Managing your data for the benefit of your organization is not a destination but a journey. It’s a journey that ultimately matters to your organization’s bottom line and success in making data informed decisions within today’s competitive fundraising environment.

In this interactive workshop, we’ll cover basic data governance concepts and models. These concepts and models will be leveraged to develop a roadmap for tackling some of the challenges that you and your fellow attendees are facing in starting up or maturing your data governance program.

Amid rapid growth in data complexity and technology advancement, many organizations still struggle to manage, understand, and measurably improve their legacy and evolving data ecosystems. In this hands-on data lab, you will learn how to build flexible data enrichment models, reusable tools and repeatable processes to 1) establish a constituent data integrity index/baseline 2) explore, visualize and measure constituent data integrity 3) identify and prioritize opportunities to improve your overall data quality in systematic and sustainable ways towards insights, equity and efficiency.

Defining the right KPIs is an evolving process. The tools and visuals used to report on them can also evolve over time. At McGill, we’ve been reporting on prospect pipeline indicators for over 15 years. During that time, we’ve moved from collating printouts in a 3-inch binder to a one-click dashboard. Discover how we refined the process over time, what considerations were taken at each step of the evolution and how we adapted to changing needs and to our growing expertise.

At our educational institutions, we use assessments and remediation for better student outcomes.
So, how can you improve your data outcomes?

In this session – we’ll cover how to leverage the data quality life cycle to improve your data.

By grading your data (on consistency, validation, and recency) and remediating the elements that aren’t passing the grade – you can ensure that every data outcome is "better".

We created dashboards allowing a pro-active approach to successfully collecting pledges. By becoming aware of the high risk pledges, Fundraising professionals were are able to focus on the high risk donors, to properly steward and sustain their affinity to the university. Thus, mitigating the risk and increasing the chance that the donor will fulfill their pledge.

Much of the conversation about improving predictive models focuses on finding the optimal model technology, such as linear regression, logistics regression, classification trees, etc. We argue that there should be just as much focus on Exploratory Data Analysis (EDA) techniques by exploring, diagnosing and transforming the data - before, during and after the modeling process. We will also demonstrate how to apply different Exploratory Data Analysis (EDA) techniques to find optimal transformation and identify outliers.