Prioritizing Data Quality by Olivia Arstein-Kerslake and Lauren Baur

Date: Wednesday, January 22, 2025

Hello! We are Olivia Arstein-Kerslake, Senior Data Intelligence Analyst and Lauren Baur, Data Intelligence Associate from VentureWell’s Data Intelligence (DI) team. VentureWell is a Massachusetts-based nonprofit that specializes in funding, training, and cultivating a pipeline of science and technology inventors, innovators, and entrepreneurs. Together with our stakeholders, we are driven to solve the world’s biggest challenges and create positive social and environmental impact.    

VentureWell collects a vast amount of data about our participants, programming, and related organizations. Every day, people across VentureWell use these data to inform and amplify our impact via securing funding, making data-informed decisions, evaluating program effectiveness, and tracking program outcomes. So data quality is essential! Read on to learn about more data quality, why it matters, and the ways in which we support data quality at VentureWell!

What is Data Quality?

Data quality refers to ensuring data are reliable and trustworthy for analysis, reporting, and decision making so they can be used to help meet the needs of users. To achieve this, organizations like ours implement ongoing processes to identify and fix errors, inconsistencies, and inaccuracies. When assessing data quality, we look at the set of dimensions described in the visual below.

Source: DQlabs

Why Does Data Quality Matter?

• Enhances decision making: To make high-quality decisions we need high-quality data! Having high-quality data allows us to understand needs, identify gaps, and prioritize initiatives that have the most impact.
• Improves program evaluation and impact measurement: High-quality data help us understand the impact of our programs, and are critical for informing our organizational growth and strategy.
• Builds trust with our clients and customers: Organizations rely on the trust and support of our funders, partners, and participants. Transparent, accurate data and systems help to build and maintain that trust, particularly when reporting on outcomes, financials, and impacts. Inconsistencies, inaccuracies, and anomalies can damage credibility and reduce confidence in an organization’s work and systems.
• Streamlines operations: Duplicate records, missing data, and outdated records can hinder processes and create extra work for staff. Prioritizing data accuracy can reduce administrative burdens and save time.

Tips to Improve Data Quality

We support data quality at multiple phases in the data life cycle:

• Quality at point of collection: Our first opportunity to ensure quality occurs the moment we collect the data. First, we design forms (surveys, reports, etc.) in a user-friendly way that reduces errors. Then, we ensure we are collecting enough data and the right data to reflect the needs of our funders and business processes. Lastly, we set up rules within forms to prevent errors, such as only allowing people to enter numbers in fields that require a numeric response.
• Review of data as they enter our system: Using a methodical approach, we regularly review any data that enter our systems. This includes ensuring integrations with other systems are working properly, as well as checking that manually-entered data are accurate.
• Quarterly platform maintenance: On a quarterly basis, members of our Data Intelligence team conduct data reviews to standardize data, de-duplicate records, and resolve anomalies. 
• Review of data processes: Our Data Intelligence team regularly assesses our data processes to determine how well they are meeting current needs. We seek out opportunities for improvement and pilot process changes.

Check out our post tomorrow on creating an organizational data use working group!

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