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. And 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) offer a plethora of benefits to startups. Data can help you understand your market and customer base, identify the functionality and features that customers value most. It can also inform your go-to-market strategy, achieve your product quality goals, deliver exceptional customer service, identify the most effective marketing strategies and tactics, boost your conversion rates, increase sales, and more.
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. But you no longer need these things. The modern analytics stack has evolved. Now, companies of all sizes can leverage the same strategies enterprises have been using for years — without committing the level of investment that used to be required.
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 a data-driven 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 is valuable for startups
Being data-driven 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. According to SaaS management platform Blissfully, the average SMB uses 20 paid SaaS apps, and each generates data that you can mine for insights.
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
Even startup teams who understand that they have valuable data and know where it resides often fail to use it. One report shows that 56% of SMBs/SMEs say they “rarely or infrequently” check their data, and 33% of those say the reason is that they have too many other responsibilities.
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.
Using a tool that allows anyone in the organization to participate regardless of technical skill means that you don’t have to rely on a data engineer or SQL analysts to generate insights. This reduces the time to insight, saves human resource costs, and cuts out days of waiting for answers. And if you have data experts on your team, they can free up time to focus on data modeling or more complex, strategic analysis.
Pass (or keep up with) the competition
Because the technology powering the modern analytics stack is relatively new, it's still 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. Forrester reports that between 60% and 73% of all company data 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. And if rival companies are already using analytics, you don't have time to waste. You need to put your data to work quickly, so you can 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. It achieved 7x faster delivery time while saving $8,000+ on every report, open up a new revenue channel, and increase customer retention and new business opportunities. See how they did it by checking out the case study.
How the cloud has unlocked 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 today’s technology, you can get set up quickly, gain access to a wide variety of data types and formats, and help everyone in your company uncover insights when they need answers. Let’s look at three specific ways the modern data analytics stack is perfectly-suited to the needs of startups.
Modern cloud tools wrangle any data type in any amount, at startup speed
Startups are typically generating large amounts of data from many different sources. You have your marketing all-in-one software, sales CRM, and your financial software. You may have a product that’s cranking out data. Maybe you have an ERP. Storing large amounts of data is affordable with the modern cloud data warehouse, and it’s easily scalable. So 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 real-time snapshot, you need to access and analyze it in real time. 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 insights 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 robust. 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. And using a cloud-native tool will also reduce shadow IT scenarios that introduce risk (bringing data into an Excel spreadsheet extract, for example).
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 while auto-generating the necessary SQL as they perform various actions. Data experts and BI analysts can dive into the SQL whenever they like.
This capability makes it possible for your people to get insights when they need them and collaborate to solve more complex issues or explore opportunities that span departments. Most significantly, it means that you don't need to hire a big team of data experts and BI analysts to become data-driven.
Another benefit comes as a result of opening up data access: you encourage a data-driven culture. People become curious about how data could help them work more effectively and make better decisions. They start diving into the data, sharing insights with team members, and solicit other perspectives that would help them more fully understand the complexities of the issues they face. As your culture becomes more comfortable working with data, your company will become more data-driven.
Sigma customer, The E.W. Scripps Company, used Snowflake's cloud data warehouse and Sigma's data analytics tool to improve speed to insights 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. Check out the on-demand webinar to see how they did it.
What to consider when evaluating and investing in analytics and BI for startups
As you might imagine, not all tools offer the same capabilities and functionality. And 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. Each element in your stack should be scalable to grow with you, so you don't have to switch later on.
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 — cloud-native solutions often co-market and almost always list integrations. You can also look at product reviews 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 capabilities are also important since having teams operate in silos 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 robust security and all relevant security certifications.
Look for 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 — If you can’t implement the tool in-house, does the vendor charge a fee for implementation?
- Training — How intuitive is the tool to use? Is a lengthy training course required before people can pick it up and generate insights? Does the vendor offer training on their tool, and if so, what fees do they charge?
- Support — Does the vendor offer support and product documentation? How useful is the vendor’s basic support? Will you need to invest in in-house support or an additional support package from the vendor?
- 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 up and down as needed, but you’ll want to be sure to factor in this cost and plan ahead as your data needs change.
- Compute and storage — Compute and storage costs are incurred at the data warehouse and/or the BI software levels. Again, cloud-based tools allow you to scale, but you’ll need to factor costs.
- 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.
For more on hidden costs, see Uncover the Hidden Costs of Business Intelligence Tools.
Build a data culture
It’s impossible to overstate the importance of a data culture. If your people aren’t motivated to get curious, ask questions, and dive into the data in search of answers, you won’t be generating or using the insights that would drive growth and improvement. You’ll want to create a data literacy program that teaches basic data skills — how to understand data, find it, interpret and evaluate it, manage it, and create interactive dashboards and visualizations. Give your people report templates for popular analyses (your analytics tool should make these available), make sure vetted datasets are easy to find and accessible, and empower teams to share and build upon one another’s data insights.
Learn more about building a collaborative, data-driven business culture in this recent blog post.
Analytics and BI = Getting Ahead
Each of these considerations is vital to an analytics and BI intuitive that delivers the results you’re looking for as a startup company. Choosing the best tools for your team and setting your company up for success will allow you to experience the competitive advantages that will put your startup ahead, and ultimately help you grow faster and smarter.