The ins and out of data cleansing for venture capitalists

In venture capital, making data-driven decisions is essential to identifying investment opportunities that will move the needle. But, making those decisions depends on having accurate and reliable data. 

This is where data cleansing comes in. Data cleansing ensures that the information in a VC firm’s customer relationship management (CRM) system is accurate, consistent, and free of errors, allowing for more reliable deal insights and confident decision-making. Without clean data, even the most sophisticated data analytics can lead to poor investment decisions. 

‍Key takeaways

• Data cleansing is essential for ensuring that CRM data is accurate, complete, and usable, enabling more reliable decision-making for VCs.
• Automating data entry reduces human error, minimizes duplicates, and improves data consistency, ultimately streamlining CRM management for VCs.
• Data cleansing improves operational efficiency by minimizing the time spent handling errors, allowing VCs to focus on strategic activities like deal sourcing and due diligence.
• Tools like Affinity automate data cleansing tasks, helping VCs maintain clean, enriched data that supports better investment decisions and faster deal closures.

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What is data cleansing?

Data cleansing, also known as data cleaning or scrubbing, is the process of removing or fixing any incorrect, corrupted, improperly formatted, duplicate, or incomplete data within a dataset. To cleanse data, all data errors must be identified and corrected. 

If a dataset contains errors, the data integrity has been compromised and any conclusions or insights from the dataset are unreliable, even if they look correct at first glance. Data cleansing improves overall data quality and provides more accurate, consistent information for decision-making in venture capital firms.

Data cleansing is part of the data management and maintenance process that all firms should conduct to help make more informed decisions. 

Characteristics of clean data

There are four characteristics to look for when you are in the data validation phase of the process:

1. Validity: Ensure your data is not only entered correctly but is also useful to your firm. For example, if you’ve been collecting founder birthdates, is this something that is actually useful for your firm? And if it is, are the birthdays correct?
2. Completeness: Verify that your dataset has all required information to provide a holistic view of your firm’s activities. For example, if you are collecting emails, phone numbers, and tracking when you email a contact, do you have all of this information recorded in your CRM?
3. Accuracy: Ensure your data is as accurate as possible. This could mean cross-referencing your data against other sources to ensure you have all the correct data recorded in your CRM.
4. Consistency: Ensure your data is consistent across your datasets, such as using the same format for names and phone numbers. For example, 123-456-7890 and 123 456 7890 can be read differently when processing data so you should choose a singular format for your CRM.

Why is data cleansing important to venture capitalists? 

While data governance and cleaning might not be at the top of the priority list for many venture capitalists, they’re important nonetheless. Data cleansing ensures that VCs can make informed, confident decisions and manage their investments efficiently. 

Data-driven decision making

VCs rely heavily on data to assess the viability of potential investments. Clean, accurate data ensures that the decision-making process is based on reliable insights. Inaccurate or inconsistent data can lead to poor decisions, which can ultimately result in missed opportunities or even financial losses. 

Risk mitigation

By using clean and validated data, VCs can identify and minimize risks earlier in the investment process. For example, identifying anomalies, discrepancies, or missing data can help them uncover key risks in companies with inaccurate financials or unreliable metrics.

Improved due diligence process

During the due diligence process, VCs scrutinize various aspects of a company’s operations, including financials, market potential, and growth metrics. Clean data ensures that they have an accurate understanding of a company’s performance, reducing the risk of overlooking potential red flags.

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Operational efficiency

Having clean data reduces the time spent sorting through errors, inconsistencies, or outdated information. This enables VCs to focus on strategic decisions rather than sorting through incomplete datasets.

Improved relationships

When your VC firm is working from up-to-date and accurate data, like contact details and engagement history, it becomes easier to tap into your network effectively. Network data can be just as important as investment data, especially in the early stages of sourcing new opportunities. 

A step-by-step data cleansing process

While every data cleansing project will differ slightly based on the data sources, analytics requirements, and scope of the project, there are several steps VCs can follow to ensure they effectively clean their data.

1. Inspect and profile your data

The first step in the data cleansing process is to inspect and audit your data to identify the issues you’ll need to handle during the cleansing process. Most often, this involves data profiling, a process that analyzes and documents relationships between data elements, checks the quality of your data, and finds errors, discrepancies, and any other problems with your data. 

Profiling is an essential first step as it shows you what you need to look for and fix while cleaning, giving you a better understanding of your data.

2. Standardize data

Now that you’ve identified what you need to work on within your data, you need to focus on standardization—converting your data to a single format that is used firm-wide. Standardization ensures that the data can be processed, analyzed, and used to make decisions. This will also help you start to clean up structural errors, syntax mistakes, and other issues like capitalization.

Everything from dates to postal codes may be entered into your CRM in a variety of formats, making it difficult to query and report on your data. For example, United States zip codes can be entered as 90210, 902 10 or 90210-1000. By defining standardized data rules, you can resolve many issues and ensure new data is entered correctly into your CRM.

3. Remove duplicate data

Duplicate records are a common occurrence in CRMs—as more than one person in your firm may be interacting with the same people, it’s easy for someone to miss a record and create their own instead of adding to the existing record. Data deduplication is the process of removing or deleting the redundant records. 

This effectively removes the dirty data and leaves behind a single piece of data that makes your CRM more accurate. While VCs aren’t working with customer data per se, it’s still important to merge the data from both records into one so you capture all the essential information about each contact.

4. Filter unwanted outliers 

When analyzing your data, you may find an observation that doesn’t appear to fit within the data you’re analyzing. If this outlier is a result of improper data entry or a typographical error, removing it will improve the performance of the data. But, just because an outlier exists doesn’t mean it’s incorrect or irrelevant data. Be sure to double-check before removing any data that’s been marked as an outlier. 

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5. Resolve missing data

Missing values and data can very quickly become a problem with conducting data analysis. Without all data in place, many algorithms won’t be able to parse a data set. If you find that your CRM is missing values, there are a variety of steps you can take:

1. Fill in missing information: If you’re missing key information like a company name or email address, it’s time to do a bit of digging and find that information so it can be imputed into the database. With Affinity’s automated activity capture, this type of firmographic data is automatically added to your records based on your firm’s email and calendar activity.
2. Delete incomplete records: If you’re missing key fields for a significant portion of the records it might be best to remove them entirely. For example, if you have a record with just a name, and you cannot locate other information, it may be best to remove the record. 
3. Monitor missing data trends: Keep an eye on missing data trends. If you start to notice certain types of data are frequently missing, it could be an indication of a deeper issue with data collection or input processes that may need to be addressed in the future.

6. Verification and data enrichment

Once you’ve tackled missing, incomplete, and inaccurate data you’re well on your way to having a CRM full of high-quality data. The next step in the process is verification—verifying data like email addresses, phone numbers, and physical addresses can help you keep your contact information up-to-date. Verifying all of these values can also ensure there are no typos that could cause issues down the road and confirm the usability of your data. 

You can further improve your data with enrichment processes. A big part of data enrichment is data appending which brings together multiple data sources—like contact information or deal data from Crunchbase, PitchBook, and Dealroom—and appends them to your CRM to create a unified data set with additional data points.

7. Automate, monitor, repeat

Now that you’ve done the time-consuming work of cleansing your data, continuing to monitor your data for errors will help ensure your data stays clean. There are four types of problems to keep an eye on:

1. Missing data: Empty field, missing values, and non-relevant information.
2. Incorrect data: Data that has been entered incorrectly such as typos or null values.
3. Duplicate data: A single piece of data that has been recorded more than once in your CRM.
4. Misformatted data: Data is not formatted according to your standardization requirements.  

To keep all data as clean as possible when it enters your CRM, you can weave automation into your workflows. Automating data entry into your CRM is an effective way to eliminate data quality issues by eliminating human errors, standardizing input formats, and ensuring real-time validation.

Automation reduces the risk of duplicates, ensures data is entered quickly and consistently, and enhances the quality of contract records through real-time updates and data enrichment. It also allows for faster data processing, freeing up your time to focus on higher-value tasks like meeting with founders and investors. 

With automation, VCs can scale their CRM management efficiently, improve deal insights, and enhance decision-making. Ultimately, automated data entry leads to more reliable reporting, better resource allocation, and more effective use of your CRM. 

Data cleaning tools: What they are and how to use them

There are a variety of tools available to automate data cleansing tasks. Many of these solutions can help standardize fields, replace null values, fix punctuation, and combine duplicate records. Affinity is the CRM solution designed for VCs that automates data entry, saving your team time and money while keeping your data clean. 

Affinity captures every email and calendar in your firm’s inbox to automatically create a comprehensive record of people, companies, and engagement history in your firm’s network. It also enriches these records with key deal data from industry leaders like Crunchbase, Dealroom, and PitchBook—providing you with a single source of truth for relationship and deal management. 

The data enrichment helps dealmakers uncover warm leads and prioritize the right opportunities to close deals 25% faster.

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Data cleansing FAQs

What are the methods of data cleansing?

Common methods of data cleansing include removing duplicates, correcting errors, filling missing values, standardizing formats, and validating data to ensure your data is high-quality.

What are examples of data cleaning?

Examples of data cleansing include removing outliers, correcting typos or formatting errors, handling missing values, and ensuring consistency across datasets.

How do you handle missing data?

Handling missing data involves techniques like finding and entering missing information, removing entries that have excessive missing values, and monitoring missing information to ensure data inputting processes are functioning correctly.

What’s the difference between data cleansing and data transformation?

Data transformation is the process of converting data from one format or structure to another whereas data cleansing is the process of removing errors from a dataset.

How can you check if your data is clean?

You can check if your data is clean by performing validation checks, reviewing for duplicates, ensuring there are no missing values, and comparing against trusted data sources for accuracy.