The Definitive Guide to Analytics and BI for Startups

Startup teams thrive on pioneering. You’re in unmarked territory, bushwhacking through rough terrain. Resources are limited. Competitors lurk, ready to take market share and seize any missteps you make in your mission to achieve product-market fit. The ability to identify opportunities and move quickly is invaluable, but too often, you’re operating blind. Without data, you’re relying on guesswork. Even educated guesses pose an unnecessary risk when you could be basing your strategy on reliable, real-time data instead.

Analytics and business intelligence software (A&BI) offers many benefits to startups. Data can help you understand your market and customers, and identify the functionality and features they value most. It can also inform your go-to-market strategy, help you achieve product quality goals, deliver exceptional customer service, identify the most effective marketing strategies and tactics, boost your conversion rates, increase sales, and more.

Business intelligence becomes accessible

In the past, analytics and BI were outside the reach of most startups and SMBs/SMEs.  Until landing significant funding, smaller companies don’t have the capital available to buy on-prem infrastructure and build large teams of data and BI experts. However, you no longer need these things. 

The modern analytics stack has changed. Now, companies of all sizes can use the same strategies enterprises have been using for years, without committing the level of investment that used to be required.

The opportunity data presents

Your data is a mine rich with precious material that you can use to grow faster and smarter while remaining lean. A&BI allows today’s startups to capitalize on their data resources as a competitive advantage while rival companies aren’t taking a data-driven approach. Even when they take such an approach, having A&BI offers your company insurance against falling behind when competitors are making use of data and reaping the benefits.  

In this guide, we explore why analytics is essential to startups and SMBs/SMEs, and why the time to implement BI is now. We also look at how you can make the most of your analytics stack and how you can move towards a truly data-driven culture using tools that are well within reach of startups.

Why analytics and BI are valuable for startups

Being data-centric is becoming essential to a company’s ability to compete. Let’s dig a little deeper into why analytics is so important to a startup or SMB/SME.

Make better decisions

The most obvious and one of the most significant benefits of analytics is better decision-making. Besides your internal data sources, you have valuable data residing in your software applications, which, combined with internal metrics, creates a comprehensive view ripe for in-depth analysis across the organization.

Bring in-depth analysis company-wide

A modern analytics and BI tool will allow you to dig into the data from various sources to uncover the “why” behind trends and identify connections between events or activities. It will also enable you to make more accurate predictions and predict outcomes in various scenarios. When you need to make a decision quickly, you can consult your analytics tool to find insights. 

Some modern tools offer features like auto-generation of SQL based on user actions in a familiar interface, allowing anyone in the company to participate, regardless of technical skill. These tools empower line-of-business teams to explore data on their own without investing in a group of data analysts.

For example, sales leaders can track changes in prospect priorities, identify sales trends before increased buying in a given region causes inventory shortages, and generate more accurate forecasts. Marketing teams can track channel and content performance, uncover behavioral trends to reduce friction, and more quickly predict churn. Finance teams can more accurately analyze risk, identify waste, and monitor KPIs at a glance. These are only a few of the many ways teams can improve their performance using data.

Additionally, these line-of-business teams can work together using an analytics tool with collaborative functionality. Teams don’t operate in silos. Each department has knowledge and experience that others can learn from within your organization. When your people bring their distinct expertise and perspectives to bear on questions and challenges and build upon one another’s work, they’ll arrive at more accurate insights faster.

Use time more effectively

By nature, startup and SMB/SME roles are broad. Many team members wear multiple hats, and leadership teams are no exception. A good analytics tool can help you access and analyze data quickly, and even alert you when an actionable insight occurs.

Democratizing data access through intuitive tools allows everyone in your organization to participate, regardless of technical skill. No longer dependent on data engineers or SQL analysts for insights, your team can reduce time to insight, decrease resource costs, and eliminate the waiting period for answers. Meanwhile, your data experts can redirect their talents toward sophisticated data modeling and strategic analysis that produces greater business value.

Pass (or keep up with) the competition

The technology powering the modern analytics stack is relatively new, presenting a fresh opportunity for startups and SMBs/SMEs. Many small and mid-size companies haven't discovered that they can access capabilities previously only reachable by the Apples and Amazons of the world, so they're not yet taking advantage of their data. Seagate’s report reveals that 68% of data available to businesses goes unused.

By implementing analytics and BI now, when your competitors are lagging, you have an advantage. You'll be able to start finding insights and taking actions that will put you ahead. If rival companies are already using analytics, you don't have time to waste. You need to put your data to work quickly to move more intelligently, operate more effectively, and grow more efficiently.

Additionally, you can use analytics tools to turn your proprietary data into a customer product or improve an existing product. These data products can help make your company more competitive or allow you to introduce new revenue streams. Payload, a Sigma customer, used Sigma to improve their existing data product. They achieved 7x faster delivery time while saving $8,000+ on every report, opened up a new revenue channel, and increased customer retention and new business opportunities.

How the cloud presents opportunities to become data-driven

Modern cloud data warehouses and cloud-native applications make it quick and simple for startups and SMBs/SMEs to experience the benefits of analytics and BI. Thanks to technological advancements, you can get set up quickly, gain access to a wide variety of data types and formats, and help everyone in your company find answers when they need them. Let’s look at three specific ways the modern data analytics stack is perfectly suited to the needs of startups.

Modern cloud tools wrangle diverse data types in real-time

Startups are typically generating large amounts of data from many different sources. You have your marketing all-in-one software, sales CRM, ERP, and financial software. You may have a product that produces large volumes of data. Storing vast amounts of data with a modern cloud data warehouse is affordable and easily scalable, meaning you can keep as much of your data as you think will be valuable.

The data coming in from these sources is primarily structured and semi-structured, but this poses no problem for the modern cloud data warehouse. The modern CDW functions duly as a warehouse and data lake thanks to easy integration with tools that allow you to quickly make use of data in various formats.

Part of what makes your data so valuable is that it’s being generated in real-time. But to benefit from that up-to-date snapshot, you need to access and analyze it live. To have this capability, simply connect a cloud ETL (Extract, Transform, Load) tool to your cloud data warehouse to move this data into the warehouse for analysis. Cloud ETL tools easily wrangle the constant stream of real-time data coming in from SaaS applications in semi-structured and unstructured formats. Some cloud ETL vendors like Fivetran also support ELT (Extract, Load, Transform), a faster process in which data is converted into analyzable form after it’s already in the data warehouse.

Broad access is affordable and simple

Modern data analytics tools make it possible to open up data access to everyone in your company, translating into more findings that have better accuracy. First, cloud-native analytics tools generally don't charge significantly more to add "seats," so you don't have to be stingy with who gets an account.

Second, the security and governance capabilities of many cloud-native tools are exceptionally comprehensive. Cloud providers prioritize compliance with the latest security standards and regulations, meaning your in-house resources don't have to keep up with it themselves. 

Many proactively search for threats, patch vulnerabilities, and send out updates. Also, using a cloud-native tool will reduce shadow IT scenarios that introduce risk, such as bringing data into an Excel spreadsheet extract.

Users don't need technical skills

Some cloud-native analytics tools, like Sigma, make it possible for users with varying technical skills to participate in the analytics process fully. For example, business users such as marketing, sales, and finance leaders work within a familiar spreadsheet-like interface that automatically generates the necessary SQL as they perform various actions. Data experts and BI analysts can dive into the SQL whenever they like.

This empowers employees to independently explore data and collaborate on cross-departmental solutions and opportunities. Most significantly, it means that you don't need to hire a big team of data experts and BI analysts to become data-informed.

Fostering data curiosity through access

Opening up data access naturally reshapes organizational behavior. When team members discover they can explore data independently, their approach to work changes. They become increasingly curious about how insights might improve their effectiveness and decision-making quality. 

This curiosity leads to organic knowledge-sharing as colleagues exchange discoveries and seek diverse perspectives. As your teams become more comfortable with data exploration, you'll witness a natural shift toward evidence-based discussions across all departments.

The benefits of a modern data analytics stack

Sigma customer, The E.W. Scripps Company, used Snowflake's cloud data warehouse and Sigma's data analytics tool to improve analysis speed dramatically. Before implementing the combined solution, 62% of their workers were unable to access the data they needed in their required timeframe. 

The company's data experts were buried in report requests, and line-of-business teams were forced to make decisions without the information they needed. E.W. Scripps was able to speed up data query times by up to 100x, reduce time to data insight by 90%+, and analyze 2x as much data at no extra cost.

Considerations when evaluating and investing in analytics and BI

As you might imagine, not all tools offer the same capabilities and functionality. Additionally, investing in the tools isn't enough to benefit from your data; your people need to adopt and use the tools effectively. Here's what to keep in mind as you're evaluating tools and building your analytics and BI initiative.

Find easy-to-use, flexible tools that work together

Without the right tools, you'll end up frustrated and fail to achieve the ROI you expect. You need a full-featured cloud data warehouse (like Snowflake or Redshift) that will allow you to connect your ETL/ELT tool and analytics tool easily. Furthermore, each element in your stack should be scalable to grow with you, preventing costly future replacements.

Your tools should quickly integrate, otherwise you may introduce bottlenecks along the way. You can identify which tools work well together by looking at the websites of each vendor since cloud-native solutions often co-market and almost always list integrations. You can also look at product review sites (like G2 and Trustradius) and read case studies to learn more about customer satisfaction.

Consider the needs of your users

Before you can choose the analytics tool that will work best for your data experts and business teams, you must identify the needs of every user. To involve people who don’t have SQL skills, be sure that the tool allows business users to access and analyze data freely, and not be restricted to out-of-date, static dashboards. Regardless of your users’ skills, you’ll want to make sure that the interface is intuitive and easy to use.

Collaboration features are also important since having fragmented teams is inefficient. Team members are often looking for answers to the same questions, so they should be able to share and build upon one another’s analyses. Look for an analytics tool that reduces the need for repetitive work by giving users the ability to create shared workspaces, share analyses across the data ecosystem, and quickly find the most up-to-date datasets available.

Do your research on security

Of course, security should be a primary concern. You want the ability to share data and analyses within your organization, and with your partners and customers, without opening yourself up to a security breach. Look for an analytics tool that doesn’t store or extract data. Your cloud data warehouse should be the only thing storing data, and your analytics platform should connect with your warehouse to work with the data in real time. Be sure your tool has comprehensive security and all relevant security certifications.

Identify the hidden costs

Many tools have hidden costs beyond licensing costs, so you’ll want to watch out for these. Hidden costs can reduce your ROI and introduce surprises down the line that hold you back. Here are the most common hidden costs:

Implementation

Vendor implementation fees should be considered if in-house implementation is not feasible.

Training

The tool's intuitiveness directly impacts the need for extensive training; evaluate whether lengthy training courses are required. Vendor-provided training and associated fees should also be assessed.

Support

Vendor support and product documentation availability are important. Evaluate the quality of basic support and determine if additional support investments are necessary.

Maintenance

With cloud-based tools, much of the maintenance costs get shifted to data storage and BI vendors. Be sure the tool you choose doesn’t put maintenance and upkeep responsibilities on you.

Data connectors

ETL and ELT solutions typically price services based on consumption. This allows you to scale as needed, but you’ll want to factor in this cost and plan ahead as your data needs change.

Compute and storage

Compute and storage costs are incurred at both the data warehouse and BI software levels. While cloud-based tools offer valuable scalability, it's essential to factor these costs into your budget. Plan for potential fluctuations as your data volume and analytical needs grow to ensure cost-effective resource management.

Opportunity costs

Look for a tool that will allow your people to do what they need to do quickly and easily. Otherwise, they’ll be wasting time they could have dedicated to other valuable tasks.  

Switching costs

While you should aim to choose tools that will grow with you, at some point you may want to switch vendors, which can be quite costly. Be sure the tools you choose can scale or allow for easy migration when the time comes.

Building sustainable data literacy

Converting initial data curiosity into lasting organizational capability requires structured support. Develop a formal data literacy program that teaches fundamental skills beyond basic access, including how to evaluate data quality, recognize meaningful patterns, and create effective data visualizations. 

Provide practical resources like report templates for common analyses, ensure vetted datasets are clearly labeled and accessible, and establish systems for teams to build upon each other's work. These intentional structures grow individual interest into collective competency that enhances business outcomes.

Analytics and BI: The startup differentiator

Each of these considerations is important to an analytics and BI strategy that delivers meaningful results for your startup. By selecting tools optimized for your team's needs and establishing strong data foundations, you’ll experience the competitive advantages that will put your startup ahead, and bring your startup towards faster, smarter growth.

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