Skip to main content

Date Published

June 30, 2025

Share this

Why The Best VC Fund Management Software Matters More Than Ever

The venture capital landscape has changed dramatically since 2021’s highs. Fewer deals and funds are capturing the lion’s share of dollars, liquidity remains scarce, and dicey macroeconomic conditions loom large. Despite these headwinds, AI has been a strong tailwind, catapulting Cursor to $100M of revenue in its first year and Intercom’s “decelerating” 8-figure ARR business to a 393% annualized growth rate. These factors—combined with the explosion of private market data and the new leverage that modern tooling gives investment firms—are forcing VCs to rethink their edge, adopt new investment strategies, and implement innovative technologies to succeed. Even established firms, often insulated from fundraising headwinds, are adjusting to the reality that venture is no longer a cottage industry: it’s become a full-fledged, competitive asset class.

As VCs scramble to find alpha and capture beta in this hyper-competitive, ever-challenging environment, a shift to automated, data-driven, portfolio and fund management has emerged. Legacy software has become a barrier to insight and speed. Manual fund management across spreadsheets, email, and board decks is now a liability, not a cost saver. Data is suddenly fueling investment decisions, not just back-office compliance. Firms are modernizing their tech stacks and operational processes across all categories, from sourcing to supporting, and finding the right software to help manage it all has become paramount.

We’ve witnessed venture’s dramatic evolution since launching Standard Metrics in 2020 by working hand-in-hand with over one-hundred leading investment firms (Accel, Bessemer, General Catalyst, etc). These conversations, paired with the challenges we’ve solved for our VC customers, have given us a deep appreciation for the operational complexity behind fund management. We wrote this guide to democratize these insights and help all VCs navigate the rising unknowns and urgency around getting fund management right.

 

How to Choose The Best VC Fund Management Software

Venture’s maturity as an asset class has spurred the development of industry-specific tools across every fund management niche. Unfortunately for GPs, CFOs, and COOs at VC firms, this plethora of software has inadvertently made it harder on firms to advance into the digital era with confidence. Resources like VC Stack help map the market but with 100+ different solutions listed, we wanted to provide a resource that helps VCs know what to look for and where to start.

 

Considerations Ahead of Evaluating VC Fund Management Software

 

1. Clarify End Users and Stakeholders

Before talking to vendors, productive VC tech stack buildouts begin with an honest discussion about who will manage, use and benefit from new software and services. Consider the daily workflows and challenges faced by different personas, such as your finance teams who are responsible for timely quarter and year-end reporting, or your deal teams who need sophisticated pipeline tracking and due diligence capabilities. Additionally, reflect on benefits to secondary stakeholders, like LPs who increasingly expect transparent, real-time performance insights, founders who would appreciate intuitive interfaces for hassle-free reporting, or auditors who will love being able to see the full picture of a datum’s source, history, and changes. Understanding each stakeholder’s distinct needs and pain points will not only make it easier to sell internally but ensure the chosen solution will genuinely enhance operational efficiency, investment decision-making, investor relations, and founder/portfolio company support.

2. Align Software Requirements with Your Firm’s Stage

The stage and maturity of your fund directly influence the features and capabilities you should prioritize when building out a VC stack. While first-time managers can manage with basic tools like spreadsheets and Notion, manual systems sacrifice speed, accuracy, and insights. That’s why emerging funds deploying capital should implement tools that scale with portfolio growth, whether that’s through automating data collection, call recording and summarization, or fund admin tasks that rapidly become time-consuming. Mature, later-stage funds should implement best-in-class tools across each vertical, layering a data warehouse on top to run complex queries across multiple datasets, identify trends and performance metrics with ease, and generate custom reports quickly. Carefully matching a software’s capabilities to your fund’s current position and anticipated evolution prevents costly misalignment, future-proofs your investment, and enables time to stay focused on what matters: investing in companies to generate returns for LPs.

3. Strategically Evaluate the Build-vs-Buy Decision

Deciding whether to build a homegrown fund operations platform or adopt off-the-shelf software is a pivotal decision for any venture firm. The right path depends on a fund’s strategic vision/brand, internal engineering/operational capabilities, and budget. It’s true that building a custom VC stack can unlock tailored workflows across deal sourcing, due diligence, portfolio company tracking, LP reporting, etc; however, this route demands substantial upfront investment, ongoing engineering resources, and the ability to navigate R&D complexity without disrupting core investment activities. On the other hand, leveraging a commercial all-in-one solution or a modular suite of best-in-class tools can help firms stay focused on investing, reduce operational overhead, and benefit from evolving industry-wide standards. Thoughtfully evaluating these trade-offs in light of your fund’s size, in-house expertise, LPA, and long-term growth plans ensures you don’t overextend resources on software development, while still building an operational foundation that scales with portfolio growth.

4. Avoid These Pitfalls When Evaluating Fund Management Software

Firms should avoid the following mistakes when evaluating fund management software. The first is vetting tools alone versus looping in other, likely to benefit stakeholders. Often, any decision regarding the broader VC tech stack will have a wider impact than it at first seems. Moving to a new KPI reporting platform, for instance, may not only impact finance workflows, but those of investors, IR, Platform and more. Second, firms should demand transparency around how pricing will scale as their portfolio and/or team grows, clarifying upfront how their contract will change at renewal and scale over the course of its lifetime. Third, while initial costs might appear high, proactively investing in software robust enough to handle future growth often proves more economical than selecting cheaper alternatives that soon require disruptive and costly replacements. Fourth, and related to the first, another pitfall is purchasing solutions without the resourcing or stakeholder buy-in to fully implement and derive value. This is when purchasing CRM, fund accounting, portfolio management and other technology with dedicated support and implementation managers pays dividends. Lastly, firms should insure their evaluations by not overlooking a vendor’s product roadmap, shipping velocity (often a function of team size), capitalization, and long-term support capabilities. This oversight can lead to dissatisfaction down the line if the chosen platform fails to evolve alongside the firm’s growing operational and strategic needs. Stay mindful of these pitfalls to make more informed, strategic decisions about fund management buildouts.

 

What to Look For When Evaluating VC Fund Management Software

 

1. Founder-friendliness

Firms must not overlook the portfolio company experience when implementing new fund management software. From platform initiatives to investment team workflows, even well-intentioned decisions can inadvertently create friction for founders. Whether it’s requesting repetitive, custom data to satisfy internal reporting, exposing founders to misleading benchmarks for guidance, or clarifying something that a founder shared on a call, the cumulative burden adds up. As such, software should not only be intuitive, it should be streamlined, insightful and valuable. That is possible by implementing solutions designed for founders, not just branded as founder-friendly. Stay cognizant of features like direct accounting integrations, global benchmarking insights, action-item summaries, and flexible document ingestion to ensure fund ops decisions stay as frictionless as possible for founders. Software should enrich, not burden founders’ lives. Don’t lose sight of the fact too that providing a best-in-class experience to portfolio companies confers benefits to VCs as well, primarily greater trust and a greater likelihood that more data is shared, on-time and accurately, for portfolio reporting and analysis.

2. Implementation Process

Implementation is not often discussed until deep into the buying process, but it can be a hidden, hefty headwind for investment firms building out their tech stacks for the first time, especially those without dedicated IT departments. That’s why it’s important early in the vendor evaluation process to diligence numerous providers and speak to references (ideally firms similar in size/structure) about their onboarding processes. How long did it take to reap benefits versus what was promised? What was the lift required from each party? Who were the stakeholders that needed to be involved? Self-serve solutions generally provide less onboarding guidance and handholding, while premium, enterprise-grade solutions have dedicated implementation teams handling a lot of the fund ops work, without costly delays and accuracy issues. It’s important to ask these hard questions upfront to prevent fund ops fires down the line: prolonged rollouts erode internal momentum, jeopardize executive buy-in, and can reflect poorly on internal advocates, especially if roles or priorities shift mid-project.

3. Product Robustness, Scalability, and Shipping Velocity

Buying venture capital fund management software can seem like a binary, obvious decision: does this product meet, or not meet, our needs? Unfortunately for fund managers, that snapshot view misses three deeper dimensions that are critical for long-term success. The first and most obvious is product robustness: how easy is the product to use; how many core problems does implementing it fix; is it offering institutional-grade, or marginally better features, and more. Remember, even a single bad experience, lack of customization, data inaccuracy, latency or security issue can erode trust and create blockers. Second, scalability defines how well the platform will handle investment and firm-level growth: whether you’re aggressively deploying, starting to deploy across several vehicles, adding more users with varying permissions-levels, etc., a truly scalable system spares you from costly re-platforming, ensuring consistent performance under rising load and expectations. Finally, shipping velocity, which signals a vendor’s ability to stay ahead of evolving market table stakes, integrate with other systems, and continually automate manual workflows for VC teams. Get a sense for that by talking to customers, looking at how many engineers work at the company, and understanding if there has been an acquisition recently which may temper support and product velocity down the line. When robustness, scalability, and shipping velocity become separate checkboxes, you secure a reliable foundation for today’s workflows while preserving optionality for future growth.

4. Interoperability

Interoperability, the ability of fund management software to seamlessly exchange data and interact with other systems, is non-negotiable for modern VC firms because it underpins operational efficiency, portfolio insights, and strategic flexibility. By insisting on well-documented APIs, plug-ins to tools like Excel, and providers across different verticals (e.g. Derivatas), firms can eliminate manual, duplicative fund ops work that introduces errors, delays, and obfuscates decision-making. Interoperability also guards against vendor lock-in: if your needs evolve or a better, more specialized tool emerges, you can swap components in and out of your tech stack without costly migrations and bespoke engineering work. Furthermore, integrated systems empower teams to build end-to-end automated workflows—KPIs collected via Standard Metrics can be ported to Derivatas for a valuation, then sync back automatically and transparently to Standard Metrics, for example. In an environment where speed, accuracy, and transparency can make or break fundraising and effective portfolio management, demanding interoperability from vendors ensures firms can adapt quickly to new data sources, regulatory requirements, and market opportunities without being hamstrung by siloed software and systems.

5. Platform Support

Support—spanning implementation, ad hoc troubleshooting, and ongoing account management—can make or break an experience with fund management software. Having a trusted, expert resource from contract signature and beyond to navigate project planning, data migration, user training and instance configuration will ensure firms go-live on time and on budget. Once live, responsive ad-hoc support for unexpected technical issues (reporting glitches, API hiccups, permissions snafus, etc) is crucial to minimize downtime and prevent small problems from snowballing into major workflow disruptions. Lastly, a dedicated account manager can serve as a strategic partner. Ideally on-shore domain experts, they understand each firm’s unique fund structures and processes, advocate for needs in a vendor’s hotly-contested product roadmap, proactively surface new features and best practices, and coordinate cross-functionally to keep systems optimized. By insisting on full-cycle support, VC firms can safeguard, improve and streamline daily operations to maximize a software’s value over the long term.

 

Evaluating ROI With New VC Fund Management Tools

 

1. Cost of Status Quo

Many VCs demand quantifiable ROI for new fund-management tools but rarely account for the opportunity cost of keeping their legacy systems and manual processes. The truth is that each hour spent across spreadsheets and email—whether that’s chasing founders down to share data, tracking carry, managing fund waterfalls, or finding pathways for an introduction—is a lost hour of return-generating sourcing, diligence and checkwriting activities. This can prove costly due to venture’s power-law: missing one good deal due to low-value portfolio management work can make or break fund returns. Additionally, manual fund management can obfuscate portfolio risks and opportunities, which can materially impact a fund’s returns if spotted and acted on early. Lastly, manual fund management also has the aforementioned, adverse impact on founders. It not only distracts firms from supporting portfolio companies, it distracts portfolio companies from productive operating activities. Recognizing these truths before and during an evaluation can make an ultimate software purchase easier to justify.

2. Benefits With New VC Fund Management Software

Now that the opportunity cost of manually managing a fund is understood, the benefits on the flip side of the coin are more obvious. And while quantifiable ROI will ultimately vary firm to firm, each should recognize the likely gains across the following areas and work with vendors to construct a more exact, bespoke picture of ROI. It’s helpful to remember that clean portfolio data and a robust, tightly integrated VC stack powers all activities at a firm: sourcing, diligence, LP reporting, audit, valuation, portfolio reviews, board meetings, fundraising, and forecasting so these decisions should not be taken lightly.

I. Operational Efficiency

Automating manual VC workflows gives firms time back to focus on the high-value tasks conducive to generating returns (e.g. meeting with founders and investing), while gaining a foundation that scales with portfolio growth.

II. Cost Savings

Reducing the time that VCs spend on low-value fund ops work is cheaper than adding additional headcount to manually manage growing portfolio management workloads.

III. Central Source of Portfolio Truth

Minimizing internal (GP/Finance/Ops/Partner) misalignment regarding the state— and future state—of the portfolio ensures everyone is on the same page, without frivolous back-and-forth liaison.

IV. Spot Investment Opportunities and Risks

Having a clear, dynamic view into the portfolio helps investment firms proactively address at-risk companies and double-down on outperformers early.

V. Reduce the Portfolio Company Reporting Burden

Intuitive, founder-friendly reporting interfaces help investment firms collect more data on company performance and also makes reporting a valuable, streamlined exercise for founders through direct accounting integrations, flexible document parsing, and robust KPI benchmarking.

VI. Enhance LP and Auditor Relationships

Robust, accurate, and traceable portfolio data fosters trust with LPs and auditors who are increasingly expecting timely, detailed reporting and compliance.

Thoughtfully taking each of these benefits into account and constructing a narrative as to which matter the most can help investment firms zone in on the most important issues they’re looking to solve and make the case for why certain tech should be implemented now versus later.

 

Where VC Fund Management Is Getting Disrupted

The surge of specialized private-market tooling and data was already rendering legacy private-equity software obsolete, but now that AI has entered the picture, the bar for speed, accuracy, and insights is even higher, leaving firms that cling to outdated systems at a severe competitive disadvantage. Every prudent VC fund manager is implementing AI across their tech stacks, and early adopters are compounding operational knowhow, streamlining fund ops, and improving decision-making by modernizing workflows. Understanding the different areas that are being impacted and the right tech to use in each vertical is crucial.

Portfolio Management

Portfolio management in VC involves overseeing and supporting the startups a firm has invested in. This includes managing and monitoring key performance metrics, key investment-level information, portfolio company milestones, and providing guidance or resources to help startups grow. Effective portfolio management not only is streamlined but aims to maximize fund returns by nurturing winners, helping underperformers early, and planning timely follow-on investments. Due to the sheer volume of portfolio and investment level data that VCs are tasked with organizing, VC portfolio management is ripe for automation and consolidation. Modern providers like Standard Metrics are now implementing AI features—like financial document parsing—to inject even greater efficiency and prescience into portfolio management.

CRM / Deal Flow

Venture CRM and deal flow management refer to how venture capital firms handle their pipeline of potential investments and relationships. This function involves tracking startup prospects, managing interactions with founders and co-investors, and moving deals through stages from sourcing to due diligence to closing. A well-structured deal flow process helps VCs efficiently filter a large funnel of opportunities into a few high-potential investments. Since modern VCs are confronted with an overwhelming amount of information across calls and meetings, manual tracking is downright inefficient. AI-first venture capital tooling is upleveling this function by intelligently screening opportunities (see Affinity’s new sourcing product), preparing for and summarizing meetings, uncovering high-potential startups with simple queries (see Hamonic AI’s Scout, and more.

Fund Accounting / Administration

Venture fund accounting and administration involve managing capital calls and distributions, keeping the financial records of a fund(s), tracking carry, handling portfolio company valuations, and preparing reports for LPs, regulators and GPs. It is detail-intensive work that must be accurate, comply with accounting standards, and delivered on a timely basis. Since these workloads are heavy with repetitive, data-intensive tasks, fund accounting and administration is a prime function for automation. Many firms still rely on manual data entry, reconciliations, and spreadsheet-based reporting, which are time-consuming and prone to error, but there’s a push to digitize this work so back-office professionals can concentrate on strategic financial oversight. New AI products in this space (see Juniper Square’s Junie AI) will be ones to watch.


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance


Date Published

May 14, 2025

Share this

To minimize the operational burden that investors face managing portfolios of private companies, Standard Metrics provides a document data parsing service that surpasses traditional internal processes in accuracy, speed, and centralization.

Historically, we’ve ensured high data accuracy and traceability through our U.S.-based team of skilled analysts, supported by in-house workflow software. This precision is essential, as the data we onboard is used by our customers for audits, LP reporting, valuations, and other mission-critical functions.

As we scale, our goal is to deliver this data faster and at higher volumes — without compromising accuracy — so that firms can access more insights about their portfolio, more quickly. AI holds great promise for streamlining this effort.

Our customers have tested other off-the-shelf AI solutions and found them lacking in accuracy. Alternative approaches, like document mapping, have also fallen short, proving time-intensive and requiring frequent rework as financial statement formats evolve.

Standard Metrics has pioneered a new solution: an AI agent that supports, rather than replaces, a managed data services team to ensure efficiency and accuracy.

 

What we built

We’ve moved from a human-centered document parsing process to a multi-step parsing flow built to improve speed of parsing while maintaining high accuracy.

To support this flow, we first rely on pre-processing to split large documents into smaller parts and classify those smaller documents by type (balance sheets vs. income statements as well as PDFs vs Excel files, for example). More on why can be found here. We then have the LLMs take a first pass at parsing these simplified documents. Our managed data services team then can QA, edit, or add to the LLMs’ work inside of our application. Along the way, we also measure accuracy of our AI-parsed metrics on a continuous basis and track errors over time through a rigorous evaluations process.

Our AI parsing effort has grown rapidly over the past couple of quarters and now handles a significant, double-digit percentage of all data points parsed by our managed data services team. Each human analyst on our team is able to ingest more data, shifting some of their time from ingesting data to overseeing our AI agents’ work. We expect this trend to continue moving forward. This means we can parse more customer data, more quickly and help customers save on overhead costs from manual and inefficient internal document parsing. We’ve also been able to maintain our aggressive internal standard for data accuracy by scaling AI data parsing with close human supervision and QA.

 

What’s next?

Beyond continuing to improve AI-powered data parsing, the Standard Metrics team is also increasingly leveraging AI to make portfolio monitoring and reporting easier for our customers. A few things we’re designing and building include:

  • Natural language questions & answers: Ask the questions you need from your data in natural language (e.g. “What is the cash balance for this company over the past 5 quarters?” or “What are some companies that might compete with this company?”) rather than complicated queries, and get natural language responses back.
  • Custom reporting: Build unique visuals and reports in-application with natural language queries rather than SQL.
  • Summarization: Finding the important key topics and trends across quarters of data and notes to make reporting faster.

 

We’re excited to embrace the speed and reasoning capabilities of AI, while maintaining a component of human supervision where consistently high accuracy is paramount. Get in touch via the form below if you’d like to learn more about how we can help automate your portfolio reporting process.


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance


Date Published

April 2, 2025

Share this

At Standard Metrics — the portfolio management platform for VCs — we believe that AI can be massively enabling in automating repetitive tasks, analyzing vast amounts of data quickly, and improving customer experiences. As Large Language Models (LLMs) like Open AI’s GPT-4 or Anthropic’s Claude have expanded their capabilities and accuracy, we have been increasingly focused on using AI to build better, more efficient products and processes — most notably by increasing our output of documents parsed by AI as we bring on more customers.

In our initial product experiments, we tried using LLMs to parse large documents for complicated tasks without implementing any segmentation by document type. However, extremely high data fidelity is critical for our customers to power processes like LP reporting, valuations, and audits, and we needed to identify mechanisms to further increase parsing accuracy. We’ve been researching ways to improve the accuracy of our AI-powered processes and landed on two critical steps: limiting context (e.g. the amount of information the LLM has to process) and simplifying jobs (e.g. what the LLM has to do to that information) for better in-context learning.

Here, we’ll discuss how these models think, present two different research findings on context length and job complexity that inspired us, review the steps we took to improve our AI-powered processes with this research in mind, and talk through what we’re focused on next. Our hope is that it can be helpful to others concerned with implementing AI best practices into their engineering and product workflows.

 

How models think

LLMs are only as good as the data they are provided. Models are initially trained on a set of data in order to, essentially, predict the next word (or two) in a sentence. They use massive volumes of training data along with the user-provided prompt to influence their prediction of the next word in the sentence you’re writing or in the question you’re asking. Some predictions have large amounts of data that can make a model extremely confident what the right answer will be.

For example, if we ask someone to pick the next word in this sentence: “Roses are Red, Violets are ___” every single English speaker on the planet will fill in the word “blue”. Models have consumed all of the same information we have (and much more), which will lead them to confidently answer the same: Blue.

This is an example of a model relying solely on its training data; there is sufficient information about this topic baked into the model that it is capable of answering this question.

 

Defining in-context learning

But while models are initially trained on a set of data, models can also consume the prompts you provide and use that information to support their answers. This process is often described as in-context learning or prompt engineering and is an adaptable way for LLMs to perform tasks without excessive extra training.

For example, here we tell the model that our favorite number is 17. We then ask the model what our favorite number is.

The model gets this right with no issues. We’ve provided new context — our favorite number — and given the model a very simple prompt for interfacing with this context. As a result, the model correctly used the context from our prompt in guessing the right words for its output.

 

So what’s the problem?

When we integrate AI-powered processes into product workflows, however, we’re often giving LLMs significantly more context (for example, a hundred page document of favorite numbers) and significantly more complicated tasks (for example, “match a name and address to each of those favorite numbers”) than the example above. These context-heavy, complicated tasks can lead to errors, which impact every company leveraging these new AI models.

Context length learnings

Given this, we decided to explore reducing the amount of context that we were feeding our LLMs, researching how different context lengths affected accuracy. In a recent study, researchers measured the accuracy of recall from models with different token context lengths. A larger primer on what constitutes a token can be found here. (The TLDR? A token is a sequence of textual characters that equals about 3/4ths a word.)

The chart below shows that models perform well at their task with high accuracy with less than 4,000 tokens of context. But as the context gets larger, models start to struggle with performance.

This is because as the amount of context that the LLM needs to process increases, the job asked of it in one go becomes more varied and, thus, performance starts to degrade. It becomes difficult for the LLM to predict what the next word should be, given the larger context it must sift through.

Prompt complexity problem

We also wanted to understand the types of prompts that led to performance degradation (and avoid asking them in the future). One blog post from Oscar Health was particularly helpful. In it, the Oscar Health team asked an LLM to get the color of a large list of objects like a life jacket, cranberry, or blackberry. The match rate for this was very high, nearing 100%.

Next, the LLM was asked to:

  1. Classify colors.
  2. Rank the colors in order of frequency.
  3. List all of the objects of the most frequently occurring color first.
  4. List objects with like colors together.

It’s unsurprising that the LLM scored less accurately on the task as a whole, given the additional complexity. However, what is surprising is that the LLM’s performance on the task of getting the color right within this set of directions – something it did easily with a less complex task — also significantly degraded. The plot below shows the % of colors that the LLM got correct.

Given the additional instructions, the LLM’s accuracy degraded in both its original task and the task as a whole.

 

How did we implement this research?

From our research, we understood that with a little context and simple prompts, in-context training can be accurate and reliable. The clearer the context and the prompt, the more accurate the LLM is. With this research in mind, we worked to decrease the amount of information our LLMs had to process as well as the complexity of the jobs we were asking it to complete.

For example, our previous process for AI document parsing — where we pulled startups’ financial data into our platform — was a “single-step parsing” process that applied the same prompt and pre-processing steps for all document categories, with all internal “steps” of the process in the same order. On top of uploading large document dumps (e.g. excessive context), this process also led to non-specific, complicated prompts that weren’t segmented by document types.

Now, we are splitting processing to a “multi-step parsing” workflow where we pre-classify what type of document the LLM is parsing (balance sheets vs. income statements, for example) in order to give different documents more specific and simpler prompts.

 

What’s next?

Broadly, Standard Metrics is aiming to increase our use of AI with more customer-facing AI analysis features, more documents parsed by AI, AI data summarization, and much more while maintaining extremely high accuracy rates. We’ll be continuously iterating with the principles of shorter context and less complex tasks in mind as we leverage AI in new ways on our platform.

Let us know if you’d like to learn more!


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance


Date Published

March 6, 2025

Share this

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.

Structured data warehouses optimize investment strategies

 

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.


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance


Date Published

November 13, 2024

Share this

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.


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance


Date Published

September 30, 2024

Share this

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.


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance


Date Published

August 22, 2024

Share this

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 9,000 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.


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance


Date Published

August 13, 2024

Share this

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.


Automate your portfolio reporting

Find out how you can:

  • Collect a higher volume of accurate data
  • Analyze a robust, auditable data set
  • Deliver insights that drive fund performance