So I am starting a new job soon and I want to understand better how to build an attribution model. So far I have only worked with the in-platform attribution but I need to have a better understanding of how to build a model myself. My biggest question at this point is: how do you connect all the data sources?? Since each data source has their own unique ids and granularity. I have a good understanding of how to dedupe data in SQL/Python but I don't understand how to join these data sources, from my understanding they are all completely different. Which fields are the ones that can be used to establish a connection?

If you can give me an example from your own experience that would be super helpful, because I've been trying google and chatgpt but the explanations are all very basic and not realistic at all.

Want to create a custom dashboard based on information in a database. Some of the charts will require custom formulas. Eventually, some information may come from sources outside of that database such as via an API, Google Analytics or other popular source, and even a separate database.

The dashboard will be shared with three viewers, each at a different organization.

The tool should be free (or inexpensive). It should also integrate with Google Workspace so that information in the tool can be programmatically presented in a Google Doc or presentation.

We've been using Google Sheets to access the data, cut it, and present it in charts. While I appreciate its flexibility and power, there have to be better options.

Thanks!

Welcome to the 'Entering & Transitioning into a Business Intelligence career' thread!

This thread is a sticky post meant for any questions about getting started, studying, or transitioning into the Business Intelligence field. You can find the archive of previous discussions here.

This includes questions around learning and transitioning such as:

• Learning resources (e.g., books, tutorials, videos)
• Traditional education (e.g., schools, degrees, electives)
• Career questions (e.g., resumes, applying, career prospects)
• Elementary questions (e.g., where to start, what next)

I ask everyone to please visit this thread often and sort by new.

When it comes to business intelligence, we spend a lot of time optimizing data pipelines, refining analytics, and ensuring dashboards provide actionable insights. But one thing that often gets overlooked is the foundation—our hosting infrastructure.

Slow or unreliable hosting can cripple performance, impact data availability, and even skew real-time analytics. If your dashboards take forever to load or your data connections time out, you’re not getting the efficiency you need. This is especially true if you're running client-facing applications where uptime and speed directly affect user experience and business decisions.

I recently switched to a hosting provider that prioritizes speed, security, and uptime, and it’s made a noticeable difference in how quickly I can access reports and run queries. If you're scaling your BI operations and need a solid foundation, investing in good hosting is worth it. For anyone looking into this, this option has been working well for me.

Curious—has anyone else here noticed the impact of hosting on their BI tools and workflows? Would love to hear what setups others are using

Are you in the early stages of getting your data infrastructure in place?

• Pulling data from 4-5+ different sources
• No data warehouse yet
• BI = spreadsheets and manual reports
• Constant back-and-forth with business teams for analytics

I’m working towards a solution to get all of this up and running in minutes.

Would love to discuss and get feedback.

What are your top BI use cases?

Hi, I have been reading through alot of posts to try to answer my question, but haven't had much luck. Probably because it's so situational

My question: Is it worth learning to set up a central database and a reporting tool to replace excel for what is currently small data?

Background: I have no formal training in data analysis, only excel courses and my own curiousity. I built several tools in Excel and handled data projects for our area manager, which led to my promotion to a BI position. There are no other BI positions in the company, and when I spoke to our ERP developer I was told we only use Birst, and that is only used for distribution. SQL and Python are on my list to learn, and I'm currently playing around with PowerPivot and Access.

Our branch currently tracks customer metrics (problem cause, downtime, etc) for service contracts using an Excel sheet I built and PowerPoint for reports. Each branch deals with different customers and handles their own data.

I am currently pushing for a standardization of the data we collect across the area to start with, and I'm wondering if I should push for centralization now as well. While excel and PowerPoint are working fine I feel we should plan beyond that; especially since these reports set us apart from competitors.

If anyone else has been in the position of starting completely from scratch I would very much appreciate your insights

I've been looking for a good option to act as the front end for some postgres dbs I've built for some charities and local organizations. They don't have the budget for me and a team to develop a full CRUD front end for the database.

I've tried NocoDB and some other open source airtable-like solutions, and Noco worked the best but it's just so incredibly slow. I'm wondering if a BI solution would be better to hook into and create dashboards for a front end. They really just need to see and export tables that are associated with contact managers, and then see stats that are easily pulled based on those tables. But most of the data is more tables and less graph requirements.

https://www.reddit.com/r/BusinessIntelligence/comments/1h7x4tg/comment/m0tib3g/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

I saw this so I thought I'd ask here what would be the best solution for something like my use case. Top comments say Grafana is for metrics which I think is what I need. Superset also looks good, but so many examples are for graphs. My usecase:

• Instead of graphs I need tables to just present people their list of people
• I need some good auth tools to just provision what people can see
• I don't need interactivity like a regular CRM or CRUD since we have other data ingestion points for the data.
• I need the data to be available per page load between the front end hosted/directed to a subdomain like data.domain.com and the postgres database.
• want to be able to invite volunteers and organizers and give them select viewing access to different rows of the relevant tables so ABAC RLS

let me know what BI tools if any can solve my needs while being cost-effective for clients.

Our last poll on “Which BI platform will you use most in 2025?” had 100s of responses—thank you to everyone who contributed to the poll and discussion.

This time, wanted to hear about how (and if) you’re using AI in your BI work -- feel free to add some color in the comments -- especially curious about what AIs are being used.

View Poll

Hey guys!!

I need to create dashboards connected to Amazon Athena tables. These tables update daily in the morning, so the dashboards must refresh automatically when the data changes.

I’m looking for a way to schedule these updates from Athena without manual intervention. Unfortunately, using Power BI for this requires a Pro license, which I don’t have access to at the moment.

I’m currently exploring Metabase (open source), but I’m not sure if it meets this requirement.

Do you know of any free platforms or solutions that can help with this?

As the title states, which BI platform will you use most in 2025? Feel free to share more in the comments, data team size, dashboards or decks, other reporting formats etc.,

View Poll

Hello everyone,

Hope you are doing great !

I am lead data in a startup (80 pers and 15-20 data users), i just joined the company 1 month ago and there is no BI tool yet. Anyway, I check Omni, Holistic and Thoughspot.

The most important is : ability to analyze and create dashboard without technical skills, and easy semantic layer

Anyone has a feedback to give me ??

Thank you,

Alexandre

Hi
I need some guidance here on selecting the best tool for the job.
We use BigQuery as data warehouse - we have quite a lot of data (1.5 PB ish) and a lot of data coming in on a daily basis. To improve cost, performance and so on we build our data marts aggregated to daily, weekly or monthly basis - i.e. three different tables with different granularity. We have the same measures in all three - so for instance 'count distinct users' - this way we also handle the non-additive measures. To add to the complexity we also create the data marts with different dimension sets - also to handle the non- or semi-additive measures. Basically created OBT style.

Now, we are in the process of choosing a new BI tool.
We want to be able to let the users seamlessly choose the measures they want with the dimensions and time granularity they want, without them having to care about which data source to get the data from, to see an output that makes sense.

So basically I want to create metadata that tells the BI tool that this measure 'count distinct users' when seen on daily level with dimension A,B and C - you go to data source A, when you see it on weekly, with dimension B and C - go to data source B and so on. All these data sources are already created and materialized in the data warehouse.

So I need a semantic layer to define this logic. While this is what I would expect to be able to do with a semantic layer, this is apparently not straight out of the box.

I've tried Looker and LookML, but they tell me that you have to create the world's biggest IF ELSE statement to be able to do that, which sounds horrible and very hard to maintain.

I've also looked into dbt semantic layer, cube, atScale and holistics - and holistics seem to be the only tool where this is actually possible to do with some ease.

Anyone with some experience/knowledge/know-how in tackling this challenge?
Thanks!

Hello! Can anyone recommend any upcoming conferences in Europe that focus on Business Intelligence, data analytics, and related technologies? We're particularly interested in events where we can dive deep into various BI tools, network with industry experts, and gather insights to make an informed decision.

Thanks in advance!

Hi everyone,

I'm looking for advice on how to modernize the reports and data visualization we offer our clients in our gym management software. Also, I am pretty tech ignorant, so explain things to me like I am five because I may have the understanding of a 5 year old when it comes to this.

Currently, our reports are pretty basic – mostly tables with limited filtering options. We want to provide our gyms with more insightful and visually appealing reports, potentially including things like customer acquisition cost and lifetime value.

I've come across BI tools like Looker, Power BI, Tableau, and Holistics, but I'm not very tech-savvy and could use some guidance. I've also seen freelancers on Upwork who build dashboards with these tools.

What's the best approach to upgrade our reporting and data visualization for our gym clients? Should we build something in-house, use a third-party tool, or hire a freelancer? Any advice would be greatly appreciated!

Thanks in advance!

Newbie BI manager here, building the function from scratch.

We have 5-10 data sources we want to batch send to our data warehouse (Snowflake). We also need to send less than 1% of the warehouse data back into the primary sources (e.g. Salesforce).

We're evaluating both Mulesoft and Fivetran right now.

Mulesoft:

• Looks more complex to maintain as a non-engineer
• Quoted 3 month implementation using their partner for our relatively simple use case
• Expensive $$$ but willing to negotiate with their Q4 ending this week
• Includes "reverse ETL" functionality for our 1% of data going back into the sources
• Specialized in system integration

Fivetran:

• Extremely simple GUI
• Implementation already 80% complete with their free trial
• Less expensive
• Requires partnering with a second vendor for our "reverse ETL" use case
• Specialized in data integration / batch uploads to warehouse

I'm leaning towards Fivetran but unsure how to best handle the "reverse ETL" issue. I received a quote from Hightouch (one of Fivetran's recommended "reverse ETL" vendors) that was just as expensive as our Fivetran quote, which seems bananas considering the difference in data volume.

In many industries companies are dealing with a mix of numerical (e.g. prices, transaction data), categorical (e.g. product categories, user characteristics), text (e.g. product descriptions, reviews), geo (e.g. store locations) and image (e.g. product images) data. It feels though that BI tools don't support this mix of data types well. So for instance I want to understand why churn increased in e-commerce. The relevant info can come from a mix of prices, affected product categories, user reviews or even product images, and it might be location specific. For those who have to deal with this, how do you go about it now without having to create dashboards with endless amounts of plots? What does your toolkit look like?

Once someone has accumulated several years of work experience, the general sentiment is that side projects aren't particularly valuable; however, is that always the case?

As an example, we all know that visualization tools are all very similar, and the ability to develop carries over from one stack to another. But, in the current market, is it worthwhile to create comprehensive projects to demonstrate skills in order to get your resume past HR?

A relevant example would be myself. I have 3 years of BI development experience, using: PowerBI, Tableau, SSIS, SQL, BigQuery, etc.

My most recent role is Tableau/SQL/SSIS centered. If I'm applying to PowerBI roles, would it be worthwhile to have a project to demonstrate that, since my "on the job" PowerBI experience was several years ago?

The same line of thinking goes towards other tools: i.e. Qlik Sense and Looker. I see a number of BI developer roles using these tools, and while I'm confident in my own ability to use these, I want HR/the hiring manager to feel likewise.

Does anyone have any advice regarding this situation? I want to be a competitive applicant; however, I don't want to artificially inflate my resume with projects if it is to my detriment. Thanks!

Hey redditors,

I work on a report, part of which will be based on analysis of Zendesk tickets of customers to our Tech Support team.

I am looking for a BI solution which allows to load Zendesk ticket data (e.g., .csv) and analyze it from various angles.

Ideally such BI solution shall be AI- enabled, at least it should be able to analyse ticket content, categorise tickets accordingly and summarize data from tickets of a certain category.

Which BI solution do you recommend and why? Ideally it should have a subscription/pricing tier which can be used in my case - my project is not approved yet, so $500/month is the budget for BI which I'd like to not exceed. If everything goes fine, we will use the chosen BI company-wide on Enterprise terms.

Thank you!

Hi everyone,

I’m struggling to figure out my next steps in the BI field. I’ve been working in BI for 3 years: 2 years at a consulting firm: i built dashboards in Tableau, then Power BI when the company switched to Microsoft solutions. I worked with strong teams (DBAs, UX engineers) and myself worked a lot with DAX, Power Query (M), and even custom visualizations using Deneb. I also designed UX/UI solutions in Figma/Adobe.

After that I worked for 1 year on a Power BI + Power Apps project: there focus was mainly on huge datasets, dashboards with almost only tables, and power apps for editing/adding data. Admittedly, I definitely feel more strongly about the visual layer, but I enjoyed doing more advanced dax, digging into the data and writing queries to get what I needed from the data when I used direct query.

The problem is, while I know DAX and Power Query well, my SQL and Python skills are basic. Most of what I accomplished with SQL was through trial and error, ChatGPT, and Stack Overflow. I can find solutions efficiently because I understand very well what must be done with data in order to achieve desired results, but I don’t have “advanced” skills in SQL, Python, Snowflake, or AWS—common job requirements now.

At interviews, I’m often asked to explain what specific SQL clause does and to give specific definitions, and I feel I’ve missed the shift where visualization-focused roles are no longer needed. I love working on visualizations, from Figma designs to writing Vega/Vega-Lite code in Power BI just to achieve perfect balance between data part and user experience part. I’ve always wanted to learn D3.js, but I worry it’s too niche, and instead, I should focus on SQL/Python to stay employable.

How would you approach this? Should I focus on SQL/Python and “clench my teeth,” or is there still a chance that data visualization is not dead? I'm writing about this in the hope that some of you have struggled with a similar problem and maybe can share their path because now I feel completely lost. Or maybe someone would be able to recommend good resources for sql and python, that would be sufficient to at least satisfy recruiters and give me more time to learn in more depth.

How many of you strictly build reports vs getting rangled into building/designing processes and even asked to make internal apps for data collection and day to day ops (rather than high level dashboards)?

Is this normal in the industry or just for me?

Hey Reddit,

I've been pondering with this question for a while and would appreciate some perspective. Proving ROI for sales, marketing, and product are clear. Sales teams close deals, marketing draws leads, product improves user experience/launches new features—all directly linked to revenue.

But when it comes to proving ROI for data teams, it gets a little unclear and challenging? Although we help all the above functions make better, informed decisions, our contributions may seem not easy to quantify impact.

How can we better highlight value when it's not directly tied to the bottom line?

I have almost 14 years of work experience as a Business Intelligence and Data Analytics professional. I have built, managed, and grown BI teams from scratch. Even today, I am equally hands-on with my own BI deliverables. I am well versed in different flavours of SQL, Tableau, QlikView, Power BI, and SSRS and can easily transition to anything that requires me to process and analyse data (ETL - SQL,SSIS, Alteryx, Python, QlikView Scripting).

What next keeps me bugging? I have applied to multiple jobs over the last six months but barely get a call. My assumptions for not getting a call are that I have already been paid well for the role and that the jobs might not have that budget, though the skills match. I try to fine-tune my resume per the job. It seems like I have reached a plateau.

I am unclear on what to do next. I love to solve problems, help teammates resolve issues and keep learning. I always like to have a hybrid role where I can lead as well as execute. I try to be aware of new updates across BI tools and at least understand how things work. I love data, storing, processing, modelling, etc. I do not have any domain expertise as such, but I have worked across Financial services (M&A, Capital markets, wealth management, etc), Internal Audit, Operational Analytics, Risk and Compliance, Internal Audit, People Analytics and many more. I am interested in learning more about Sustainability and Supply Chain, which I will pick up this year.

I am currently all over the place, with no clear path around what next? Options revolving in my head are:

• Learn/Move into DE, manage Big data, cloud, lakes --> Databricks, Snowflake, Fabric, etc.
• Learn Business: Supply Chain, Sustainability, Wealth Management, Risk, Internal Audit
• Lead vs. IC in the BI space

Thanks.

PS: If you have suitable roles for me, please do reach out as well.

With the advent of several AI Analytics startups with BI products . Is it possible that entry level analysts will be completely replaced by AI? How can we safeguard ourselves against this possibility?

Hey all,

I work in a data team and a huge problem I have is a lack of clarity around who owns what. Whether it’s a specific dataset, a business rule, or even a key workflow, it’s often hard to figure out who’s responsible.

What ends up happening is the data engineers typically implement their best understanding of ‘what should be’ based on scattered conversations with various business teams and nothing is clearly documented. (That doesn’t stop those same business teams from getting frustrated at the output not quite being right though!)

If your organisation does assign clear ownership, how do you do it? Do you use any systems or tools? I’d love to hear how others deal with this, thanks a lot!

Hi everyone,

I’ve just qualified for the second round of the Claims BI Analyst position at an insurance company. For this round, I’ll be given an analytical task (expected to take 2–3 hours) where I need to analyze data, present my findings, and answer a few interview questions based on the task.

I’d love to hear from fellow BI professionals about how I can best prepare and impress the recruiter. Specifically: • What’s the ideal way to structure my findings and presentation to stand out? • Any tips on what questions they might ask during the presentation or interview?

I’ve got two days to prepare and want to make the most of it. Any advice or suggestions would be greatly appreciated!

How many people do different companies employ in BI or other data-related roles? Is a team of five big or small? How does that correlate with total company headcount or annual revenue?

We are four data people in a ~450 person company, and I am surprised to sometimes hear management talk about our team as large.

Hi guys, I just finished reading Fundamentals of Data Engineering and wrote up a review in case anyone is interested!

Key takeaways:

1. This book is great for anyone looking to get into data engineering themselves, or understand the work of data engineers they work with or manage better.

2. The writing style in my opinion is very thorough and high level / theory based.

Which is a great approach to introduce you to the whole field of DE, or contextualize more specific learning.

But, if you want a tech-stack specific implementation guide, this is not it (nor does it pretend to be)

https://medium.com/@sergioramos3.sr/self-taught-reviews-fundamentals-of-data-engineering-by-joe-reis-and-matt-housley-36b66ec9cb23

Hey all,
What plans to you have to update your data/BI stack this year? I'm getting constant calls from Google Cloud, but would most likely chose Snowflake or even lean towards microsoft Fabric/Azure -- and all this only if I can adjust the budget and team. It's a huge project, and don't get me started on AI -- definitely a place for it, but not seeing any real impact yet, when compared to seasoned analysts.

Where do you anticipate your stack landing by EOY?