The need for good self-serve analytics tools has never been greater. There's more data in more places than ever before. Legacy analytics tools like Excel can't handle big data and can be security risks. More powerful BI tools are complex, expensive, and slow with big data.
In this guide, we'll review top self-serve analytics tools, including:
- Row Zero
- Google Sheets + add-ons
- BI tools connected to Excel or Sheets
- Use case specific analytics tools
We'll also explore choosing a self-serve analytics tool:
- What is self-serve analytics?
- How to choose a self-serve analytics tool
- Self-serve analytics vs guided analytics
- AI and self-serve analytics
Best Self-Serve Analytics Tools
The best self-serve analytics tool for your org will depend on your team needs, data size, and budget. In all scenarios, it needs to be easy to use and easy to access data. That's why modern spreadsheets are a critical component of any self-serve analytics tool set, since they are universally accessible and the go-to data analytics tool for non-technical users. The best solution is likely an enterprise spreadsheet like Row Zero, or a BI tool that connects to legacy spreadsheets like Excel and Google Sheets. Here are top tools to consider:
1. Row Zero - a connected spreadsheet for big data
Row Zero is an enterprise-grade spreadsheet built for big data, security, and data connectivity. Row Zero works like Excel and Google Sheets but supports billion row spreadsheets (1000x Excel's limits). You can easily connect to data sources and build connected spreadsheets that auto-update. Row Zero's advanced security features are built for self-serve data analysis - organizations can restrict data export, sharing, copy/paste, etc. and workbooks inherit each user's row level security from the data warehouse.
Why Choose Row Zero
Row Zero offers several unique advantages over legacy spreadsheets:
- Big data power and speed to handle 1000x bigger datasets and heavy computations
- Built-in data connectors to cloud data sources like Snowflake, Databricks, Redshift, Postgres, etc. to streamline import, automate data updates, and enable dynamic self-serve reporting and dashboards.
- Enterprise-grade security that significantly improves spreadsheet data governance
- Works like Excel and Google Sheets so your team can be instantly successful and comfortable using the application. Row Zero provides the power of a BI tool in the comfort of a spreadsheet.
2. Google Sheets plus Add-ons
If your teams work with smaller datasets (under 10,000 rows), combining Google Sheets (or Excel) with add-ons may be your best self-serve analytics solution. Spreadsheets are the go-to data analysis tool for non-technical users. The best way to make spreadsheets a self-serve data tool is to connect them to your data sources. There are a number of add-ons that do that. Some data platforms have dedicated add-ons (e.g. Databricks for Google Sheets) and there are also solutions like Coefficient and Coupler that connect to many data sources (e.g. Salesforce, Hubspot, Quickbooks, etc.). Read our review of Coefficient and alternatives.
Similarly, you can often connect BI tools (e.g. Tableau) and use-case specific tools (e.g. Anaplan) directly to spreadsheets for further analysis.
There are three primary drawbacks with this approach:
- Data limits - Google Sheets and Excel aren't built for big data and will slow down or crash as you approach their data limits.
- Cost/management - If add-ons need to be purchased for each user, the costs can quickly add up. For example, Coefficient's Pro plan is $99 per user per month. For comparison, Row Zero is $20-25 per user per month for Business plans.
- Security - Without proactive management, typical spreadsheet usage in organizations can lead to security risks. You especially want to avoid letting non-technical folks access sensitive data and export to CSV, since this can lead to untraceable data leakage.
3. BI tools connected to Excel or Sheets
One of the most common self-serve business intelligence solutions is to have data teams build out BI tool dashboards where users can access and explore data and export to CSV for further analysis in a spreadsheet.
This creates a pathway for non-technical folks to get access to subsets of large datasets and real-time dashboards. It's also possible to connect some BI tools directly to Excel and Google Sheets to streamline data import.
While this approach can work well for stable, re-curring dashboards, it doesn't work well for ad hoc analysis and has several drawbacks:
- Not truly self-serve - If the data team needs to build dashboards to unlock non-technical users, then the solution is not self-serve. It's guided analytics. Most BI tools are too complex for non-technical users to build data analysis from scratch.
- Dashboard overload - Because this BI tool approach is not self-serve, it leads to a lot of dashboards being built for one-off data requests which leads to dashboard overload and too many data requests.
- Performance and data limits - While they can often support bigger datasets, BI tools can be slow to actually explore the data when filtering or editing dashboards. Users will also still face the same restrictive limits when exporting to Excel or Sheets.
- Cost - BI tools can be expensive when deployed across large teams and they can also run up data warehouse costs if not managed appropriately since they often sit on top of the data warehouse.
- Security - One of the most common things non-technical folks do with BI tool dashboards is export to CSV, which leads to security and data governance issues, yet placing restrictions on exporting data may defeat the purpose of using BI tools for self-serve analytics.
These drawbacks are why many organizations seek out alternative self-serve business intelligence tools like Row Zero.
4. Use case specific analytics tools
If your end users are all on the same team doing similar functions, you may benefit from use case specific self-serve data tools. Some examples include Anaplan for finance teams, Health Catalyst for healthcare analytics, Shopify Analytics for e-commerce analytics, etc.
If you have a common data source that your team uses (e.g. Salesforce), then try searching for "self-serve analytics for [data source]". Sometimes these industry-specific BI tools are best optimized for your data source and needs, with pre-built dashboard templates and calculations. Most will make it easy to export the data to a spreadsheet for further analysis. Like the #2 and #3 solutions above, just be aware of data limits, costs, and security risks with this approach.
What is self-serve analytics?
Self-serve analytics is an approach to data analysis that enables non-technical users like sales managers, business analysts, marketers, and executives to independently access, explore, and analyze data without relying on data or IT teams for support. Modern self-serve analytics platforms make it easy for non-technical users to:
- Access pre-approved, governed datasets or dashboards
- Build reports and charts without coding or SQL knowledge
- Explore and filter data through dashboards or spreadsheet-like interfaces
- Answer business questions and solve problems quickly and independently
A key component of self-serve analytics is a no code analytics tool which lets non-technical users analyze, clean, transform, and visualize data without writing code. Spreadsheets are the ultimate no code analytics tool since business users can perform complex calculations with simple formulas, buttons, and drag and drop actions.
Goals of self-serve analytics
The goals of self-serve analytics solutions are to:
- Democratize data access - Make big data accessible to more people and make analysis easy to understand and explore.
- Speed up decision-making - Allow teams to quickly analyze the data they need to run their business unit.
- Reduce data requests - Reduce or eliminate dependency on centralized data teams, which can create bottlenecks and slow down decision making.
- Improve efficiency - Make data analysis fast, streamlined, and automated so teams can operate efficiently.
A good self-serve analytics platform creates a data-driven culture that is efficient and collaborative.
How to choose a self-serve analytics tool
When choosing a self-serve analytics tool, the most important thing is to keep the end users in mind. Ultimately, you need a tool that non-technical folks will consistently use without assistance. It needs to be easy to use, easy to access data they need (ideally automatically), and easy for non-technical folks to do their own analysis (charts, calculations, etc.). The best self-serve analytics tools for your company will depend on your goals, budget, and answers to three key questions:
1. Who is the end user?
Is the end user the whole company, a department, or a small functional team? If it's the whole company or large department, you'll need something universally accessible and shareable like a spreadsheet. If end users are limited to a specific function, then you may do best with a function-specific BI tool that has pre-built templates and workflows for that use case.
2. How big is the data they need?
Many tools have data limits or get very slow with big data. The Excel row limit is 1,048,576 rows. Google Sheets limits are similar. Many BI tools are slow to filter, edit, and calculate big data.
Under 10,000 rows
If your datasets are consistently under 10,000 rows, you can use most tools, and legacy spreadsheets like Excel and Google Sheets can be a great self-serve analytics tool when paired with an add-on to automate data import and updates.
Above 100,000 rows
If your datasets are consistently above 100,000 rows, you'll likely need a more powerful spreadsheet like Row Zero, which can handle millions of rows on a free plan and billion row datasets on enterprise plans. You can also try 'spreadsheet-like' BI tools, but you may find folks still try to export to Excel.
If your datasets are between 10,000 and 100,000 rows, you may be able to get by with Excel and Google Sheets, but may need more powerful BI tools for one-off analysis when things get too slow or freeze.
3. Where is the data they need?
To make data self-serve, you need to make it easy to get any data needed into the tool. Ideally you can directly connect your spreadsheet to your data sources to streamline import and automate data updates. Eliminating manual downloads and uploads is critical to efficiency and data governance.
- Mostly files: If your data is always in smaller CSV files, you can simply upload into most tools. If your files are too big for Excel or are in big data formats like Parquet, JSONL, or .gz, use Row Zero. Using files has significant drawbacks in terms of efficiency, data integrity, and data security, so try to connect to source data and avoid files. If you must use files, set up a dynamic where users can update spreadsheets with new files to avoid rebuilding analysis.
- A few data sources: If your data is in one or two software tools (e.g. Salesforce) or one data warehouse (e.g. Snowflake) then you'll want to optimize your self-serve BI tool for this data source.
- Many data sources: If your data is in many software tools (e.g. Stripe, Quickbooks, and Salesforce), then you'll want a solution that supports data transfer from many sources.
Evaluate what data is needed on both a recurring and one-off basis. Where is the data? How big is the data? What doesn't work with your current data analytics tools? What's a reasonable budget? From there, you can evaluate the best self-serve analytics solutions.
Self-serve analytics vs guided analytics
While self-serve analytics may be the goal, the reality is many organizations have more of a guided analytics setup, where data teams build dashboards for business teams and ad hoc analysis can be a bit of a mess.
Guided analytics can offer significant benefits - business users can access pre-built real-time dashboards of governed data and can be trained for a specific tool, dashboard, or use case. Guided analytics can be very efficient for recurring use cases. However, the negatives are significant - it can be a challenge to answer follow-up questions outside of the pre-built analysis and business users may struggle to get the raw data they need to solve problems. Centralized data teams can quickly become a bottleneck and business users will likely seek workarounds to get the data they need, including exporting sensitive customer data out of CRMs, ERPs, etc.
The conflict between self-serve analytics vs guided analytics in an organization can lead to different sources of truth, different numbers that need to be reconciled, and significant data governance issues.
Here's a good rule of thumb - if end users need to make data requests or still export a CSV file to open in Excel, then you probably don't have a self-serve BI tool, you have a guided analytics tool.
AI and self-serve analytics
AI is increasingly becoming a core part of self-serve analytics, but it can also widen the gap between the more tech savvy users and less tech savvy users. Self-serve analytics is intended to empower non-technical folks and some of these less technical folks may also be less savvy using AI tools. AI can be very helpful and drastically improve efficiency, but it also makes mistakes despite providing confident-sounding answers. This can create some risks where less technical folks rely fully on AI analysis without the ability to recognize when it makes mistakes.
A good balance may be to encourage use of AI for self-serve data analysis but provide training for how to best leverage AI for data analysis and how to scrutinize AI-driven data analysis.
Alternative self-serve BI tools
There are several alternative business intelligence tools that are focused on providing a more user-friendly experience for non-technical users. These include spreadsheet-like BI tools and other DIY analytics tools:
- Metabase is a free self-serve analytics tool that is open-source.
- Sigma is a collaborative data workspace with a spreadsheet UI that connects to cloud data warehouses. Here's a review of Sigma features.
- Rows is a re-imagined spreadsheet experience built around collaboration, interactivity, and data connectors. Here's a review of Rows features.
- Qlik has a self-service business intelligence tool that integrates with other data sources.
While these tools are focused on self-serve analytics, they still have a learning curve for non-technical users, have limitations compared to spreadsheets, and can be expensive compared to other self-serve business intelligence solutions like Row Zero.
Conclusion
By setting up a modern self-serve analytics platform, companies can improve productivity and data governance while making big data accessible to non-technical users. Good self-serve analytics is critical to establishing a data-driven culture. The best self-serve analytics tool for your org will depend on your team needs, data size, and budget. The good news is there is already a universal analytics tool that everyone knows how to use - a spreadsheet - and spreadsheets have dramatically improved in recent years. Modern spreadsheets like Row Zero can handle 1000x more data than Excel and connect directly to data warehouses like Snowflake, Databricks, etc. And if you're still using Excel and Google Sheets, there are add-ons like Coefficient that connect directly to data sources like Salesforce, Hubspot, Stripe, etc. to automate data updates.
If you can make modern spreadsheets work, then use spreadsheets. They're universally accessible and provide ultimate flexibility across use cases and skill levels. If modern spreadsheets and add-ons don't cut it, then things will be more complicated and expensive and you'll likely need to pair BI tools with spreadsheets.
If you're evaluating self-serve business intelligence tools, be sure to test out Row Zero. Row Zero matches the user experience of Excel and Google Sheets with the power and connectivity of a modern BI tool. It's a great alternative to Excel and Google Sheets replacement for big data users. You can try Row Zero for free to see for yourself.