How to Set Up Your Revenue Data Stack

In today’s competitive business environment, it’s more important than ever to have a well-structured and efficient foundation on which you can perform analytics and reporting on your company’s revenue data. While data is seemingly at our fingertips, and it’s a no brainer to be tracking our data, it’s easier said than done to set up a data stack for revenue intelligence. This article explores why a revenue data stack is important, its essential components, steps to set it up, and best practices for managing it.

Understanding the Importance of a Revenue Data Stack

What is a Revenue Data Stack?

Simply put, a revenue data stack refers to a strategic combination of tools and technologies designed to collect, process, store, analyze, and visualize company revenue data. It acts as a robust infrastructure that enables businesses to efficiently manage and make sense of vast quantities of data, ultimately leading to increased revenue.

Imagine your revenue data stack as the backbone of your business operations. It provides a solid foundation for your data-driven decision-making processes, ensuring that you have access to accurate and reliable information.

Every organization's revenue data stack can differ based on its specific needs and industry requirements. However, regardless of the tools and technologies used, the primary goals remain the same - to effectively learn from accurate, real-time data, deliver insightful analytics, and to take action and decisions based on data.

Why Your Business Needs a Revenue Data Stack

Managing and analyzing data is key to making informed business decisions. In today's fast-paced and ever-changing business landscape, relying on guesswork and hasty decisions can be detrimental to your success. This is where a well-structured revenue data stack comes into play.

By implementing a revenue data stack, you provide your business with a structured framework for processing and analyzing data. It eliminates the guesswork and provides you with accurate insights that can drive better decision-making.

Furthermore, a revenue data stack offers an organized structure to work with varied data from diverse sources. It allows you to seamlessly integrate data from your sales, marketing, and finance departments, providing a holistic view of your company's revenue stream.

With a revenue data stack in place, you gain the ability to precisely track your company's revenue stream. This enables you to identify patterns and trends, isolate problem areas, and find room for improvement. Whether it's uncovering untapped market segments or optimizing your pricing strategy, a revenue data stack empowers you to make data-driven decisions that can positively impact your bottom line.

In conclusion, a revenue data stack is not just a collection of tools and technologies. It is a strategic investment that enables businesses to harness the power of data and transform it into actionable insights. By implementing a revenue data stack, businesses can unlock the true potential of their revenue data and drive growth in today's data-rich environment.

Below are a few key dashboards that are critical in monitoring revenue, particularly for SaaS companies:

Marketing Dashboards

These track campaigns across channels tied to spend and revenue impact. This is important because it helps to plan marketing budgets for next year based on previous activities and results, essentially informing what to do next year.

Sales Dashboards

Sales dashboards reveal outbound prospecting efforts, what’s in the pipeline and conversion rates across stages. This can help answer questions such as “Do I need to hire more sales reps to achieve my sales objectives?” or “How much should be forecasted based on performance from previous quarters?”

Product Usage, User Growth And Customer Satisfaction

This is particularly important for product-led growth SaaS companies. Tying prospection to product usage, when are these users “hot” enough for sales to reach out? Customer success should also autonomously have usage information to identify the best timing for expansion, upsells and renewals and to prevent churn.

Overall Revenue

Overall revenue is especially notable for investors and overall company finance. Customer success teams also need to tie revenue/contract size to usage and satisfaction. If the bill goes up, but usage and satisfaction go down, these teams must be prepared for a tough negotiation.

Essential Components of a Revenue Data Stack

A revenue data stack is a crucial component for businesses to gain insights into their economic performance. It consists of various elements that work together to collect, process, store, analyze, and visualize data. Let's explore each of these components in more detail:

Data Sources

Data sources refer to the systems through which businesses collect data. These sources can include customer transactions, CRM information, website analytics, social media interactions, and more. Each data source provides a unique piece of the revenue puzzle, offering a comprehensive view of the business's economic performance.

Identifying and integrating these data sources within the revenue data stack is crucial for a holistic and accurate analysis. By leveraging multiple data sources, and being inclusive of all of them, businesses can gain deeper insights into their revenue streams, customer behavior, and market trends.

Source system examples: Pipedrive, HubSpot, Salesforce, Stripe, Google Analytics, Facebook Ads, and more.

Data Ingestion and Transformation Tools

Once data is collected from various sources, it needs to be processed and transformed into a format that’s suitable for analysis. ETL (Extract, Transform, Load) or ELT solutions, are commonly used for this purpose. These tools play a vital role in transforming raw data into a structured format that can be easily analyzed.

When selecting a data processing tool, compatibility with different data formats and types of sources within the revenue data stack is essential. These tools are particularly crucial for handling big data, making it manageable and usable for further steps in the data stack. These tools also make it possible for data to move from the source systems to another location — the data warehouse, which we’ll cover just below.

Data Ingestion & Transformation Tool examples: Hevo Data, Stitch, Airbyte, dbt

Data Storage Solutions

Choosing suitable storage options is a critical step in setting up a revenue data stack. Businesses may opt for databases, data warehouses, or data lakes, depending on their data volume and need for agility. The selected storage solution should be reliable, secure, and capable of handling the volume and velocity of data.

Storing different types of data in a structured manner is essential for easy retrieval and analysis. The storage solution should also support the scalability requirements of the business, allowing for the seamless addition of new data sources and accommodating future growth.

Data Storage Solutions: Snowflake, BigQuery, RedShift

Data Analysis and Visualization Tools (BI Platforms)

After data is processed and stored, it is ready for analysis and visualization. Data analysis and visualization tools are essential for uncovering actionable insights from datasets, aiding in decision-making processes.

This is where business intelligence (BI) platforms comes in, which includes data visualization capabilities. Tools like Whaly offer an intuitive user interface and have the capability to present complex data in an easily comprehensible format. By visualizing data through charts, graphs, and dashboards, businesses can have their metrics on display in real-time, empowering everyone with a deeper understanding of their revenue patterns, along with the ability to identify trends and make data-driven decisions.

BI Platforms: Whaly, Sigma, Looker, Power BI, Metabase

In summary, a revenue data stack consists of various essential components, including data sources, data processing tools, data storage solutions, and data analysis and visualization tools. By leveraging these components effectively, businesses can gain valuable insights into their economic performance, enabling them to make informed decisions and drive growth.

Steps to Set Up Your Revenue Data Stack

Setting up a revenue data stack is thought of as a complex process, since it involves stitching together all the different tools mentioned above. These days, there are many modern solutions on the market that tackle multiple of these steps in one platform, and are easier to set up with no-code or low-code options. We’re here to say that putting together your revenue data stack doesn’t have to be a painful process. Here are the steps and considerations:

Identifying Your Data Needs and Goals

Before diving into the setup process, it is crucial to identify the nature and volume of data your business deals with. This step will help you understand the specific requirements and challenges you may face when setting up your data stack.

Furthermore, it is essential to consider your team's competencies and the scale of your operation. This knowledge will aid in selecting the most appropriate tools for processing, storage, and analysis.

Choosing the Right Tools

The choice of tools can make or break your data stack. It is important to carefully evaluate and select tools that are compatible with your data types and volume, as well as within your budget. Here are some considerations:

• Scalability
• Learning curve (is there a proprietary language that needs to be learned?)
• Are data engineering resources required for set-up?
• Implementation time
• Time to value - time it takes to set up the first dashboards and draw first insights
• Pricing
• Performance / latency
• Customer support
• Does the platform include any ETL connectors already? Do you need tools for each separate step listed above?

Conduct a careful evaluation:

• Talk to peers at other companies to see which platforms they find useful and easy to use
• Go to G2.com to check customer reviews
• Check out various websites and see if they have a “comparison” page against other solutions, so you can make a fair evaluation

Integrating Your Data Sources

Once you have identified your data sources, the next step is to ensure that they are integrated within your data stack. This process may involve developing custom APIs or connectors, or utilizing third-party services, depending on your stack's setup.

It is important to ensure that all your important data sources feed into your data stack efficiently. This integration process can sometimes be complex and may require technical expertise. While you can consider involving your IT team or hiring a data engineer, you can also look out for low-code or no-code data platforms like Whaly, that also include implementation and onboarding services. Some vendors offer great service and support, and can be much cheaper than hiring a data or IT consultant to help.

To look up which vendors offer connectors to which sources, check out Whaly’s connector catalog, a comprehensive ETL catalog.

Setting Up Data Processing and Storage

With your data sources integrated, the next step is to configure your data processing tools to work with the incoming data. Careful planning is required to ensure that your tools can handle the commonalities, such as data formats and volumes, as well as any variances, such as peak loads or unusually large data sets.

Simultaneously, setting up your data storage also requires careful thought. It is crucial to choose solutions that cater to your business needs while ensuring data security and ease of access. Consider factors such as scalability, reliability, and data redundancy when selecting your storage options.

If you’re setting up your data stack for the first time, we recommend going with a cloud-based data warehouse to start, like Snowflake, BigQuery, or RedShift. These are managed warehouses, which are simple and fast to set up and start importing your data right away.

There are other in-house warehouse options available and offered as open source for free (like Postgres or Presto), but it can be a big headache to set up on your own and get started.

We recommend that you start with a cloud-based managed data warehouse, and then, if it becomes too expensive (since they charge on usage), you can either figure out how to decrease usage, or switch to the in-house solution, once you’re a little more mature in your data journey and have a data team that can handle this.

Implementing Analysis and Visualization Tools

The last step in the setup process is implementing your chosen analysis and visualization tools. These tools should be able to work seamlessly with your processed and stored data and deliver the insightful analytics that you desire.

It is important to run multiple tests to ensure that the in-tool calculations and visualizations meet your expectations. Any modifications required should be addressed promptly to ensure optimal performance of your analysis and visualization tools.

By following these steps and giving careful consideration to each stage of the setup process, you can create a robust revenue data stack that empowers your business with valuable insights and analytics.

Best Practices for Managing Your Revenue Data Stack

Setting up a revenue data stack is an essential step for businesses in the digital age. By understanding its importance, the necessary components, and best practices for managing it, you can set up a revenue data stack that is tailored to your business needs and aids in driving revenue growth.

Ensuring Data Accuracy & Governance

The effectiveness of your revenue data stack significantly depends on the quality of your data. Adopt strict data governance and management practices to ensure the data you are working with is accurate, complete, and reliable.

One way to ensure data quality is by conducting regular data audits. These audits involve thoroughly examining your data to identify any inconsistencies, errors, or missing information. By doing so, you can address these issues promptly and maintain the integrity of your data.

In addition to audits, implementing stringent data entry standards can also contribute to data quality. By establishing clear guidelines and rules for data entry, you can minimize the chances of human error and ensure consistency across your data stack.

Furthermore, developing automated systems for data validation can help streamline the process of identifying and rectifying data quality issues. These systems can automatically flag any anomalies or discrepancies, allowing you to take corrective measures swiftly.

Lastly, ensure that you pick a BI platform that has high governance features and standards in place. Whaly’s BI platform, for example, has 2 separate tools for data analysts and business teams. Only data-savvy “builders” can perform complex modeling in the Workbench, while business teams with less technical expertise can explore and query data all they want in the Workspace. This clear boundary of separation makes it so that business teams cannot ‘break’ the data that has been prepared by data teams.

Regularly Updating and Maintaining Your Stack

Stay up-to-date with the latest developments in analytics, tools, and technology to keep your stack current and competitive. Regular maintenance and updates are pivotal to preventing tool obsolescence and keeping your data operations streamlined.

As technology evolves rapidly, it is crucial to stay informed about new advancements that can enhance your revenue data stack. This includes being aware of emerging analytics techniques, innovative tools, and cutting-edge technologies that can improve data processing and analysis.

Consider incorporating regular checks and maintenance procedures in your business processes to ensure the ongoing effectiveness of your stack. This can involve conducting periodic reviews of your data stack's performance, identifying areas for improvement, and implementing necessary updates or upgrades.

Additionally, it is beneficial to establish a feedback loop with your team and stakeholders. Encourage them to provide input on the usability and functionality of your revenue data stack. By gathering feedback, you can identify pain points, address any concerns, and make informed decisions regarding the maintenance and enhancement of your stack.

Enabling Self-Service & Training All Teams

Lastly, it’s time to evangelize the usage of the BI platform (and the tracking and monitoring of revenue data) across all relevant teams. Getting people acquainted with the stack and how to use it is absolutely vital to getting your company to become “data-driven” — ensuring that all teams leverage data in their daily decision-making. In our view, data should be not limited to technical teams at any company. Data is only useful in the hands of those who make the business decisions, so you need to get the business teams on board!

Conduct regular training sessions to educate them about new tools and best practices. This not only increases your team's productivity but also ensures the correct usage of your data resources.

During training sessions, emphasize the importance of data security and privacy. Make sure your team understands the protocols and measures in place to safeguard sensitive information. This will help instill a culture of data protection within your organization.

Furthermore, use these training sessions as an opportunity to gather feedback from the users. Encourage them to share their experiences, challenges, and suggestions for improvement. By actively involving your team in the management of your revenue data stack, you can foster a sense of ownership and promote continuous learning and growth.

To summarize, managing your revenue data stack requires a proactive approach. By ensuring data quality, regularly updating and maintaining your stack, and training your team on its usage, you can maximize the value of your data resources and drive revenue growth for your business.

Not sure how to get started? Get in touch to speak to a data expert, or try Whaly out for free today!