February 21, 2025

7 Questions to Ask When Evaluating Business Intelligence Software

February 21, 2025

The pressure to use data effectively is mounting. From frontline teams to executive leadership, everyone’s looking for answers that data can provide. It’s about staying competitive and making better decisions, faster. The evidence is clear: Companies embracing data-driven strategies are six times more likely to be profitable year over year. That gap keeps growing as more businesses prioritize analytics.

As a result, investments in analytics and business intelligence (A&BI) are climbing. A recent report found that 62% of mid-size and large companies are investing more than $50M into big data efforts, and 12% are spending over $500M. Even better, 92% of them say those investments are paying off. Still, even with a massive budget, the software you choose makes or breaks your ability to turn data into value. And let’s be honest, the BI vendor market is crowded. Between flashy features and legacy names, knowing where to start or what matters is hard.

If you're beginning your evaluation, there's a lot to consider: What differentiates modern BI tools? Which features will help your teams move faster? And what should your decision-making process look like?

This guide breaks down the seven most important questions to ask when evaluating BI software, along with context to help you make the right choice.

The BI software vendor landscape is gigantic, ranging from new entrants to megavendors.

If you’re starting to evaluate software, you may be unsure where to start. What’s the difference between all these BI software vendors? Which capabilities and features are important to have? How should you approach the process? What’s changed since your last BI purchase?

In the following blog post, we explore the top 7 questions every company should ask during the evaluation process and break down some top BI trends and features to consider.

A brief history of analytics and business intelligence software

If you’re new to the world of business intelligence, or revisiting after a hiatus, it’s helpful to have a solid background of where we’ve been — and where we’re headed.

Dashboards reigned supreme

Business intelligence has historically focused on tracking and curating data, usually in the form of dashboards or reports. Early BI tools were designed to surface high-level insights in dashboards on a scheduled basis, but lacked real-time reporting capabilities.

These dashboard-focused tools usually answered pre-determined questions executives raised in advance. If questions changed, or additional information was required to make decisions, the dashboards needed modification. Only technical people with knowledge of SQL or other coding languages could build them, which was handled by a member of the data or IT team — effectively making them the data gatekeepers.

On-prem constraints stunted insights

In the past, implementing a business intelligence solution meant building an on-premise data center and hiring an army of IT talent to manage it. Because data storage and compute was relatively expensive compared to today, data analysis was limited.

Outdated data was often deleted to save on massive storage costs, preventing long-term historical analysis. Computing and resource constraints, combined with business teams’ inability to dig into the data behind these dashboards and reports, limited ad hoc analyses to situations when it was absolutely necessary — leaving many questions unanswered. And without the ability to ask those questions on the fly, mission critical insights remained hidden.

What’s changed?

Data volume is exploding. From mobile apps and smartwatches to embedded sensors and enterprise platforms, organizations are generating more data than ever. In just 60 seconds, over 6 million people shop online, and more than 5 million Google searches are made.

This pace has fundamentally changed how businesses approach analytics. Legacy systems can’t keep up with the scale or speed required. That’s why organizations are increasingly turning to cloud infrastructure to handle modern data challenges. Modern cloud data warehouses (CDWs) are built to handle this kind of scale. They pull in structured and semi-structured data, scale elastically to meet demand, and allow teams to analyze billions of rows in seconds, without costly hardware or rigid extract processes.

However, while access to data has improved, using it effectively remains a struggle. A recent industry study found that 59% of businesses still cite poor data quality as their biggest blocker. Even with the proper infrastructure, insights often stay locked behind tools that are too technical or disconnected from how business teams work.

That’s why your BI solution matters. It should help your teams move quickly, confidently, and independently, because the ability to ask and answer data questions can’t be limited to just a few people.

Annual size of the global datasphere

Source: Data Age 2025, sponsored by Seagate with data from IDC Global Datasphere, November 2018

This new data economy is powered by the modern cloud data warehouse (CDW). Modern CDWs collect data from any source and scale elastically to support nearly infinite users and ad hoc analytic workloads. This includes support for unstructured and semi-structured data such as JSON. Storage and compute costs have also come way down, meaning historical data doesn’t have to be tossed, and technology can meet — and even exceed — the demand for insights.

It’s no surprise that analysts expect 83% of enterprise analytical workloads to be cloud-based this year. But despite the wealth of data available and many opportunities to harness it to drive decisions, 73% of companies fail to put it to use. Meanwhile, decision makers can’t access this data or uncover insights in a timely or efficient manner.

So where do you go from here? You need a BI tool built to thrive in the new data economy. But how do you know which one to choose?

7 must-ask questions for evaluating bi tools

If you’re comparing BI software, we’ve put together a list of questions to help guide your evaluation criteria and ensure you choose the best solution for your business’ needs.

1. Is the software solution built for cloud data warehouses?

Why it matters

As you evaluate analytics and BI software solutions, make sure to look at cloud-based tools that capitalize on CDW capabilities. Things change more quickly than ever, and teams need real-time data access to make sound — yet rapid — decisions.

Today’s volume and variety of data is much better managed in the cloud — not stuck in a slow on-prem database or sitting in an extract on someone’s PC. That’s why 68% of database market growth is in the cloud.

Unfortunately, many companies that have invested in CDWs still use BI tools meant to meet the needs of the pre-CDW era. These solutions fail to maximize the value of CDWs by requiring data extracts prior to analysis, making it difficult to analyze semi-structured JSON data, and presenting other roadblocks that slow down time to data insight.

What to look for

Most analytics tools available today have some form of cloud offering, but few were built for the cloud data warehouse. Seek a BI solution that gives teams direct access to data inside the CDW.

These modern BI solutions accelerate time to business insight by querying data live against your CDW and leveraging the compute power and speed of the cloud to quickly analyze massive datasets in real time. They also capitalize on cloud benefits such as elasticity, real-time data access, sharing, and usage-based pricing.

2. Does the tool require SQL or other proprietary coding knowledge to use?

Why it matters

A growing number of companies aim to make data accessible across their organizations. In fact, 91% of companies have identified self-service analytics as a priority investment area. The idea is simple: let business experts answer their own questions without waiting on a technical team.

The reality is that most “self-service” BI tools still require SQL or proprietary languages to explore data beyond a dashboard. That puts real data access out of reach for the people who need it most. Instead of empowering teams to dig deeper, BI teams often end up fielding a steady stream of requests, stuck in what many call “report factory hell.”

This model is inefficient and expensive. It creates bottlenecks, slows decisions, and pulls data experts away from higher-value work. If your company is serious about using data to guide decisions, your BI tool should support that vision with a truly accessible experience.

What to look for

Think about who needs to interact with data. If it’s more than just a handful of analysts, you need a solution that lowers the technical barrier. Look for tools that make it possible to explore data visually, write queries with or without SQL, and bring everyone closer to the information they need, without extra overhead.

Tools like Sigma combine the flexibility of a SQL runner with a familiar spreadsheet-like interface. That means domain experts in marketing, sales, or operations can jump in and start working with data without waiting in line. Sigma also offers a full SQL editing experience for analysts and engineers when needed.

This dual approach improves collaboration across roles, shortens the time to insight, and helps teams spend more time solving problems instead of pulling reports.

Because Sigma feels like a spreadsheet, users haven’t hesitated to dive right into Snowflake data for faster insights.” – How Clover Improved Time to Data Insights by 90% with Sigma‍

3. How difficult is it to analyze semi-structured data?

Why it matters

Your company is likely sitting on a ton of semi-structured data, such as JSON. JSON has become the preferred data interchange format for mobile devices, web applications, online services, and sensors. This includes some of today’s most popular websites like Facebook and Google — and the fast-growing market of wearables and IoT devices.

These services and devices produce an unprecedented amount of data in our digital economy. Unstructured and semi-structured data now make up 80% of the data collected by enterprises. And that number is only expected to grow. This data is a potential treasure trove for companies able to harness it effectively.

But combing through JSON in real time to find patterns, emerging trends, and insights has historically been challenging with BI tools. Extracting nested JSON rows and analyzing them for insights still requires a deep technical background — meaning it’s usually off-limits for those outside the BI team. Even for those versed in SQL, the process can prove to be time-consuming.

What to look for

To make the most use of JSON, you need to parse out its nested structure and analyze the relevant fields. Look for BI software that makes it possible to easily identify and parse relevant JSON, preferably without having to write SQL. This saves data experts valuable time while empowering business users to create data views that unlock the value of semi-structured data. It also helps to be able to join JSON with existing datasets for deeper analysis.

See how it’s helped companies like Clover, Blue Bottle Coffee, and Volta Charging become data-driven. See all customer stories →

4. Does it protect my data?

Why it matters

Organizations spend millions on their data warehouses, security solutions, and compliance initiatives. But all that spend can instantly be rendered useless by everyday business workflows like downloading data to a Microsoft Excel spreadsheet.

It might seem like downloading data to a personal computer is a trivial issue. But real-world events suggest otherwise. The average data breach costs companies $3.86 million.

Business experts aren’t looking to circumvent enterprise governance practices. They’re trying to get the answers they need to make better business decisions. And because they lack the coding expertise or extensive training required to work with data directly in most BI tools, they are often powerless to answer the questions raised in that last meeting or email. So they turn to what they know best: the spreadsheet

What to look for

Avoid shadow IT scenarios that lead to data breaches by investing in BI software that connects directly to your CDW and doesn’t migrate data or rely on CSV extracts. Employees always have guided access to relevant data and can generate insights without putting the company at risk.

You should also look for security and compliance features like object and row-level security, single sign-on, and user access permissions by team or role. Additionally, compliance certifications such as SOC II, GDPR, CSA, CCPA, HIPAA, and Privacy Shield help protect your data from falling into the wrong hands.

5. Does it have last-mile data prep capabilities (i.e., semantic modeling)?

Why it matters

After data is collected, transformed, and loaded into your cloud data warehouse (CDW), a final stretch of preparation is often needed to make it truly useful for analysis. This "last-mile" data prep involves refining data models, cleaning up inconsistencies, and adding business context. Without the right tools, this process can be time-consuming and may require specialized technical skills, creating bottlenecks and delaying insights.

Moreover, effective data visualization and reporting are integral to this stage. Transforming prepared data into clear, actionable visualizations enables stakeholders to grasp complex information quickly, identify trends, and make informed decisions. Tools that seamlessly integrate data prep with robust visualization capabilities can significantly enhance the value derived from your data.

What to look for

Seek a BI tool that offers intuitive last-mile data prep features and robust data visualization and reporting capabilities. This combination empowers business users to actively shape data models and create compelling visual narratives without relying heavily on technical teams.

Sigma provides a familiar spreadsheet-like interface, allowing users to perform complex data transformations and analyses directly within the platform. Its extensive library of charts and graphs enables users to turn complex data into clear insights. Features like drill-down capabilities allow for deeper exploration, while flexible scheduling options facilitate automated report delivery.

By integrating data prep with visualization and reporting, Sigma enables teams to collaboratively build, share, and iterate on data models and dashboards. This fosters a more agile and informed decision-making process, ensuring that insights are accurate and accessible.

6. Can you go beyond dashboard visualizations?

Why it matters

Data visualization has emerged as one of the best ways to absorb large amounts of information, present data to key stakeholders, and tell a compelling story. While the human mind is capable of incredible feats, most of us cannot easily understand complex statistical models or digest large datasets.

However, we are adept at spotting patterns within visualized data. That’s why BI dashboards are so popular. When done right, data visualizations give the viewer insight into the trends, goals, and metrics contributing to the business.

While dashboards are a great way to get a high-level view of what’s happening, they aren’t going to answer every question. In fact, dashboards usually raise more questions. Why is website traffic slumping? Why was there such a significant drop in sales leads from Texas? Why did we see an increase in customer service requests in Q2? You get the idea.

These follow up questions lead to deeper insights and help business leaders steer decisions. Unfortunately, many BI tools don’t provide users with the ability to drill down into the data presented in a dashboard. To get their answers, they have to go back to the BI team. This effectively turns dashboards into stop signs, not the starting points they should be.

What to look for

First, seek out a solution that doesn’t require manually writing SQL to make interactive data visualization dashboards. This opens the door for business and non-technical users to create their own dashboards instead of relying on data and BI teams each time a change is needed. Real-time, interactive dashboards allow you to drill down and answer follow up questions raised when you see a trend in the visualizations. These dynamic insights act as a launchpad, propelling teams toward deeper questions, smarter answers, and closer collaborations.

It’s also important that dashboards are easily shareable — either through embedding or public share links. A good BI solution makes it possible to share insights not only internally across teams, but also externally with partners and customers. With easy access to dashboards, people can keep track of key metrics and performance.

7. Can teams collaborate and build on each other’s work?

Why it matters

Businesses save valuable time and make better decisions when teams share knowledge and build on each other’s work. No one knows everything, and as the old adage goes, two heads are better than one.

Unfortunately, due to technical user experiences that alienate business teams, mainstream BI tools have left business and data users with two equally bad options:

Business teams have to learn specific technical skills to be able to answer a question.
Data and BI teams have to develop a level of understanding about a business area to develop an appropriate query.

Most of the time, neither team has the correct level of technical knowledge and domain expertise to do both jobs — and they shouldn’t have to.

Data silos across business teams are also an issue. Joining multiple data sources and getting a full, up-to-the-minute picture of performance across applications isn’t easy — especially when it requires coding expertise. Many teams turn to BI tool extracts or resort to using pre-built dashboards and reports from their favorite applications, both of which fail to take the larger business context into consideration.

When teams are unable to share, reuse, and build on each other’s analyses, insights are lost, poor decisions are made, work is duplicated, and productivity suffers.

What to look for

Seek a BI tool that strives to bridge the data language gap between data and business experts by empowering both teams to put their expertise to use. The tool should be intuitive enough for business teams to answer data questions for themselves, but powerful enough for BI teams to dig in and do complex analyses. For example, newer BI tools have changed the modeling conversation, making it possible for business users to work alongside data and BI teams to visually add business context to data models without manually writing SQL.

Your BI tool should serve as a single source of truth for your entire organization. This means selecting a solution that makes it easy to join and analyze data across multiple data sources in real time. Workspaces — combined with robust, role-based data access permissions — also promote unity by organizing relevant data sets and analyses in one central location by team.

When teams have access to each other’s analyses, they’re able to build on one another’s work rather than recreating the wheel every time they have a question. Choose a tool that enhances team productivity by enabling teams to repurpose or reuse analyses, and providing templates for the most common BI use cases.

Some BI tools even have the ability to serve as a single source of truth for a company’s entire data ecosystem. These solutions write modeled data sets back to the cloud data warehouse as SQL views. These views can then be queried and reused to power analyses and visualizations across internal and external applications.

BI features and functionality checklist

Cloud functionality

Cloud-native (SaaS) BI solution

Real-time queries live against the cloud data warehouse

Scalable analysis across billions of data rows

Real-time joins across multiple sources in a few clicks

User experience

Visually analyze data with automated SQL generation

SQL authoring capabilities for coders

Zero proprietary coding knowledge required

Familiar, intuitive user interface

Ability to upload and share.CSV files, and join to existing data

Data governance and security

Zero data migration, extracts, copies, storage, or cacheing required

Robust, role-based data access permissions

Single sign-on (SSO)

Object and row level security

Privacy certifications including SOC II, GDPR, CSA, CCPA, HIPAA, and Privacy Shield

Data modeling

Visual, semantic data modeling — no SQL writing required

Ad hoc data exploration without updating central models

Quickly and easily update models over time

Write models back to the cloud data warehouse for reuse across applications

Semi-structured data analysis

Ability to visually identify and parse nested JSON data

Data visualization

Shareable charts and dashboards

Ability to drill down into underlying dashboard data

Dynamic parameters and filters

Private and public dashboard embedding & signed embedding

Collaboration

Shared team workspaces

Single source of truth across teams

Easy sharing and permission settings

Reusable data sets and analyses

Pre-built templates

Next steps

Hopefully this guide has given you a better understanding of the features and functionality to look for as you identify the right BI software solution.

Ready to evaluate a modern A& BI tool for your CDW like Sigma?

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