You're a venture capitalist. How do you know which data analytics tools are worth investing in?

Property Assets- Interest and Experience Partly due to the wide and changing nature of Proptech, the investment opportunity resonates with HNW/UHNW investors and Family Offices. Most investors within this space will own multiple properties across different sectors ranging from their main homes, investment properties, holiday homes and commercial buildings (small or large) as part of a wider portfolio. This will be on top of exposure with REITs, shares in commercial landlords and other indirect exposure to the sector with estimates that roughly 30% of a UNHW portfolio is property-related. Clearly, property is a familiar asset class.

Setting the right expectations that align with your goals is key. The tech tools you use are the most important part of your workflow, only after your network and Investment Acumen. 1. Sourcing deals based on your thesis and strategy. 2. Managing your portfolio through KPIs almost in real-time. 3. Devicing strategy: sizing and timing of deals. All the above require a sweet sync between your ideology and in-house data processing capability. A right balance of capabilities in processing public data for trends, sentiments and private data to help make the best decision possible. At the end of the day, this is a people-driven industry. So, ensure that you have fun along the way and empower your team with the right training and tools.

Goals when crafting an investment strategy should be clearly defined. Examples of proper data usage could include geographical and vertical trends

The main question you should ask yourself is in what workflows do I wish to leverage data analytics tools. There are 3 main workflows in the investment process: (1) Sourcing; (2) Due Diligence; (3) Portfolio Management. There are additional sub-workflows such as market research and deal monitoring which usually fall under one of the main 3 workflows. Each workflow and it's respective data analytics efforts are different. Additionally, one must consider the investment stage they focus on. If you focus on A+ series, sourcing 'people in the garage' is not the main focus. Its more about identifying those hockey curves before anyone else. The best way to go about each workflow is to utilize dynamic data.

Firstly, do a deep analysis of your firm and define the following Does becoming a tech-heavy, data driven firm align with your strategic goals and investment thesis? What is it that you want to achieve with data analytics tools, whether it's analysing trends, optimizing processes, or automating work flows?

Secondly, figure out the following: - Resource Allocation: Assess your resources as they directly correspond to evaluating your financial and human resources. Understand the costs (initial and recurring) and the expertise required (data scientists, engineers) for implementing and operating data analytics tools. - Integration Capability: Are these new tools compatible with your existing systems?

As a venture capitalist, assessing your resources is crucial before investing in data analytics tools. From my perspective, it's important to evaluate both financial resources and the human capital available to implement and utilize the tool effectively. Consider the cost of the tool against your budget and the potential ROI. Additionally, assess whether your team has the necessary skills to leverage the tool to its full potential or if training will be needed. Investing in a powerful tool is only worthwhile if you have the means to support its implementation and ongoing use, ensuring it contributes positively to your objectives.

To maximize the value of any tool, balance its cost against your budget and the expected return on investment. Equally crucial is assessing your team's ability to harness the tool's full potential. Determine if their skill set aligns with the tool's demands or if training is necessary. Remember, the true worth of a tool is realized only when you have the financial capacity and human expertise to effectively implement and utilize it, aligning with your strategic goals.

Reflecting on how tech giants like Google navigate their tool selection, it's evident that choosing data analytics tools goes beyond just budgeting. It's about picking solutions that resonate with your team's skill set and mesh well with your current tech stack. Opt for user-friendly tools that empower rather than overwhelm, ensuring they fit your team's expertise. This strategy not only optimizes resource usage but also enhances your decision-making process with accessible data insights. It’s a practical approach that encourages efficiency and drives sustainable growth.

One mistake a lot of investors do is 'buying' what their peers have instead of being realistic towards their goals. Do you need a tool that costs $25k / year and will exhaust all your resources? Many times, nimble tools might offer lest functionality when it comes to data coverage, but will surpass existing solutions in their ease-of-use and UI. Ultimately, focus on the functionality the tool provides and not so much it's prestige. If you have unique perspective on investing, you must definitely consider relying on the new 'age' of tooling to leverage new dynamic data points such as: (1) Hiring trends, (2) Product adoption, (3) Tech evolution, (4) GTM efficiency. They will provide you with a stronger edge, although not as comprehensive.

To evaluate VC tech stacks, start with a deep dive into Automated Data Funnels. Assess their integration potential and build-vs-buy feasibility. Next, scrutinize your processes for optimization and automation opportunities, and decide which to outsource - this will clarify tool comparison. Lastly, weigh custom-building a VC Tech Stack against off-the-shelf all-in-one solutions, considering alignment with your strategic goals. This approach offers a comprehensive, tailored comparison, guiding you to a decision that resonates with your unique business needs.

Third, compare all the VC tech stacks: - Automated Data Funnels: Do a feasibility study and integration capability study to compare options. Find out if you have the capability to build or integrate these funnels into the tools you're considering - Optimize, Outsource and Automate: Which processes can be optimized or automated and which do should you outsource will help you compare the tools. - Compare VC Tech Stack vs. Buying an All-in-One Solution: Maybe the solutions available in the market just do not align with your goals. Consider building one yourself.

It may be useful to actually run two or three data analytics tools simultaneously for a few months to then see which one to choose. Being able to compare the tools with live data will give you the best possible insight as to which one you'd want to use. Most tools offer monthly subscription plans which makes it easier to drop them later on.

If you can't get a substantial free trial to truly test a tool's capabilities - scrap it. Otherwise you will be 'betting' your yearly budget on something you might not need and will not give you the needed edge you are looking for. The space is too competitive to afford a year without the proper tooling. All reviews and recommendations from friends mean nothing if you use the tool and you don't 'fall in love'. On a demo call, explain your exact thesis and see how the sales rep highlights features that actually contribute to your specific workflows.

Looking at how top-tier companies like Salesforce navigate their tool selection, it’s clear that choosing the right data analytics tools is more art than science. Salesforce’s knack for picking tools that scale with their growth, ensure data security, and enhance user engagement sets a high bar. The key lies in diving deep into user reviews, taking advantage of free trials, and seeking advice from peers. This practical approach helps peel back the layers beyond mere specs, ensuring the tools you choose not only serve your current needs but are also future-proof. It’s all about securing tools that will grow with you, making every investment count towards your long-term vision.

Monitoring your ROI from data analytics correlates back to the reasoning you set out to purchase them in the first place. Assuming it was for one of your three major workflow, taking note shouldn't be challenging. (1) Sourcing - Did you discover new RELEVANT deals in a timely manner? (2) Due Diligence - Were you able to surface interesting signals that changed your view of a company? Negative or positive? (3) Portfolio Monitoring - Did you discover something new about your portfolio, their competitors? Assuming you take notes, incorporating another field in your CRM mentioning which 'source' powered some of these notes is relatively frictionless.

Now for the best part: - ROI Analysis: Monitoring results is your ROI analysis. Assess the impact of the tools on the investment decisions and returns help in evaluation of the effectiveness against your KPIs. - Keep on optimizing: Optimizing your stack should never stop. Keep on exploring new tools and compare your options. Like a startup, keep solving those problems.

Like Arabella, our astute shark, who monitors her hunting success, you must track the performance of your data analytics tools. Evaluate them against key indicators: ROI, revenue growth, customer satisfaction. Arabella's feedback from fellow sea creatures is akin to your need for team and stakeholder insights on tool effectiveness and usability. Adaptability is key, just as Arabella changes hunting grounds when needed. If a tool doesn’t meet your goals, be prepared to adjust or switch. This approach of vigilant monitoring and flexibility is vital for maintaining your edge in the venture capitalist waters. Spread Shark Love #divineintervention #gabenfreude

Il y a deux types d'outils : - la gestion du deal flow - l'analyse de chaque deal. Il existe pas mal d'outils, notamment pour mener les due dil. Mais, une fa?on non-outillée de derisquer sa décision d'investissement repose sur la réseau du fonds pour (1) avoir le bon deal flow (2) s'appuyer sur d'autres experts pour analyser chaque dossier.

Certainly, let's break down the key points in evaluating data analytics tools for investment: Market Potential: Assess overall market size and growth potential. Identify target audience and industries. Understand demand for advanced analytics solutions. Differentiation and Innovation: Evaluate uniqueness and innovation. Consider proprietary algorithms and technologies. Assess alignment with emerging trends and challenges. Scalability and Performance: Analyze scalability for increasing data volumes. Evaluate processing speed, accuracy, and efficiency. Consider adaptability to different data types and sources.

Venturing further in the data analytics journey, think of Arabella, our resourceful shark, exploring beyond her usual hunting grounds. In venture capitalism, as in the ocean, there are always unexplored territories ripe with potential. This space is where you can delve into those untapped areas, sharing unconventional examples, stories, or insights. Perhaps it’s a unique application of a tool that opened new doors, or an unexpected data trend that led to a breakthrough. Like Arabella discovering a new current that leads to a bountiful feeding ground, these explorations can lead to significant advancements. Embrace this spirit of discovery; it often leads to innovation and growth in the dynamic world of venture capital. #gabenfreude