Business intelligence (BI) tools play a pivotal role in enabling organizations to sift through the massive amounts of data they generate and extract meaningful, actionable insights. With an ever-evolving landscape, the introduction of new and innovative BI tools can significantly impact how businesses handle and interpret data. Evidence, an emerging player hailing from Toronto, Canada, has been making waves as part of Y Combinator’s summer 2021 cohort. It sets itself apart by focusing on catering to technical data teams through a code-based, open-source approach.
The Current BI Landscape
Established BI Tools and Their Approaches
The BI market is currently dominated by established tools such as Tableau, Looker, and PowerBI. These industry giants have developed distinct methodologies primarily categorized based on their user interfaces and the user intent they address. Google’s Looker, for example, is designed with a code-based workflow to facilitate data ingestion through complex coding processes. This makes it beneficial for data teams that are comfortable with coding and need advanced features for in-depth data manipulation.
Conversely, tools like Tableau target less technically-inclined users by offering user-friendly, drag-and-drop interfaces. While this simplifies the data manipulation process, providing an intuitive way to create visualizations quickly, it often lacks the advanced functionalities required by more technical users. Some platforms attempt to straddle both ends of the user spectrum by offering a hybrid approach, including both code-based workflows and drag-and-drop interfaces. This ensures they can cater to a wider audience, providing sophisticated tools for data experts while maintaining accessibility for those who lack coding expertise.
The Dichotomy in BI Tools
A notable trend within the BI tools sector is the stark division between sophisticated, manual coding approaches and simpler, user-friendly drag-and-drop interfaces. Evidence clearly positions itself in the former category, perceiving drag-and-drop methodologies as cumbersome for technically proficient data teams. Such teams often seek higher sophistication and granularity in their data products, which drag-and-drop interfaces might not adequately provide. By focusing on a code-centric approach, Evidence enables these teams to maintain intricate control over their data products.
One of the practical benefits of this approach is the ease of maintaining and evolving data products. Modifying and repurposing code can be significantly less time-consuming compared to reworking entire drag-and-drop workflows. The manual coding methods also offer a deeper level of customization, allowing data teams to tailor solutions to their specific requirements. This dichotomy highlights a critical differentiation in the market: while some tools are designed to democratize data analysis for broader, non-technical audiences, others like Evidence focus on providing advanced functionalities that meet the needs of highly technical users.
Evidence’s Unique Positioning
Code-Based, Open-Source Philosophy
Evidence has entered the BI space as a modern contender with a unique open-source, code-centric philosophy. It empowers data teams to create data products using SQL and markdown, emphasizing the flexibility and customization capabilities that a code-based system offers. This approach stands in stark contrast to the prevailing trend of low-code/no-code solutions. Such solutions aim to democratize data analysis, making it accessible to a broader, non-technical audience, but often do so at the expense of the advanced functionalities that technical users need.
Opting for a code-driven methodology allows Evidence to cater specifically to the needs of technically adept data teams. It offers the tools necessary for a highly customized and flexible data analysis process, which is critical for teams that handle complex and specialized data tasks. The code-based approach also integrates well with existing workflows that data teams might use, ensuring a seamless and efficient data product creation process. This strategy not only highlights the unique positioning of Evidence in the BI landscape but also underscores the growing demand for sophisticated tools that align more closely with software engineering practices.
Benefits for Technical Data Teams
Sean Hughes, co-founder and COO of Evidence, articulates the advantages of a code-based approach for modern technical data teams. These teams often align their workflows closely with software engineering practices, thus benefiting from the capabilities Evidence provides, such as version control and governance through tools like Git. This alignment fosters better collaboration within teams, enables comprehensive project histories, and streamlines overall workflow management, making the data analysis process more efficient and cohesive.
Traditional BI tools tend to accumulate outdated or irrelevant data reports due to the extensive efforts required to overhaul or repurpose them. Evidence addresses this issue by ensuring every step of the data analysis and reporting process is code-driven. This code-centric methodology facilitates ease of modification and adaptation over time, ensuring that data reports can evolve without significant overhauls. It also allows for higher levels of customization in data reporting, enabling technical data teams to create precise and tailored solutions, which is essential for nuanced data analysis tasks.
The Rise of Open-Source BI Tools
Popularity and Implications
The growing popularity and implications of open-source software in the BI sector are prominently highlighted by Evidence’s approach. Its open-source nature allows businesses to have substantial control over their data and the flexibility to self-host, distinguishing it from proprietary incumbents like Looker or Tableau. This shift towards open-source solutions is gaining traction among users who prioritize data sovereignty and customization. Open-source BI tools like Lightdash, Metabase, and Apache Superset reinforce this trend, appealing especially to users seeking granular control over their analytics processes.
The open-source model offers several advantages, including transparency in the software’s functionality, the ability to customize tools to meet specific needs, and community-supported enhancements and bug fixes. These features are particularly attractive to technical data teams that require robust, adaptable BI solutions. By embracing an open-source philosophy, Evidence taps into a growing demand for more controlled and customizable data tools, providing an alternative to the often rigid and less transparent proprietary software models.
Evidence’s Commercial Expansion
After a period of limited early access, Evidence announced the opening of its premium cloud product to a larger audience, supported by $2.1 million in seed funding. This significant investment, sourced from A Capital and Y Combinator, among others, has enabled Evidence to extend its commercial footprint. Evidence Cloud is designed for companies that may lack the resources to self-host, thus broadening the tool’s accessibility. The cloud offering echoes the freemium model, with a free starter tier and a scalable team plan, ensuring it can meet diverse enterprise needs.
This commercial expansion allows Evidence to reach a wider user base, making its advanced BI capabilities available to more organizations. The freemium model ensures that startups and smaller companies can access high-quality BI tools without the upfront costs, while larger enterprises can benefit from scalable solutions that grow with their needs. Evidence’s ability to offer both self-hosted and cloud-based options signifies a flexible approach to meet varying business requirements, positioning it as a versatile player in the competitive BI market.
The Broader Tech Ecosystem
Pushback Against Low-Code/No-Code Movements
The article touches upon broader trends within the tech ecosystem, notably the mild pushback against the low-code/no-code movements and the growing embrace of more technical, customizable tools. By positioning itself in alignment with the trend of treating data workflows akin to software development processes, Evidence aims to contribute to a broader movement within the analytics industry. This movement is characterized by the need for advanced functionalities, customization, and flexibility, which no-code/low-code tools often struggle to provide.
Technical data teams increasingly prefer tools that offer the depth and complexity needed for sophisticated data analysis. Evidence’s focus on a code-based, open-source approach resonates with these preferences, positioning the company as a valuable player in this evolving landscape. By aligning itself with software engineering practices, Evidence not only offers advanced analytical capabilities but also supports robust project management through tools like Git, fostering collaboration and detailed project histories.
Advanced Functionalities for Technical Data Teams
Business intelligence (BI) tools play a crucial role in helping organizations process and analyze the vast amounts of data they generate, translating it into valuable, actionable insights. In the continually changing landscape of data management, innovative BI tools are essential for businesses to efficiently handle and interpret their data. One such noteworthy newcomer is Evidence, a company based in Toronto, Canada. Evidence has been making significant contributions as a part of Y Combinator’s summer 2021 cohort. What makes Evidence stand out is its unique approach, which focuses on serving technical data teams with a code-based, open-source solution. This strategy not only differentiates Evidence from other BI tools, but also resonates well with organizations that require flexibility and customization in data analysis. As businesses increasingly rely on data to drive decisions, tools like Evidence are becoming indispensable, highlighting the importance of continuous innovation in the realm of business intelligence.
