12 answers
9
Questions
Asked
7751 views
Which BI tools are in demand in 2025 Planning to learn dbt → Power BI — need advice on the best niche and next steps?
I'm aiming to build a career in Business Intelligence. Given how competitive the job market is right now, I’ve decided to learn dbt and Power BI to strengthen my profile and understand the modern data stack better.
I come from a non-technical background with a gap after graduation, but I’ve started learning SQL and want to focus fully on the BI side of things—dashboards, reporting, insights, etc.
I'm curious to know:
1. How many BI tools are actively used in the current market?
2. Which BI tools are most valuable to learn in 2025?
3. What niche/role within BI makes sense for someone starting out like me and for professional career in future ?
Would love any advice from professionals already working in BI—your suggestions will help me shape a clear roadmap.
Thank you!
12 answers
Updated
Wyatt’s Answer
Hello Anjali,
I bring over 25 years of experience in the technology sector, having built my career at a high-end technology consulting firm. I began as an engineer and steadily advanced into executive leadership, ultimately overseeing several technology departments and driving strategic initiatives across the organization.
You’re making a VERY smart decision by focusing on Business Intelligence, especially with tools like SQL, dbt, and Power BI since they’re at the core of today’s modern data stack. While there are many BI tools in the market, only a few dominate. The most commonly used ones include Power BI, Tableau, Looker, Qlik, and lightweight options like Metabase and Mode. Among these, Power BI stands out as a must-learn in 2025, especially in enterprise settings, while dbt is highly valuable for modern data transformation and modeling. SQL remains the foundational skill across all BI roles, and you’re right to focus on it early.
For someone starting out, especially from a non-technical background, the most accessible roles are BI Analyst, Reporting Analyst, and Data Analyst. These positions focus on building dashboards, creating reports, and translating data into insights for business stakeholders. As you gain more technical skills (especially with dbt and SQL), you can transition into analytics engineering, a newer and in-demand role that bridges analysts and data engineers. Over time, this path can lead to roles like Product Analyst, Data Product Manager, or even strategy-focused positions, depending on your interests.
To stand out in a competitive market, start building portfolio projects that showcase your ability to turn raw data into business insights, using SQL for querying, dbt for modeling, and Power BI for visualizations. Public datasets from platforms like Kaggle or government portals are great starting points. Consider certifications like PL-300 (Power BI) and dbt Fundamentals, and document your learning journey on LinkedIn. Recruiters value clarity, initiative, and real-world examples, so the more you can demonstrate those through your portfolio and communication, the stronger your profile will be.
Let me know if you want help mapping out a 3–6 month learning plan!
Best Wishes,
Wyatt
I bring over 25 years of experience in the technology sector, having built my career at a high-end technology consulting firm. I began as an engineer and steadily advanced into executive leadership, ultimately overseeing several technology departments and driving strategic initiatives across the organization.
You’re making a VERY smart decision by focusing on Business Intelligence, especially with tools like SQL, dbt, and Power BI since they’re at the core of today’s modern data stack. While there are many BI tools in the market, only a few dominate. The most commonly used ones include Power BI, Tableau, Looker, Qlik, and lightweight options like Metabase and Mode. Among these, Power BI stands out as a must-learn in 2025, especially in enterprise settings, while dbt is highly valuable for modern data transformation and modeling. SQL remains the foundational skill across all BI roles, and you’re right to focus on it early.
For someone starting out, especially from a non-technical background, the most accessible roles are BI Analyst, Reporting Analyst, and Data Analyst. These positions focus on building dashboards, creating reports, and translating data into insights for business stakeholders. As you gain more technical skills (especially with dbt and SQL), you can transition into analytics engineering, a newer and in-demand role that bridges analysts and data engineers. Over time, this path can lead to roles like Product Analyst, Data Product Manager, or even strategy-focused positions, depending on your interests.
To stand out in a competitive market, start building portfolio projects that showcase your ability to turn raw data into business insights, using SQL for querying, dbt for modeling, and Power BI for visualizations. Public datasets from platforms like Kaggle or government portals are great starting points. Consider certifications like PL-300 (Power BI) and dbt Fundamentals, and document your learning journey on LinkedIn. Recruiters value clarity, initiative, and real-world examples, so the more you can demonstrate those through your portfolio and communication, the stronger your profile will be.
Let me know if you want help mapping out a 3–6 month learning plan!
Best Wishes,
Wyatt
I will use this advice as I prepare for my career.
Anjali
Thank you for replying to my question Sir! This is amazing.
I would definitely need your help mapping out 6 month learning plan. Thanks again !
Anjali
You are very much welcome Anjali!
Wyatt .
Hi Anjali, I submitted a learning plan for you in the answer section of your question. Hope you find the information helpful to assist you moving forward.
Wyatt .
Updated
Wyatt’s Answer
Hello Anjali,
This is a follow to my first response where you requested a learning plan.
A strong 6 month plan should begin with building foundational skills in SQL and analytics. In the first two months, focus on mastering SQL basics like SELECT statements, JOINs, and window functions. Supplement this with introductory data analysis concepts and possibly the Google Data Analytics certificate. By Month 3, shift your focus to Power BI, learning how to create dashboards, use DAX, and connect data sources. At the same time, start exploring dbt by setting up your environment and learning how to model data using Jinja and ref() functions. During Months 5 and 6, build portfolio projects that integrate SQL, dbt, and Power BI. These end-to-end projects will be key for showcasing your skills. Aim to complete the PL-300 Power BI certification and dbt Fundamentals to strengthen your resume.
If you choose to continue to a full-year plan, deepen your technical skills in Months 7 to 9 by working with more complex SQL queries, dbt macros, and advanced Power BI features like bookmarks and drillthrough pages. This is also a good time to start using Git for version control and documenting your dbt models more professionally. By this stage, you should be comfortable creating advanced dashboards and reusable dbt models. From Months 10 to 12, focus on polishing your portfolio and preparing for the job market. Build a personal website or Notion page to host your case studies, and prepare for interviews by practicing how to explain business impact, data storytelling, and stakeholder communication. Begin applying for internship roles and or find opportunities to spend time and shadow people who are in roles such as BI Analyst, Junior Analytics Engineer, or Reporting Analyst using keywords like SQL, Power BI, dbt, data modeling, and dashboards.
Throughout the year, aim to build at least 2–3 high-quality projects such as an ecommerce sales dashboard, HR attrition report, or marketing campaign ROI tracker. These should show not just technical skill, but also business reasoning and clear communication. Share your work on GitHub and LinkedIn to build your network and online presence. If you’re consistent with your learning and portfolio development, you could be in a strong position to enter the BI job market within 12 months, even from a non-technical background.
Keep in mind that the above plan follows an aggressive timeline, so feel free to stretch it out to two years if needed, especially since you’re balancing school alongside your learning. Whatever you do, don’t let yourself get overwhelmed. Learning a specialized technology can feel challenging at times, especially when you’re new to the field or juggling other responsibilities such as college, but that’s completely normal. Remember, technical skills build gradually, and each step you take is meaningful progress, even if it doesn’t feel fast. Think of it as forward motion learning: with consistent effort and steady practice, you’re always moving closer to your goals. The key is to stay committed, be patient with yourself during the tough moments, and trust that persistence will lead to real, lasting growth in your career.
Best of luck to you and your exciting endeavors!
Wyatt
This is a follow to my first response where you requested a learning plan.
A strong 6 month plan should begin with building foundational skills in SQL and analytics. In the first two months, focus on mastering SQL basics like SELECT statements, JOINs, and window functions. Supplement this with introductory data analysis concepts and possibly the Google Data Analytics certificate. By Month 3, shift your focus to Power BI, learning how to create dashboards, use DAX, and connect data sources. At the same time, start exploring dbt by setting up your environment and learning how to model data using Jinja and ref() functions. During Months 5 and 6, build portfolio projects that integrate SQL, dbt, and Power BI. These end-to-end projects will be key for showcasing your skills. Aim to complete the PL-300 Power BI certification and dbt Fundamentals to strengthen your resume.
If you choose to continue to a full-year plan, deepen your technical skills in Months 7 to 9 by working with more complex SQL queries, dbt macros, and advanced Power BI features like bookmarks and drillthrough pages. This is also a good time to start using Git for version control and documenting your dbt models more professionally. By this stage, you should be comfortable creating advanced dashboards and reusable dbt models. From Months 10 to 12, focus on polishing your portfolio and preparing for the job market. Build a personal website or Notion page to host your case studies, and prepare for interviews by practicing how to explain business impact, data storytelling, and stakeholder communication. Begin applying for internship roles and or find opportunities to spend time and shadow people who are in roles such as BI Analyst, Junior Analytics Engineer, or Reporting Analyst using keywords like SQL, Power BI, dbt, data modeling, and dashboards.
Throughout the year, aim to build at least 2–3 high-quality projects such as an ecommerce sales dashboard, HR attrition report, or marketing campaign ROI tracker. These should show not just technical skill, but also business reasoning and clear communication. Share your work on GitHub and LinkedIn to build your network and online presence. If you’re consistent with your learning and portfolio development, you could be in a strong position to enter the BI job market within 12 months, even from a non-technical background.
Keep in mind that the above plan follows an aggressive timeline, so feel free to stretch it out to two years if needed, especially since you’re balancing school alongside your learning. Whatever you do, don’t let yourself get overwhelmed. Learning a specialized technology can feel challenging at times, especially when you’re new to the field or juggling other responsibilities such as college, but that’s completely normal. Remember, technical skills build gradually, and each step you take is meaningful progress, even if it doesn’t feel fast. Think of it as forward motion learning: with consistent effort and steady practice, you’re always moving closer to your goals. The key is to stay committed, be patient with yourself during the tough moments, and trust that persistence will lead to real, lasting growth in your career.
Best of luck to you and your exciting endeavors!
Wyatt
Thankyou so much for your suggestion and support. Really appreciate your advice.
Thanks!
Anjali
This is excellent plan and advice.
Lingaraj Dharwad
Thank you for the kind words Lingaraj. Best wishes to you.
Wyatt .
Updated
Nikhil’s Answer
1. How many BI tools are actively used in the market?
Here are some
Power BI (Microsoft) – widely used in enterprises
Tableau (Salesforce) – strong in visual analytics
Looker (Google) – common in modern data stacks
Qlik Sense, Domo, Metabase, Superset – used in niche or industry-specific setups
Plus, dbt, Snowflake, BigQuery, Azure Synapse, etc., complement BI for data modeling and warehousing
You don’t need to learn all of them—focus on the ones most aligned with job demand and your learning comfort.
2. Which BI tools are most valuable to learn in 2025?
Power BI – still one of the most in-demand BI tools globally, especially in enterprise and consulting
dbt (data build tool) – widely used for data modeling and transformation, often paired with modern BI
SQL – essential across all tools and roles; keep building this skill
3. Role within BI makes sense for someone starting out
BI Analyst or Reporting Analyst – focuses on dashboards, reporting, data cleanup
Data Analyst – similar, with more focus on SQL and analysis
Junior Data Modeler (if you enjoy dbt and want to work on pipelines)
Here are some
Power BI (Microsoft) – widely used in enterprises
Tableau (Salesforce) – strong in visual analytics
Looker (Google) – common in modern data stacks
Qlik Sense, Domo, Metabase, Superset – used in niche or industry-specific setups
Plus, dbt, Snowflake, BigQuery, Azure Synapse, etc., complement BI for data modeling and warehousing
You don’t need to learn all of them—focus on the ones most aligned with job demand and your learning comfort.
2. Which BI tools are most valuable to learn in 2025?
Power BI – still one of the most in-demand BI tools globally, especially in enterprise and consulting
dbt (data build tool) – widely used for data modeling and transformation, often paired with modern BI
SQL – essential across all tools and roles; keep building this skill
3. Role within BI makes sense for someone starting out
BI Analyst or Reporting Analyst – focuses on dashboards, reporting, data cleanup
Data Analyst – similar, with more focus on SQL and analysis
Junior Data Modeler (if you enjoy dbt and want to work on pipelines)
Thank you, Nikhil for the advice.
Anjali
I agree with the advice Nikhil gave!
As a recruiter, the tools we look most for are PowerBI and Tableau. These two are very standard across all industries.
Evan Barnes-Wallace
Updated
Sharven’s Answer
I’ve seen the full evolution from PowerPoints to Power BI, Tableau, and now AI-powered visualizations. So I want to approach this from a different angle, one that’s often overlooked. As someone who leads analysts and regularly reviews dashboards that influence key decisions, I can tell you the core purpose of BI is not about flashy visuals it’s about delivering clean, concise, and actionable insights.
You don’t need to master every tool. Pick one or two and go deep. Spend most of your time learning how to interpret data, craft a clear narrative, and communicate insights effectively. The best BI professionals I’ve worked with aren’t known for their tools they’re respected for their ability to cut through noise and present the truth of the data, even when it's uncomfortable. Your job is to help leaders make fast, informed decisions. And for that, clarity always wins over complexity. Learn the tools 25%, focus on communication and storytelling 75% that’s the formula that gets noticed.
One tip I always give my analysts: Every visualization you create should uncover a non-obvious insight. Tackle the hard-to-spot trends, patterns, or red flags that others might miss. Then explain them with confidence and clarity. A beautiful dashboard means nothing if the person reading it doesn’t understand it in under five minutes. Be that bridge between data and decisions because that’s what truly sets great BI analysts apart.
You don’t need to master every tool. Pick one or two and go deep. Spend most of your time learning how to interpret data, craft a clear narrative, and communicate insights effectively. The best BI professionals I’ve worked with aren’t known for their tools they’re respected for their ability to cut through noise and present the truth of the data, even when it's uncomfortable. Your job is to help leaders make fast, informed decisions. And for that, clarity always wins over complexity. Learn the tools 25%, focus on communication and storytelling 75% that’s the formula that gets noticed.
One tip I always give my analysts: Every visualization you create should uncover a non-obvious insight. Tackle the hard-to-spot trends, patterns, or red flags that others might miss. Then explain them with confidence and clarity. A beautiful dashboard means nothing if the person reading it doesn’t understand it in under five minutes. Be that bridge between data and decisions because that’s what truly sets great BI analysts apart.
Thank you, this is amazing! I really needed it.
Anjali
This is awesome! Thankyou for giving me advice.
Really appreciate it.
I totally get that building flashy dashboards or knowing the latest data stack isn’t enough if you can’t translate data into business impact.
Could you please share how someone can actually learn to think this way?
Which resources, books, courses, or real-world exercises that help build this mindset?
I’m looking to go beyond technical skills and get better at asking the right questions, understanding context, and turning data into actual business decisions.
Where would you recommend starting?
Anjali
You're already thinking in the right direction the ability to connect data with business impact is what sets great analysts apart. But here’s the truth: no course will make you an expert. At best, they’ll get you to a beginner or intermediate level. What actually matters is what you do with those skills and that comes from consistently solving real, tough business problems. The people who grow fastest in this field are the ones who approach every dataset by asking, “What decision does this support?” and “How does this move the business forward?”
Sharven Rane
If you're serious about leveling up, stop looking for the “perfect resource” and start thinking like an owner. What’s broken? What’s inefficient? What’s costing time or money? Use your current skills to fix those things. That cycle of learn, apply, solve, repeat is what makes you valuable in this field. It’s not about being flashy with tools; it’s about being useful where it counts.
Sharven Rane
Updated
Sukanta’s Answer
Hi Anjali,
Here are some popular BI tools in the market. You don't need to learn them all—just focus on the ones that match job demands and your comfort level.
Microsoft Power BI: Works well with Microsoft products like Excel and Azure, offers AI features, and provides real-time dashboards. It's widely used in businesses for self-service and governance.
Tableau: Known for its strong visual storytelling and easy drag-and-drop interface. It's popular across industries for data visualization and creating dashboards.
Looker (Google Cloud): Features a semantic modeling layer and embedded analytics, with seamless integration with Google Cloud Platform. It's a favorite for modern data setups using BigQuery and GCP.
Qlik Sense: Offers an associative engine and in-memory processing, making it great for real-time exploration and predictive analytics.
ThoughtSpot: Provides search-based analytics and AI-driven insights, and is cloud-native. It's becoming popular for its user-friendly natural language querying.
SAP Analytics Cloud: Combines enterprise planning, BI, and predictive capabilities in one tool, making it ideal for organizations using SAP ERP.
Zoho Analytics: An affordable option with AI-assisted analytics and integrations with the Zoho suite, it's gaining popularity among small to medium businesses and startups.
Here are some popular BI tools in the market. You don't need to learn them all—just focus on the ones that match job demands and your comfort level.
Microsoft Power BI: Works well with Microsoft products like Excel and Azure, offers AI features, and provides real-time dashboards. It's widely used in businesses for self-service and governance.
Tableau: Known for its strong visual storytelling and easy drag-and-drop interface. It's popular across industries for data visualization and creating dashboards.
Looker (Google Cloud): Features a semantic modeling layer and embedded analytics, with seamless integration with Google Cloud Platform. It's a favorite for modern data setups using BigQuery and GCP.
Qlik Sense: Offers an associative engine and in-memory processing, making it great for real-time exploration and predictive analytics.
ThoughtSpot: Provides search-based analytics and AI-driven insights, and is cloud-native. It's becoming popular for its user-friendly natural language querying.
SAP Analytics Cloud: Combines enterprise planning, BI, and predictive capabilities in one tool, making it ideal for organizations using SAP ERP.
Zoho Analytics: An affordable option with AI-assisted analytics and integrations with the Zoho suite, it's gaining popularity among small to medium businesses and startups.
Thank you so much for the advice.
Anjali
Updated
Robert’s Answer
Hi there, Anjali.
I'd like to relate a bit of practical experience, as the team of analysts and data scientists that I manage is in the process of assessing the tools that will take us into the future. The trends that we're seeing center on platforms that reduce cost, offer multiple points of integration, and have close proximity to powerful AI assistants and engines. Fewer and fewer standalone products like Tableau and PowerBI are looking to be directional technologies as we move into a timeline that centers more on conversational AI and agent automation.
For someone coming into the field, I would recommend developing competency in the underlying data platforms - SQL driven relational databases such as Oracle, MySQL/MariaDB, PostgreSQL, etc. along with cloud-based big data engines like Hive and BigQuery. Visualization and analytics tools require well-engineered datasets before they can produce useful results, so the data layer is the foundation on which everything rests.
Looker with its Conversational Analytics integrations with Gemini AI and BigQuery, and proximity to data automation tools like Google Cloud Composer's implementation of Apache Airflow, is rapidly gaining footholds in the enterprise space. Building Python data modelling and manipulation skills using distributions like Anaconda is also super valuable. The main thing to keep in mind is that the visualization/presentation tool is only one small layer in the stack that makes data usable to solve problems.
I'd like to relate a bit of practical experience, as the team of analysts and data scientists that I manage is in the process of assessing the tools that will take us into the future. The trends that we're seeing center on platforms that reduce cost, offer multiple points of integration, and have close proximity to powerful AI assistants and engines. Fewer and fewer standalone products like Tableau and PowerBI are looking to be directional technologies as we move into a timeline that centers more on conversational AI and agent automation.
For someone coming into the field, I would recommend developing competency in the underlying data platforms - SQL driven relational databases such as Oracle, MySQL/MariaDB, PostgreSQL, etc. along with cloud-based big data engines like Hive and BigQuery. Visualization and analytics tools require well-engineered datasets before they can produce useful results, so the data layer is the foundation on which everything rests.
Looker with its Conversational Analytics integrations with Gemini AI and BigQuery, and proximity to data automation tools like Google Cloud Composer's implementation of Apache Airflow, is rapidly gaining footholds in the enterprise space. Building Python data modelling and manipulation skills using distributions like Anaconda is also super valuable. The main thing to keep in mind is that the visualization/presentation tool is only one small layer in the stack that makes data usable to solve problems.
Updated
Sindhura’s Answer
You're on the right track by concentrating on SQL, Power BI, and dbt—these skills are very important for BI jobs today.
1. BI Tools in the Market:
While there are many tools out there, most companies stick to a few main ones: Power BI, Tableau, Looker, Qlik, and SAP Analytics.
2. Most Valuable BI Tools in 2025:
For starting out, focus on Power BI and SQL. Once you're comfortable, consider learning Tableau or Looker. Knowing a bit about the modern data stack, like dbt and cloud warehouses such as Snowflake or BigQuery, will give you an edge.
3. Best BI Niche to Start With:
Look for roles like BI Analyst, Reporting Analyst, or Data Analyst with a BI focus. These jobs involve creating dashboards, reports, and insights. As you gain experience, you can move up to roles like BI Developer or Analytics Engineer.
Concentrate on building strong basics, working on business-focused projects, and maintaining a clean portfolio. Your skills will shine through, no matter your background.
1. BI Tools in the Market:
While there are many tools out there, most companies stick to a few main ones: Power BI, Tableau, Looker, Qlik, and SAP Analytics.
2. Most Valuable BI Tools in 2025:
For starting out, focus on Power BI and SQL. Once you're comfortable, consider learning Tableau or Looker. Knowing a bit about the modern data stack, like dbt and cloud warehouses such as Snowflake or BigQuery, will give you an edge.
3. Best BI Niche to Start With:
Look for roles like BI Analyst, Reporting Analyst, or Data Analyst with a BI focus. These jobs involve creating dashboards, reports, and insights. As you gain experience, you can move up to roles like BI Developer or Analytics Engineer.
Concentrate on building strong basics, working on business-focused projects, and maintaining a clean portfolio. Your skills will shine through, no matter your background.
Updated
Stacy’s Answer
Hi Anjali,
It's great that you're thinking about a career in BI! Working in ETL makes you valuable in many industries because executives need to understand the stories data tells. While others have mentioned some excellent BI tools, I want to highlight the importance of Microsoft tools. Learning Azure, Power BI, Power Query, and other Microsoft technologies is always beneficial.
Consider taking courses that explore the connection between BI and AI to prepare for future opportunities. Microsoft offers many helpful resources for this. It's also useful to look at examples of dashboards, both unique and commonly used by leaders. Leaders value a clean user interface, but they care most about the data's story and how BI can reveal it.
Best wishes,
Stacy
It's great that you're thinking about a career in BI! Working in ETL makes you valuable in many industries because executives need to understand the stories data tells. While others have mentioned some excellent BI tools, I want to highlight the importance of Microsoft tools. Learning Azure, Power BI, Power Query, and other Microsoft technologies is always beneficial.
Consider taking courses that explore the connection between BI and AI to prepare for future opportunities. Microsoft offers many helpful resources for this. It's also useful to look at examples of dashboards, both unique and commonly used by leaders. Leaders value a clean user interface, but they care most about the data's story and how BI can reveal it.
Best wishes,
Stacy
Updated
veronica’s Answer
Hello Anjali,
Here’s a clear path forward that might help you :
Master SQL first – Both DBT and Power BI rely heavily on SQL.
Take a beginner DBT course (e.g., dbt Learn or Udemy).
Practice building dashboards in Power BI with real data (e.g., Kaggle, Google Sheets).
Create a portfolio project showing a full pipeline: DBT → data model → Power BI dashboard.
Join communities like dbt Slack, Power BI Reddit, and Locally Optimistic.
Good luck and hope this information was useful!
Vero.
Here’s a clear path forward that might help you :
Master SQL first – Both DBT and Power BI rely heavily on SQL.
Take a beginner DBT course (e.g., dbt Learn or Udemy).
Practice building dashboards in Power BI with real data (e.g., Kaggle, Google Sheets).
Create a portfolio project showing a full pipeline: DBT → data model → Power BI dashboard.
Join communities like dbt Slack, Power BI Reddit, and Locally Optimistic.
Good luck and hope this information was useful!
Vero.
I appreciate you taking the time to answer this.
Anjali
Updated
Evita’s Answer
Hi Anjali!
Nice to meet you. I’ve been working with reports for several years now, and I think it’s great that you’re learning SQL — it’s extremely important. Depending on the role or the tool you want to specialize in, having strong SQL skills gives you a solid foundation for working with data.
There are many tools in the market. As a developer, I’ve used Power BI, and as a user, Tableau. Regardless of the number of tools out there, what really matters is choosing one to specialize in and understanding how it works — because in the end, they all serve the same purpose.
There are many niche areas in the data world where you can grow, and it’s also a good idea to identify which one you feel most aligned with. Business knowledge is always going to be a key factor, and with the rise of AI, I truly believe this can take your career even further.
Nice to meet you. I’ve been working with reports for several years now, and I think it’s great that you’re learning SQL — it’s extremely important. Depending on the role or the tool you want to specialize in, having strong SQL skills gives you a solid foundation for working with data.
There are many tools in the market. As a developer, I’ve used Power BI, and as a user, Tableau. Regardless of the number of tools out there, what really matters is choosing one to specialize in and understanding how it works — because in the end, they all serve the same purpose.
There are many niche areas in the data world where you can grow, and it’s also a good idea to identify which one you feel most aligned with. Business knowledge is always going to be a key factor, and with the rise of AI, I truly believe this can take your career even further.
Thank you for giving me advice.
Anjali
Updated
Priyanka’s Answer
Here are some popular options:
1. Tableau
2. Power BI, a tool from Microsoft
3. IBM Cognos
4. Looker
1. Tableau
2. Power BI, a tool from Microsoft
3. IBM Cognos
4. Looker
Updated
MAGGIE’s Answer
Power-BI and Tableau are fantastic tools to explore. You can dive into learning Tableau with Salesforce's Trailhead platform. It's free to sign up, and the Trailblazer app makes learning exciting with fun challenges and rewards like badges and points for completing modules.
Here's a link to get you started: https://www.salesforce.com/account-benefits/
Here's a link to get you started: https://www.salesforce.com/account-benefits/
Delete Comment
Flag Comment