two think-cell charts the same size?

To make two think-cell charts the same size, select the first chart, go to the "Format" tab, and note its dimensions. Then select the second chart, go to the same tab, and manually enter the same dimensions for width and height. Alternatively, you can copy the chart and paste it in the same dimensions as the first.

2. How to activate think-cell in PowerPoint?

To activate think-cell in PowerPoint, first ensure it is installed on your computer. Open PowerPoint, go to the "Insert" tab, and find the think-cell section. Click on the think-cell icon and select "Activate." If prompted, enter your license key or follow the on-screen instructions to complete the activation process. Restart PowerPoint if necessary.

3. How do you add lines in think-cell?

To add lines in think-cell, select the chart where you want to add a line. Go to the "Insert" tab in the think-cell toolbar and choose "Line." Click on the desired location in your chart to place the line. You can then adjust its properties, such as color and style, using the formatting options available.

4. How do I move to another series in think-cell?

To move to another series in think-cell, select the chart you want to modify. Click on the series you wish to move, then drag it to the desired position. Alternatively, you can right-click on the series, choose "Format" and adjust the series order in the options provided. Save your changes to update the chart.

5. How do you add a difference in think-cell?

To add a difference in think-cell, select the data series in your chart where you want to show the difference. Right-click and choose "Add Data Label" to display the values. You can also use the "Difference" feature by selecting two data points and clicking on the "Difference" icon to visualize the change between them directly on the chart.

Visit: VS Website See: VS Portfolio

[athulyavijayan]

Data Science vs. Machine Learning vs. Artificial

Intelligence: What Is the Difference

Technology advancement has popularized many buzzwords, and Data Science,

Machine Learning (ML), and Artificial Intelligence (AI) are the three most

commonly mixed terms. Although all of these domains share a wide range of

overlapping functions, they play distinct roles in the arena of technology and

business. Knowledge about their distinction is essential for experts, businesses,

and students pursuing a career in data-driven decision-making.

What is Data Science?

Definition

Data Science is a multi-disciplinary area that integrates statistics, mathematics,

programming, and domain expertise to extract useful information from data. It is

centered on data gathering, cleaning, analysis, visualization, and interpretation to

support decision-making

Key Components of Data Science

1. Data Collection – Extracting raw data from multiple sources, including

databases, APIs, and web scraping.

2. Data Cleaning – Deleting inconsistencies, missing values, and outliers to

ready data for analysis.

3. Exploratory Data Analysis (EDA) – Grasping data trends, distributions, and

patterns using visualizations.

4. Statistical Analysis – Using probability and statistical techniques to make

conclusions from data.5. Machine Learning Models – Utilizing algorithms to predict and automate

insights.

6. Data Visualization – Making graphs and charts to present findings clearly.

Real-World Applications of Data Science

Business Analytics – Firms employ data science for understanding customer

behavior and optimizing marketing efforts.

Healthcare – Doctors utilize data science for disease prediction and drug

development.

Finance – Banks use data science for fraud detection and risk evaluation.

E-commerce – Businesses understand customer ratings and product interest to

make recommendations better.

What is Machine Learning?

Definition

Machine Learning (ML) is a branch of artificial intelligence that deals with the

creation of algorithms that enable computers to learn from data and get better

over time without being directly programmed. It makes it possible for systems to

make predictions, classifications, and decisions based on patterns in data.

Key Elements of Machine Learning

1. Supervised Learning – The model is trained on labeled data (e.g., spam

filtering, credit rating).2. Unsupervised Learning – The model learns patterns from unlabeled data (e.g.,

customer segmentation, anomaly detection).

3. Reinforcement Learning – The model learns by rewards and penalties to

maximize performance (e.g., self-driving cars, robotics).

4. Feature Engineering – Variable selection and transformation to enhance model

accuracy.

5. Model Evaluation – Model performance evaluation using metrics such as

accuracy, precision, and recall.

Real-World Applications of Machine Learning

Chatbots & Virtual Assistants – Siri, Alexa, and Google Assistant learn user

preferences to enhance responses.

Fraud Detection – Banks utilize ML models to identify suspicious transactions.

Recommendation Systems – Netflix, YouTube, and Spotify utilize ML to

recommend content based on user behavior.

Medical Diagnosis – Machine learning assists in identifying diseases from X-rays

and MRIs.

What is Artificial Intelligence?

Definition

Artificial Intelligence (AI) is a more general term that focuses on building

machines with the ability to emulate human intelligence. It encompasses

rule-based systems, robots, and machine learning algorithms to accomplish

tasks usually requiring human intelligence.

Types of Artificial Intelligence1. Narrow AI (Weak AI) – Built for particular functions (e.g., facial recognition,

search engines).

2. General AI (Strong AI) – Seeks to be able to do any intellectual task that a

human can accomplish (not yet fully achieved).

3. Super AI – A hypothetical phase in which machines are more intelligent than

humans.

Important Elements of AI

Natural Language Processing (NLP) – AI's capacity to process and create human

language (e.g., chatbots, voice assistants).

Computer Vision – The capability of machines to interpret and process images

and videos (e.g., facial recognition).

Expert Systems – AI-powered software that mimics human decision-making.

Neural Networks – A subset of machine learning inspired by the human brain,

used for deep learning.

Real-World Applications of AI

Self-Driving Cars – AI enables autonomous navigation and decision-making.

Smart Assistants – Google Assistant, Alexa, and Siri process voice commands

using AI.

Healthcare Innovations – AI helps in robotic surgeries and personalized

medicine.

Cybersecurity – AI detects cyber threats and prevents cyberattacks.

Data Science vs. Machine Learning vs. AI: Main Differences

How These Disciplines Interact

AI is the most general discipline that machine learning is a part of.

Machine learning is an approach applied in AI to create self-improving models.

Data science uses machine learning for business intelligence and predictive

analytics.

For instance, in a Netflix recommendation engine:

Data Science gathers and examines user action

Machine Learning forecasts what content a user will enjoy given past decisions.

Artificial Intelligence personalizes the recommendations through deep learning

and NLP.

Conclusion

Data Science, Machine Learning, and Artificial Intelligence are related but have

different applications and purposes. Data science is all about data analysis and

visualization to derive insights, machine learning is about constructing models

that can learn from data, and AI is concerned with developing machines that can

emulate human intelligence.

Knowing these distinctions will assist professionals in selecting the ideal career

path and companies in optimizing these technologies to their advantage.

Whether you aspire to have a career in data analytics, construct AI-based

applications, or work on machine learning models, there are promising avenues

in each one of these professions in the modern data-centric age

[lucajack123]

Effective Data Visualization with Power BI: A Guide to Best Practices

In today’s data-driven world, effective visualization is essential for making sense of complex information. Power BI, Microsoft’s powerful business intelligence tool, enables organizations to transform raw data into meaningful insights.

For those looking to enhance their skills, Power BI Online Training & Placement programs offer comprehensive education and job placement assistance, making it easier to master this tool and advance your career.

However, simply creating charts and graphs isn’t enough—businesses must apply best practices to ensure their visualizations drive clear, actionable decisions. This guide explores the key principles for effective data visualization using Power BI.

Understand Your Audience

Before designing a Power BI report, it's crucial to know who will be using it. Different stakeholders require different levels of detail.

Executives need high-level summaries with key performance indicators (KPIs).

Analysts require detailed breakdowns with interactive filters.

Operational teams benefit from real-time tracking dashboards for quick decision-making.

By tailoring visualizations to the audience’s needs, businesses can ensure their reports are relevant and effective.

Choose the Right Visualization Type

Selecting the appropriate chart or graph is critical for conveying insights accurately. Some of the most effective Power BI visualizations include:

Bar charts and column charts: Best for comparing values across categories.

Line charts: Ideal for showing trends over time.

Pie charts and donut charts: Useful for illustrating proportions but should be used sparingly.

Heat maps: Great for visualizing geographical or density-based data.

Waterfall charts: Perfect for financial breakdowns and variance analysis.

Using the right type of visualization prevents misinterpretation and enhances data clarity.

Keep It Simple and Avoid Clutter

Overloading a report with excessive visuals, text, and colors can reduce readability. Follow these principles for a clean, impactful design:

Limit the number of visual elements per page to avoid overwhelming users.

Use consistent colors to represent similar data categories.

Apply white space strategically to enhance readability.

Label charts clearly and concisely to avoid confusion.

A well-organized report ensures users can quickly extract insights without unnecessary distractions. It’s simpler to master this tool and progress your profession with the help of Best Online Training & Placement programs, which provide thorough instruction and job placement support to anyone seeking to improve their talents.

Use Data Storytelling to Enhance Insights

Effective Power BI dashboards go beyond numbers—they tell a story. Businesses can enhance storytelling by:

Highlighting key takeaways with data labels and callouts.

Using trend indicators (arrows, color coding) to show performance shifts.

Adding annotations to explain sudden spikes or dips in data.

By guiding the audience through the insights, businesses make their reports more engaging and easier to understand.

Leverage Interactivity for Deeper Analysis

Power BI offers interactive features that allow users to explore data dynamically. Best practices include:

Filters and slicers: Let users customize views based on specific criteria (e.g., date range, region).

Drill-through reports: Enable users to click on a metric for deeper insights.

Hover tooltips: Display additional details without cluttering the report.

Interactive elements empower users to analyze data based on their unique needs.

Optimize Performance for Fast Loading

A slow Power BI dashboard can frustrate users and hinder decision-making. To optimize performance:

Use data aggregation to reduce the number of data points being processed.

Avoid excessive visuals or complex calculations on a single page.

Optimize data models by removing unnecessary columns and relationships.

Enable query reduction settings to minimize data refresh times.

By optimizing performance, businesses can ensure their Power BI reports run smoothly, enhancing the user experience.

Conclusion

Effective data visualization in Power BI requires more than just good-looking charts. It involves understanding the audience, choosing the right visuals, keeping reports simple, and leveraging interactivity. By following these best practices, businesses can transform raw data into actionable insights and drive smarter decision-making.

[convertml]

Churn Analysis: Predict and Mitigate Customer Churn Using ConvertML

Introduction

Customer churn is one of the biggest challenges for businesses, especially for SaaS companies where customer retention is key to their long-term success. Churn Analysis helps businesses identify the reasons behind their customers leaving. ConvertML provides an AI-powered Predictive Churn solution that makes it easier to analyze churn, helping businesses to predict customer churn in real time and act proactively to curb churn rates.

In this blog, we are going to see how ConvertML’s Churn Rate Prediction tools help businesses retain their customers, boost revenue, and enhance customer satisfaction.

What Is Churn Analysis?

Churn analysis is identifying why a customer stopped using your product or service. Here, one can track customer behavior, analyze patterns, and put up strategies for less churning. A high churn rate prediction means that the company is losing customers faster than gaining them, which hampers and may easily undermine high revenue and business growth prospects.

Companies can convert traditional churn analysis into an easy AI-powered predictive churn model process using ConvertML.

How ConvertML Predicts Customer Churn in Real-Time

1. AI-Powered Churn Prediction Model

ConvertML leverages advanced decision tree algorithms to identify customers at risk of leaving. This model uses both historical and real-time data to provide accurate predictive churn insights.

2. Churn Categorization and Segmentation

ConvertML’s churn segmentation strategy allows businesses to categorize customers into:

At-Risk Customers — High likelihood of churning

Medium-Risk Customers — May churn if not engaged

Low-Risk Customers — Loyal customers with low churn probability

Businesses can then create targeted customer retention campaigns based on churn risk levels.

3. Revenue-Based Churn Evaluation with Waterfall Charts

ConvertML’s Waterfall Chart visually breaks down customer revenue changes into five categories:

New — Recently acquired customers

Growing — Existing customers increasing their spending

Stable — Customers maintaining their spending levels

Diminishing — Customers spending less than before

Churned — Customers who have stopped purchasing

This approach allows businesses to understand how customer churn impacts revenue and which segments are contributing the most to losses.

4. Churn Drivers: Understanding Why Customers Leave

Many businesses struggle with churn because they don’t know what’s causing it. ConvertML’s Churn Drivers List helps businesses identify key reasons behind customer churn using regression techniques.

With this data, companies can:

Pinpoint factors that negatively impact customer retention

Develop effective churn prevention strategies

Improve customer experience and satisfaction

5. Visualizing Churn with Dynamic Decision Trees

ConvertML’s Dynamic Decision Tree Diagram makes churn analysis more intuitive. Key features include:

Branching Factors — Threshold values that influence churn

Multi-Color Nodes — Showing churn probability percentages

GenAI-Powered Insights — Auto-generated descriptive labels for easy interpretation

These insights allow businesses to take data-driven actions to retain customers effectively.

How ConvertML Helps in Churn Prevention

1. AI-Driven Alerts for Churn Trends

With ConvertML, businesses can get real-time alerts when there’s a significant change in churn trends over a specific period. This helps companies take immediate action to prevent revenue loss.

2. Strategic Retention Campaigns

Businesses can implement targeted marketing campaigns to retain and re-engage customers. ConvertML’s insights help companies:

Personalize communication with at-risk customers

Offer customized promotions or incentives to retain customers

Improve service quality based on churn drivers

3. Customer Success with CoPilot

ConvertML’s CoPilot customer success tool helps customer success teams proactively manage churn by:

Identifying high-risk customers early

Engaging customers with personalized support

Ensuring a seamless customer experience

4. Enhancing Product Development with Churn Insights

Customer feedback plays a crucial role in product development. ConvertML’s churn analysis tools allow businesses to:

Identify product features that contribute to churn

Prioritize improvements that enhance customer experience

Optimize user experience based on churn drivers

By aligning product development with churn insights, businesses can create more engaging and customer-friendly solutions.

Why Choose ConvertML for Churn Rate Prediction?

ConvertML’s AI-powered Predicting Customer Churn solution stands out because of:

✅ Explainable AI Models — Transparent and easy-to-understand churn prediction models ✅ Real-Time Churn Analysis — Instant insights into customer churn trends ✅ Comprehensive Segmentation — Categorizing customers into risk levels for better targeting ✅ Revenue-Based Churn Evaluation — Understanding churn impact on business revenue ✅ Predictive Churn Drivers — Identifying top factors influencing churn ✅ Automated Alerts & Retention Strategies — Taking proactive action before churn occurs

Conclusion

Customer retention is key to long-term business success. ConvertML simplifies churn analysis by providing predictive churn models, real-time insights, and churn rate prediction tools that help businesses retain customers effectively.

By leveraging AI-powered customer churn prediction, companies can reduce churn, improve revenue, and enhance customer satisfaction. Don’t let churn hurt your business — start using ConvertML today to predict customer churn and take proactive measures!

Want to reduce customer churn and boost retention? Try ConvertML today!

[learning-code-ficusoft]

How to Build Data Visualizations with Matplotlib, Seaborn, and Plotly

How to Build Data Visualizations with Matplotlib, Seaborn, and Plotly Data visualization is a crucial step in the data analysis process. 

It enables us to uncover patterns, understand trends, and communicate insights effectively. 

Python offers powerful libraries like Matplotlib, Seaborn, and Plotly that simplify the process of creating visualizations. 

In this blog, we’ll explore how to use these libraries to create impactful charts and graphs. 

1. Matplotlib:

 The Foundation of Visualization in Python Matplotlib is one of the oldest and most widely used libraries for creating static, animated, and interactive visualizations in Python. 

While it requires more effort to customize compared to other libraries, its flexibility makes it an indispensable tool.

Key Features: Highly customizable for static plots Extensive support for a variety of chart types Integration with other libraries like Pandas Example: Creating a Simple Line Plot import matplotlib.

import matplotlib.pyplot as plt

# Sample data years = [2010, 2012, 2014, 2016, 2018, 2020] values = [25, 34, 30, 35, 40, 50]

# Creating the plot plt.figure(figsize=(8, 5)) plt.plot(years, values, marker=’o’, linestyle=’-’, color=’b’, label=’Values Over Time’)

# Adding labels and title plt.xlabel(‘Year’) plt.ylabel(‘Value’) plt.title(‘Line Plot Example’) plt.legend() plt.grid(True)

# Show plot plt.show()

2. Seaborn:

 Simplifying Statistical Visualization Seaborn is built on top of Matplotlib and provides an easier and more aesthetically pleasing way to create complex visualizations. 

It’s ideal for statistical data visualization and integrates seamlessly with Pandas. 

Key Features:

 Beautiful default styles and color palettes Built-in support for data frames Specialized plots like heatmaps and pair plots 

Example:

 Creating a Heatmap

import seaborn as sns import numpy as np import pandas as pd

# Sample data np.random.seed(0) data = np.random.rand(10, 12) columns = [f’Month {i+1}’ for i in range(12)] index = [f’Year {i+1}’ for i in range(10)] heatmap_data = pd.DataFrame(data, columns=columns, index=index)

# Creating the heatmap plt.figure(figsize=(12, 8)) sns.heatmap(heatmap_data, annot=True, fmt=”.2f”, cmap=”coolwarm”)

plt.title(‘Heatmap Example’) plt.show()

3. Plotly: 

Interactive and Dynamic Visualizations Plotly is a library for creating interactive visualizations that can be shared online or embedded in web applications. 

It’s especially popular for dashboards and interactive reports. Key Features: Interactive plots by default Support for 3D and geo-spatial visualizations Integration with web technologies like Dash 

Example: 

Creating an Interactive Scatter Plot

import plotly.express as px

# Sample data data = {  ‘Year’: [2010, 2012, 2014, 2016, 2018, 2020],  ‘Value’: [25, 34, 30, 35, 40, 50] }

# Creating a scatter plot df = pd.DataFrame(data) fig = px.scatter(df, x=’Year’, y=’Value’, title=’Interactive Scatter Plot Example’, size=’Value’, color=’Value’)

fig.show()

Conclusion 

Matplotlib, Seaborn, and Plotly each have their strengths, and the choice of library depends on the specific requirements of your project. 

Matplotlib is best for detailed and static visualizations, Seaborn is ideal for statistical and aesthetically pleasing plots, and Plotly is unmatched in creating interactive visualizations.

[whatsdash]

Top Features to Look for in Agency Reporting Software

Agency reporting software is an essential tool for modern marketing and creative agencies. It streamlines data collection, automates reporting, and helps agencies demonstrate their value to clients. With so many options on the market, selecting the right tool can be daunting. This guide will highlight the top features you should prioritize when evaluating agency reporting software.

1. Customizable Reporting Dashboards

Every agency has unique clients with varying reporting needs. Customizable dashboards allow you to tailor reports to individual clients, ensuring they receive relevant, actionable insights. Look for software that lets you design and arrange widgets, charts, and data views for a personalized experience. Then WhatsdashTM could be the best option.

2. Integrations with Popular Tools

A robust reporting tool should integrate seamlessly with other platforms your agency uses, such as:

Google Analytics

Facebook Ads

HubSpot

Shopify

The ability to pull data from multiple sources into one cohesive report saves time and reduces the risk of errors from manual entry.

3. Automation Capabilities

Time is a precious commodity for agencies. Automation features like scheduled report generation and email distribution can significantly reduce the manual workload. Choose software that allows you to set up recurring reports, so clients receive updates without requiring ongoing manual input.

4. Real-Time Data Updates

Outdated data can lead to misinformed decisions. Real-time data updates ensure you and your clients are always working with the most current information. This is particularly critical for agencies managing dynamic campaigns such as PPC or social media.

5. White-Labeling Options

White-label reporting tools let you brand reports with your agency’s logo, colors, and design elements. This adds a professional touch and reinforces your agency's identity, making reports feel like a natural extension of your service.

6. Ease of Use and Collaboration

Even the most feature-rich software is useless if it's too complicated. Look for tools with intuitive interfaces that your team and clients can easily navigate. Collaboration features like shared comments, annotations, or live report access enhance teamwork and improve client relationships.

7. Robust Analytics and Visualizations

Data is more persuasive when it's visually compelling. Software with advanced analytics and visualization tools, such as graphs, heatmaps, and trend lines, makes complex data easier to understand and act upon.

8. Scalability

As your agency grows, your reporting needs will evolve. Choose a tool that can scale with your business, accommodating more clients, larger datasets, and additional integrations without a steep learning curve.

9. Security and Compliance

With increasing concerns over data privacy, your reporting software must prioritize security. Ensure it adheres to industry standards, especially if you’re handling sensitive client data.

10. Cost-Effectiveness

While premium features are important, the software should provide value for money. Evaluate pricing plans to ensure they align with your agency’s budget and usage needs. Many providers offer tiered pricing, so you only pay for what you use.

Conclusion: 

Selecting the right agency reporting software is crucial for streamlining operations and maintaining strong client relationships. By focusing on features like customization, integrations, automation, and scalability, you can choose a tool that empowers your agency to deliver exceptional value.

Investing in the right software not only enhances your agency's efficiency but also strengthens your ability to showcase measurable results to clients—ultimately driving retention and growth.

What features matter most to your agency? Share your thoughts in the comments below!

[powerbionlinetrainingvisualpath]

Power BI Training | Top Power BI Online Training

Power BI Training: Tips for Creating Engaging Visuals

Power BI Training empowers users to harness the full potential of this dynamic data visualization tool, enabling them to create impactful visuals that convey insights effectively. The ability to craft engaging visuals in Power BI is essential for professionals aiming to present data in a way that is both informative and visually appealing. Whether you are just starting or taking Power BI Online Training, mastering the art of visual storytelling is key to success.

Crafting engaging visuals in Power BI goes beyond selecting charts and graphs. It requires a deep understanding of your audience, the data's context, and the story you want to convey. Both beginners and advanced users who have undergone Power BI Training or completed Power BI Online Training can benefit from these actionable tips to elevate their visualizations.

Understanding Your Audience and Objectives

To create impactful visuals, always start with a clear understanding of your audience and objectives. Ask yourself:

Who will be viewing this report?

What key insights should they take away?

What level of detail is appropriate?

Power BI Training emphasizes the importance of tailoring visuals to specific audiences. For instance, executives may prefer high-level summaries, while analysts might require detailed breakdowns. Aligning your visuals with your goals ensures they resonate and deliver value. Power BI Online Training further enhances this skill by offering practical examples of audience-focused design.

Choosing the Right Visuals for the Data

Power BI offers a wide range of visuals, including bar charts, scatter plots, and maps. Selecting the right type of visualization is critical for clear communication. Here are a few guidelines:

Comparison: Use bar or column charts to compare categories.

Trends over Time: Line charts are ideal for tracking data over periods.

Proportions: Pie charts or donut charts show parts of a whole, though they should be used sparingly.

Geographical Data: Leverage map visuals for location-specific insights.

Advanced users who have taken Power BI Training can also explore custom visuals and third-party integrations for more tailored solutions. Meanwhile, Power BI Online Training provides hands-on experience in experimenting with these visual options.

Focusing on Simplicity and Clarity

Clarity is king when it comes to data visualization. Avoid clutter and ensure your visuals communicate insights at a glance. Some best practices include:

Limiting the number of colours to maintain a cohesive look.

Using labels and tooltips to provide context without overwhelming the viewer.

Highlighting key data points with contrasting colours or annotations.

A significant takeaway from Power BI Training is the emphasis on simplicity. Overcomplicating visuals can confuse the audience, detracting from the message. In Power BI Online Training, learners practice creating visuals that are both straightforward and impactful.

Leveraging Themes and Formatting

Consistency in design helps reinforce your brand and makes reports visually appealing. Power BI allows you to apply themes to standardize fonts, colors, and layout. To create engaging visuals:

Use pre-defined themes or customize your own to align with organizational branding.

Adjust font sizes and styles for readability.

Align visuals for a professional and polished look.

Themes and formatting are essential components of advanced Power BI Training, where users learn to create cohesive and aesthetically pleasing dashboards. Similarly, Power BI Online Training teaches learners to experiment with design elements to enhance report engagement.

Incorporating Interactivity

Interactive features in Power BI bring reports to life, allowing users to explore data dynamically. Add interactivity by:

Slicers: Enable users to filter data based on categories like region, time, or product.

Drill-through Pages: Provide detailed insights by linking high-level summaries to in-depth pages.

Bookmarks and Buttons: Enhance navigation and storytelling by guiding viewers through a data narrative.

Both Power BI Training and Power BI Online Training stress the importance of interactivity in making data accessible and engaging. Hands-on exercises in these courses help user’s master interactive features.

Emphasizing Storytelling in Visualizations

Every dataset tells a story. Use visuals to guide your audience through that narrative, focusing on key takeaways. Techniques to enhance storytelling include:

Structuring reports with a logical flow: overview → analysis → conclusion.

Using annotations to highlight critical data points.

Including summaries or KPIs to reinforce main insights.

Storytelling is a core concept in Power BI Training, equipping users to create compelling narratives from raw data. Through Power BI Online Training, users gain real-world experience in combining data and design for maximum impact.

Testing and Refining Visuals

Before presenting your Power BI reports, test their effectiveness by gathering feedback from peers or stakeholders. Focus on:

Ease of understanding: Can viewers grasp the key insights quickly?

Aesthetic appeal: Are the visuals visually balanced and aligned?

Accuracy: Is the data represented correctly and without bias?

Revisions based on feedback ensure your visuals meet the highest standards. Power BI Training highlights the importance of iterative design, while Power BI Online Training provides ample opportunities to refine skills through practice.

Conclusion

Creating engaging visuals in Power BI requires a blend of technical expertise, creativity, and an understanding of your audience's needs. Through Power BI Training, you can develop the skills necessary to choose the right visuals, maintain simplicity, and incorporate interactivity for maximum impact. Enrolling in Power BI Online Training enhances your ability to design effective dashboards and reports that resonate with your audience.

By implementing these tips, you can transform your Power BI reports into powerful tools for decision-making. Whether you’re presenting to executives or exploring data trends, compelling visuals ensure your message is clear, impactful, and actionable.

Visualpath is the Leading and Best Institute for learning in Hyderabad. We provide Power BI Course Online. You will get the best course at an affordable cost.

Attend Free Demo

Call on – +91-9989971070

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[amazonquicksighttraining]

Amazon QuickSight Training | AWS QuickSight Training in Hyderabad

Amazon QuickSight Training: 10 QuickSight Tips & Tricks to Boost Your Data Analysis Skills

Amazon QuickSight is a powerful business intelligence (BI) tool that empowers organizations to create interactive dashboards and gain valuable insights from their data. Whether you’re new to Amazon QuickSight or looking to enhance your existing skills, mastering its advanced features can significantly improve your data analysis capabilities. This guide focuses on ten essential tips and tricks to elevate your expertise in Amazon QuickSight Training and make the most out of this tool. 

1. Understanding the Basics with Amazon QuickSight Training 

To effectively use QuickSight, start by understanding its fundamentals. Amazon QuickSight Training provides a thorough overview of key functionalities such as connecting data sources, creating datasets, and building visualizations. By mastering the basics, you set the foundation for leveraging more advanced features and gaining meaningful insights. 

2. Optimize Data Preparation 

One of the first steps in data analysis is data preparation. Use Amazon QuickSight's in-built tools to clean, transform, and model your data before creating visualizations. Features like calculated fields and data filters are especially useful for creating precise datasets. AWS QuickSight Online Training covers these capabilities in detail, helping you streamline the preparation process. 

3. Mastering Visual Customization 

Effective data presentation is critical for analysis. With QuickSight, you can create highly customized visuals that align with your specific needs. Learn to adjust colors, axes, and data labels to make your charts more intuitive and visually appealing. Amazon QuickSight Training emphasizes the importance of tailoring visuals to improve storytelling and engagement. 

4. Utilize Advanced Calculations 

QuickSight supports advanced calculations, such as percentiles, running totals, and custom metrics. Leveraging these features allows you to derive deeper insights from your data. AWS QuickSight Training provides step-by-step guidance on creating advanced formulas, which can save time and add value to your analysis. 

5. Enable Auto-Narratives for Insights 

Auto-narratives in QuickSight use natural language processing (NLP) to generate textual summaries of your data. This feature is particularly useful for highlighting trends, anomalies, and key performance indicators (KPIs). AWS QuickSight Online Training teaches how to enable and customize auto-narratives for improved decision-making. 

6. Take Advantage of SPICE Engine 

QuickSight’s Super-fast, Parallel, In-memory Calculation Engine (SPICE) is designed for speed and efficiency. It enables users to analyze massive datasets without relying on external databases. Learning how to optimize SPICE usage is an integral part of Amazon QuickSight Training and ensures you can work with data at scale. 

7. Implement Conditional Formatting 

Conditional formatting helps draw attention to critical data points. You can set rules to highlight values based on specific conditions, making your dashboards more actionable. AWS QuickSight Training explores how to implement these rules to enhance the interpretability of your visuals. 

8. Share and Collaborate Effectively 

QuickSight makes it easy to share dashboards and reports with stakeholders. By learning best practices for sharing, including granting permissions and scheduling email reports, you ensure that insights reach the right audience. AWS QuickSight Online Training includes collaboration techniques to improve team workflows. 

9. Use Embedded Analytics 

Embedding QuickSight dashboards into applications or websites is a game-changer for businesses. This feature allows organizations to provide real-time insights to users within their existing platforms. Amazon QuickSight Training delves into embedding analytics, offering practical examples to integrate dashboards seamlessly. 

10. Stay Updated with New Features 

AWS QuickSight regularly updates its features to enhance user experience and functionality. Staying informed about these updates through AWS QuickSight Training ensures you are always utilizing the latest tools and capabilities to boost productivity and efficiency. 

Conclusion: Amazon QuickSight is a versatile and user-friendly BI tool that caters to a wide range of data analysis needs. By leveraging these tips and tricks, you can unlock the full potential of QuickSight and deliver impactful insights. Whether you're new to BI or an experienced analyst, Amazon QuickSight Training equips you with the skills to excel in data visualization and reporting. 

AWS QuickSight Online Training and AWS QuickSight Training courses are invaluable resources for professionals looking to stay competitive in today’s data-driven world. From mastering visual customization to utilizing SPICE and embedding analytics, QuickSight offers endless opportunities for growth and innovation. With continuous learning and practice, you can transform raw data into actionable intelligence, empowering your organization to make informed decisions and drive success.

Visualpath is a top institute in Hyderabad offering AWS QuickSight Online Training with real-time expert instructors and hands-on projects. Our Amazon QuickSight Course Online, from industry experts and gain experience. We provide to individuals globally in the USA, UK, etc. To schedule a demo, call +91-9989971070. 

Key Points: AWS, Amazon S3, Amazon Redshift, Amazon RDS, Amazon Athena, AWS Glue, Amazon DynamoDB, AWS IoT Analytics, ETL Tools.

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Visit: https://www.visualpath.in/online-amazon-quicksight-training.html

[govindhtech]

HighCharts API And Looker Chart Config Editor Tips & tricks

Make your data story more comprehensive by using personalized Looker charts and visualizations with HighCharts API.

Looker Chart Config Editor Tips And tricks

A collection of numbers is all that data is unless it can be used to tell a story and obtain further information. Google Cloud is always working to enhance Looker’s features so you can work together with reliable metrics and share your data stories. It has added the capability to add bullet chart, sunburst, venn, and treemap visualizations to Looker Explores and dashboards by utilizing the Chart Config Editor to previously available Looker visualizations. It wanted to offer some best practices on how to use the Chart Config Editor to enrich your visualizations and make meaningful data experiences so that you can make the most of these new Looker visuals.

HighCharts API

For those who are unfamiliar with the Chart Config Editor, Looker visualizations show your data using the Highcharts interactive charting library. You may customize your visualizations by utilizing the editor, which exposes portions of the Highcharts library API. In order to enhance your visualizations, it will explore the Highcharts API and discover some useful Chart Config Editor tips and tricks in this post. You need have access to Chart Config Editor and be familiar with the JSON format in order to fully comprehend its examples in order to get the most out of this post.

HighCharts API reference

In a line chart, set the labels and look of each line

Consider a representation of a line chart that shows several time series, each represented by a single line. You might find it difficult to distinguish between the lines in the dashboard viewer, or you might want to highlight a certain line more than others. Highchart offers several `series} properties that you can use to modify how each line is presented and styled. Among the qualities are:

{dashStyle} to alter the pattern of each line

To alter the thickness of every line, use {lineWidth}.

{opacity} to alter the opacity of each line

Use dataLabels to add labels to the values or data on a line.

You can apply each {series} property in any combination to make the data in your line visualization easier for your stakeholders to grasp.

By setting the default styling across all lines using Highchart’s plotOptions attribute, you can further simplify the settings shown in the above sample. Afterwards, you may use the {series} element, which changes the default styling, to further modify individual lines. The following Chart Config Editor setting sample shows both the overriding and default styling:

Allowing visuals to scroll inline

Imagine a column chart visualization where each column represents a month and the date time x-axis spans several decades. The width of your dashboard limits the visualization you may use, so as time goes on, the widths of each column get smaller and the monthly or annual trends are compressed, making them difficult to grasp.

To enable horizontal scrolling for your stakeholders through a column or line-chart visualization, try defining the width of your visualization using Highchart’s chart.scrollablePlotArea} attribute. TheminWidthattribute allows you to establish the minimum width of your visualization, while thescrollPositionX` attribute allows you to specify the visualization’s starting scrolling position. The visualization’s minimum width of 2,000 pixels and its initial scrolling position to the right are established in the Chart Config Editor configuration sample below.

chart: { scrollablePlotArea: { minWidth: 2000, scrollPositionX: 1 } },

Try experimenting with the scrollPositionY} andminHeight` attributes as well to allow scrolling vertically in your visualizations.

Complete control over the data labels for additional chart visualizations, such as pie charts

You can name each pie slice using the plot menu options in a pie chart visualization, but usually you can just display the slice’s value or percentage. To fully comprehend all of the data, the observer must perform a double take, glancing between the legend and the data labels. Additionally, all of the data will not be accessible in a scheduled PDF delivery of the dashboard containing the visualization, even if users might mouseover each pie slice to view it.

To help your stakeholders quickly extract information from your charts, you can use the Chart Config Editor to display any information that is available on the HighCharts PointLabelObject on the data labels. This includes the percentage and value displayed simultaneously. You can also further customize the labels with HTML. To modify the format and style of a chart’s data labels, you must set up the previously stated Highchart {dataLabels} attribute from its first example. The following {dataLabels` attributes need to be configured:

{enabled}, as demonstrated in our first example, to enable data labels on the chart

To enable HTML styling of data labels, use useHTML.

To apply CSS styles to every data label, use {style}.

Use {format} to specify the piece and format.

The data labels of the pie chart are shown in the Chart Config Editor configuration sample below with a font size of 12 pixels. If the property name for the format} attribute is enclosed in curly braces, then all of the PointLabelObject's properties can be shown in the data label. The example assigns the following string to theformat` attribute}:

The pie slice name bolded with the key attribute of the PointLabelObject within an HTML Draw Focus On This Aspect

The value of the data point with the `y} property of the PointLabelObject

Specifies the percentage of the data point with one decimal place formatting using the PointLabelObject’s percentage property

The format of the final data label is as follows: Category: 11.5%, 596524.

Keep in mind that the tooltip.format} attribute and thedataLabels.format} attribute function similarly; the documentation has more information on this. Also take note that for pie charts, it need the plotOptions.pie.dataLabels} attribute. You must override theplotOptions.line.dataLabelsattribute if you wish to format a line chart with the same data-label style. The interface and functionality of many chart kinds are mostly shared via thedataLabels` element.

Make your charts more insightful and powerful

We hope that these illustrations will work as a springboard for you as you investigate the HighCharts API and discover how to set up your Looker visualizations to convey useful and compelling narratives. You now know how to enable inline scrolling of visualizations, edit data labels, and change the appearance of each series of your data using the Looker Chart Config Editor and HighCharts API.

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