Venture capital firms thrive on data. From evaluating investment opportunities to tracking portfolio performance and refining investment strategies, having access to accurate and timely data is crucial. However, with information scattered across various systems—CRMs like Affinity, financial datasets from PitchBook, fund accounting systems such as Investran, and portfolio management platforms like Standard Metrics—many firms struggle to effectively harness their data. Leading VC firms are increasingly turning to centralized data warehouses and lakehouses to solve this challenge.

How VC firms are centralizing their data with warehouses

Data warehouses are performant analytical databases that help you take data from multiple sources and structure that data into a consistent schema. By integrating disparate data sources into these singular, unified systems, firms can eliminate inefficiencies, reduce errors, and ensure that all stakeholders are working with the most up-to-date information. Instead of juggling multiple platforms with siloed and incomplete information, firms can rely on a single repository that enables seamless data retrieval and analysis. Some of our VC customers are already leveraging data warehouses like Snowflake and Databricks today.

Having data in one place is only the first step. The real value emerges when firms can analyze this data effortlessly. A centralized data warehouse allows VC analysts to run complex queries across multiple datasets, identify trends and performance metrics with ease, and generate custom reports quickly. By streamlining analysis, firms can uncover patterns about their portfolio performance that might otherwise be buried in spreadsheets and disjointed systems. The ability to cross-reference internal data with external market intelligence can also help firms refine investment theses and make more informed decisions: for example, a VC team could compare internal data on portfolio companies with data brought in from Pitchbook on similar companies in their valuations processes.

The role of AI in data warehouses

Venture firms are also increasingly exploring the intersection of AI and machine learning with their data warehouse to enhance their data strategy. A well-structured data warehouse serves as the foundation for more advanced analytics and natural language querying functionality, allowing firms to apply predictive modeling, automated trend detection, and intelligent deal sourcing. For instance, AI enables users to quickly interact with data warehouses using natural language (e.g., “Find me Series A startups we’ve invested in with 50% year-over-year revenue growth”), eliminating the need for complex and time-consuming SQL queries.

By leveraging AI-powered tools on top of a centralized data system, firms can thus uncover hidden investment opportunities, forecast company performance, and optimize due diligence processes, ensuring they improve their returns in an increasingly technology-driven industry.

How a data warehouse can help

A well-structured data warehouse doesn’t just improve efficiency—it unlocks new capabilities.

• Portfolio monitoring becomes significantly more effective as investors can quickly assess performance, identify early warning signs, and reduce scrambling for data when ad-hoc requests are made.
• Investment research and thesis development benefit from historical and real-time centralized data, leading to a better understanding of market dynamics and emerging opportunities.
• Custom-built internal tools can be developed that live on top of a now comprehensive corpus of portfolio data.
• Automation of data aggregation and centralization across different platforms allows firms to improve operational efficiency and reduce the time spent on manual data entry.
• Data at your fingertips becomes even more important in the fast-paced age of AI and helps investment team members access accurate insights into their portfolio with just one natural language query.

What we are building at Standard Metrics

At Standard Metrics, we recognize the challenges that VCs face in managing their data effectively across multiple sources. We already have a robust API that VC customers are leveraging to pipe data into their own data warehouses today. This is a great option, but requires internal resources to support.

How are we planning to address this need?

1. We are building a new data warehouse product at Standard Metrics. These will live natively on our platform, integrate out of the box with our data, and serve as a central repository for all of a customer’s data, expanding integrations with more sources to provide a comprehensive view of firm operations and investments.
2. We are launching an embedded business intelligence product on top of our Standard Metrics data warehouses so that our customers can fully analyze and visualize their data without leaving our platform. Our advanced analytics and AI-powered reporting tools will enable investors to slice, dice, and visualize their data with ease.

As venture capital evolves, firms that embrace centralized data management will gain a significant competitive edge. Eliminating silos, enhancing analysis capabilities, and streamlining workflows will allow investors to make smarter investment decisions and stay ahead of the competition. At Standard Metrics, we’re excited to be leading this transformation, and we look forward to continuing to build the future of VC portfolio management.

Thanks to Ethan Finkel for contributing to this piece.

A portfolio review is more than just a routine check-up for a venture capital or private equity firm; it’s a core ritual that shapes future investment decisions and influences how a firm supports its portfolio companies.

In a portfolio review, the firm’s partners typically go company by company, assessing financial metrics, comparing actual results against planned projections, and discussing qualitative updates around product development and team-building. However, as this industry adopts tools for centralizing its data, a new generation of technology-forward firms is redefining the way these evaluations are conducted.

The Traditional Approach

The conventional methodology for portfolio reviews has focused on evaluating each company across a variety of factors on a regular basis (often quarterly), including:

1. Financial Performance: Measuring revenue growth, profitability, cash and runway metrics, and other key financial indicators.
2. Performance Against Plan: Evaluating how companies are performing against their operational and financial plans as well as the firm’s internal underwriting.
3. Product Updates: Understanding advancements in product development and market reception.
4. Management Assessment: Reviewing the capabilities and effectiveness of leadership teams.

These factors are critical, but they lack a broader context that could provide deeper insights into a company’s performance.

Why Benchmarks are a Game Changer

What’s missing from the traditional approach is real-time market context, especially as industries shift rapidly amidst economic turbulence. Using tools like Standard Metrics’ Global Benchmarking product, forward-thinking VC and PE firms are now starting to programmatically integrate external private market data into their portfolio reviews. For example, a firm can look at how a company’s growth and profitability compare to privately-held peers based on data from the most recent fiscal quarter instead of evaluating it in a vacuum or based on heuristics.

Here are a few reasons why this methodology is particularly compelling:

1. Enhanced Contextual Understanding: By situating a company’s performance within the market landscape, firms can identify trends, challenges, and opportunities that might otherwise go unnoticed.
2. Informed Management Support: Providing portfolio companies with insights derived from real-time data can empower management teams to pivot strategies more rapidly and effectively.
3. Proactive Decision-Making: With access to real-time data, firms can make more informed, timely decisions regarding their investments—whether it’s deciding to double down on a promising company or re-evaluating a lagging investment.
4. Competitive Advantage: Firms that adopt this data-driven approach will have an edge over those that stick to traditional methodologies. Understanding market dynamics in conjunction with internal performance can lead to better strategic planning and ultimately, higher returns.

A standout example of this innovative approach can be found in the recent work of our customer 8VC, which they described in detail in a blog post titled Introducing Global Benchmarking from Standard Metrics. 8VC evaluated their portfolio companies against real-time market data and benchmarks, transforming the traditional review process into a more dynamic and informed assessment.

8VC was able to draw clear conclusions from their benchmarked data, not just on individual companies, but on entire segments and sub-strategies within their portfolio. For example, they were able to carefully examine the revenue growth performance of companies they had incubated, identifying that early companies were growing slower than peers but later-stage companies began to significantly outperform the field. This is a meaningful conclusion that would have otherwise been unavailable to the firm, and we expect our customers to leverage these types of findings from benchmarks for internal planning, fundraising from LPs, and more.

Conclusion

As the VC and PE landscape continues to evolve, data-driven portfolio reviews will become the industry norm. The innovative practices being pioneered by firms like 8VC signal a shift toward a more comprehensive approach that integrates internal performance metrics with external market realities. Embracing these changes will be essential for any firm looking to maintain its edge in an increasingly competitive environment.

According to a survey last year by Juniper Square, “portfolio monitoring” has become the #1 area for forward-looking technology investment in the venture capital industry. 53% of VC firms are looking to improve their portfolio data management.

Why are VCs rapidly adopting software to digitize their data for the first time? Historically, the venture capital industry has had access to notoriously poor data products and tooling. But over the past five years, as the industry has expanded while becoming more global, distributed, and competitive, there’s been a surge of interest around portfolio data management tools. (We prefer the terms “portfolio collaboration” and “portfolio management” to “portfolio monitoring” and wrote a blog post about why.)

At its core, a central source of truth for portfolio data enables investors to evaluate performance metrics, risk factors, and market trends more effectively. With proprietary data well-organized, firms can build robust benchmarks and help to drive investment discipline, improve asset allocation decisions, and collaborate more effectively internally and externally. Centralized and easily-accessible data is no longer a luxury, but a necessity for effective portfolio management and LP reporting.

Enhancing Decision-Making

For most VC firms, portfolio performance and investment data are still scattered across multiple systems, spreadsheets, and even physical documents. This fragmentation can lead to inefficiencies (weeks spent on analysis, rather than hours), errors (incorrect data shared with limited partners, auditors), and missed opportunities (neglecting underperforming and over-performing companies). Traditional, manual approaches to collecting data create nightmarish reporting experiences for portfolio companies.

In an industry where timing and accuracy are critical, having a centralized data repository can significantly improve the firm’s ability to respond to market changes and investment opportunities. It can mean the difference between doubling down on an outperforming portfolio company and having another fund pre-empt their next round unexpectedly. It can also help firms to step in and help a promising but struggling portfolio company that’s low on runway before it’s too late. The power of centralized data is magnified when firms have access to benchmarking tools that can immediately flag outperformance or areas of concern in their portfolio.

Centralized data also unlocks better collaboration among team members. When everyone has access to the same information with thoughtfully constructed permissions, it becomes easier to align strategies, share insights, and make collective decisions. When an investor leaves a firm, institutional knowledge in the form of structured data and reporting workflows remains behind. This approach can lead to a more cohesive investment strategy and fewer miscommunications. Everyone is on the same page.

Driving Operational Efficiency and Scalability

Managing a portfolio involves a wide variety of different tasks and workflows, from due diligence and compliance, to performance tracking and reporting. Each of these requires accurate and timely data. Centralized data management streamlines these processes by reducing the time spent on data collection and validation. This allows team members to focus on higher-value activities, such as identifying new investment opportunities and working closely with existing portfolio companies.

With the right tools, centralized data collection and analysis can automate a significant amount of routine work (for example, see our case study with January Capital). Automation reduces the risk of human error and ensures that critical tasks are completed consistently and accurately. This can be particularly beneficial for firms managing large and complex portfolios, where manual processes can be both time-consuming and error-prone.

As VC firms grow, invest in additional portfolio companies, and raise new funds, all of these challenges become more complex. Centralized data management provides a scalable solution to support this growth. By having robust data infrastructure in place, firms can easily integrate new companies, strategies, and reporting requirements without disrupting existing operations.

Process Matters

Centralized data is a critical asset for VC firms. It enhances decision-making, drives operational efficiency, and supports growth. Having a centralized data source is an important initial step, but implementing best-in-class processes for portfolio data collection is also critical. We’ll be sharing more learnings about this theme as we continue building for the space and work closely to identify new opportunities with our customers.

At Standard Metrics, we’re committed to providing private investment firms with the tools they need to manage their portfolios effectively. By centralizing data and making it easily accessible, we empower firms to make better decisions, operate more efficiently, and achieve their goals. For more information on how Standard Metrics can help your firm centralize its data and optimize portfolio management, please reach out to our team below.

The practice of investor relations is still fundamentally broken in the private markets. Data isn’t being leveraged to its full potential, important signals and collaboration opportunities are missed, and operational inefficiencies take away precious time from investors and operators.

Venture capital and private equity firms need reliable financial metrics from their portfolio companies to power reporting workflows, make informed decisions, assess risks, and uncover opportunities. But acquiring and leveraging this data in a consistent and efficient manner is challenging without purpose-built software. Making this problem worse, portfolio company CEOs and finance leaders on the other side of this process find themselves overwhelmed by manual and unhelpful reporting workflows with their investors, leading to delays, errors, and gaps in information flow.

The industry status quo is Excel sheets and templates being emailed back and forth between stakeholders. I spent six years as a VC before starting Standard Metrics, and I saw these manual workflows and downstream challenges first-hand.

Our solution to this problem at Standard Metrics centers around the concept of a two-sided network. By building reporting software for both investment firms and their portfolio companies, we aim to create a system that works well for everyone. Ultimately, our goal is to make the reporting process as automated and useful as possible for both sides.

Board meetings and portfolio reviews are two examples of workflows that are dramatically improved by collaborative reporting. Having everyone on the same page with trusted metrics paves the way for more efficient conversations, tailored portfolio company support, and insights into future investment decision making.

Network effects are powerful, and our platform becomes more significantly useful to our users as others join. For investors, it’s magical when they onboard and their portfolio companies are already using Standard Metrics. They can easily connect with each other on our platform, speeding up implementation timelines and improving how quickly investors can begin to collect data.

For companies, when more of their investors use Standard Metrics it streamlines their reporting processes. Their data and documents are already on our platform, and each incremental investor report is typically easier and faster than the last. We’ll also share more in the future about new participatory data products we’re building that provide market insights to our users. This is uniquely enabled by building a direct relationship with both sides of the reporting workflow.

But building software is hard, and building software for multiple stakeholders is even harder, especially as a small company. When we founded Standard Metrics, we knew that building a network would be a long-term investment that would require significant R&D, patience, and grit. Network effects start from zero with a new network. It’s challenging to balance the needs of multiple stakeholders, leading to difficult resource allocation decisions in product, design, and engineering.

Fast-forward a few years though, with a lot of hard work and some luck, you get this:

Now with over 7,000 portfolio companies on Standard Metrics, we feel like we’re just getting started. We’re hard at work launching new products and features that will help both investors and companies to move faster together.

Chris Dixon at A16Z famously coined the term: “Come for the tool, stay for the network.” By building a strong, interconnected network on top of automated workflow tools, we’re working to lay the groundwork for a more collaborative innovation economy.

Almost every software-as-a-service business dreams of killing spreadsheet-based workflows. Name a successful SaaS company, and you can easily imagine a spreadsheet (or several) being retired as a result of buying their product.

In the process of building Standard Metrics, we came to realize that a middle path is the right one for software companies like ours: we automate as much as we can within our vertical application, but we also enable our customers to leverage live platform data in spreadsheets and other horizontal tools like data warehouses to cover edge case workflows.

A lofty goal: moving private markets data and workflows to the cloud

When we launched Standard Metrics in 2020, our goal was to automate and improve financial reporting for venture capital firms and startups. Our initial strategy was to ensure that all portfolio data collection and analysis occurred within our application. The idea was to create a robust environment where every data point was collected, managed, analyzed, and visualized without ever needing to export it to other tools.

We believed that a SaaS application should be a self-contained ecosystem, capable of handling every data need from within its own walls. Spreadsheets, with their limitations and tendency for data silos, seemed like an anachronism in this vision. What need could a user possibly have for using a spreadsheet if we built them the exact tools they needed for their job? By keeping users within our platform, we hoped to eliminate inconsistencies, errors, and inefficiencies associated with moving data back and forth.

Assumptions challenged

As we scaled our business and engaged more deeply with our customers, we began to see both the successes and the limitations of our initial approach. It became increasingly clear that while our platform could handle a significant amount of data processing and analysis, there were compelling reasons why spreadsheets continued to play a crucial role in our customers’ lives after they had collected and digitized their data on our platform.

Spreadsheets are powerful tools for bespoke analysis. They allow users to integrate data from various sources, build complex models, and conduct detailed and customized analyses that go beyond the scope of any single application. For example, many of our customers would build specific models for each portfolio company they invested in, but needed to manually update the model on a monthly or quarterly basis with financial information they collected on Standard Metrics.

We also began to see increasing customer adoption of data warehouses. Data warehouses serve as centralized repositories that aggregate vast amounts of information, enabling sophisticated queries and reporting. Our users often need to combine data from Standard Metrics with other datasets for internal tools or analysis that combines portfolio company data with information from prospective investments.

A shift in our strategy

The problem with our approach was clear. Our product was great for most user workflows, but we could never match the horizontal flexibility of Excel or a data warehouse. Why should we fight these tools when we could work better together? This led us to shift our strategy a couple of years ago, and we began to focus on enhancing the ways in which our platform could work with these tools.

We set out to build solutions that would allow our users to leverage Standard Metrics data wherever they worked best. This led to the development of our Excel plug-in (we also now have a Google Sheets integration live in Beta) and our API. Users can seamlessly integrate Standard Metrics data into their existing workflows, whether they are working in Excel, connecting to a data warehouse, or utilizing other applications (such as internally-developed software). If there’s a use-case we can’t yet support in-app, customers can perform exactly the analysis they need to with fresh data, pulled live from our purpose-built time series database. By offering these integrations, we are not only respecting the existing tools our users rely on but also enhancing their capabilities.

Data accessibility is king

Our customers have taken these horizontal integrations and run with them. Some examples of interesting use cases we’ve seen:

• Building an LLM chatbot on top of Standard Metrics data pulled into a data lakehouse
• Identifying outlier portfolio performance using bespoke internal Excel models
• Producing custom internal quarterly financial reports
• Syncing portfolio data with a home-built crypto/blockchain reporting tool

While the dream of a spreadsheet-free world is still appealing, we’ve learned that the ideal solution lies in creating a flexible, integrated data ecosystem. Ultimately, data needs to be accessible where it will be most impactful for customers. But the ideal isn’t a hard-coded value, and a spreadsheet isn’t a database. Our integrated approach at Standard Metrics ensures that our customers can achieve accurate, timely, and insightful analysis, driving better decision-making and enhancing their investment strategies.

Thanks to Ethan Finkel on our product team for helping to write this piece.