Put Your Constituents at the Center of Your Data Strategy

Learning Objectives

After completing this unit you will be able to:

• Explain why a constituent-centric data strategy is useful to educational institutions.
• Describe the steps in data strategy pre-work.

This module provides information about the managed package version of Education Cloud. As of March 2023, new or migrated customers use Education Cloud integrated platform solutions instead of managed packages. For information about the integrated platform solution, go to our Education Cloud Documentation.

The importance of data continues to accelerate at a pace that’s not likely to slow down anytime soon. Here’s the good news: your institution isn’t lacking for data. You may have decades (or maybe even centuries!) worth of this valuable asset collected and stored in various systems.

What’s the not-so-good news? When data is siloed and inaccessible, its value can’t be realized. Instead of being exciting and full of potential, unmanaged data can be chaotic, overwhelming, and a bit of a mess. And even more concerning than disorganization or lost opportunities is the vulnerability that comes with siloed data. When you don’t have a consistent way to secure data across systems, it's easier for the data to be exploited or for you to miss red flags since you can’t see the full picture.

A data strategy helps bring order to your institution’s processes of data collection, storage, and security, and makes it possible to access and analyze the gold mine of information held within the data. Armed with these insights your institution can make strategic and sound decisions.

In this module we explore the benefits of a constituent-centered approach to data strategy in education. With a strategy that prioritizes constituents such as prospects, students, and alumni, your institution can use data to provide the most engaging and compassionate experience to these valued constituents.

To illustrate what it can look like to implement a constituent-centric data strategy, we follow along with several departments at (fictional) Cloudy College as they collaborate on their data strategy plans. Here’s a quick introduction to Cloudy College and Cloudy’s Salesforce Administrator.

• Cloudy College is a private, liberal arts college in the Northwest United States. Founded in 1964, Cloudy College recently enrolled more than 3,800 students for the fall semester. Cloudy College uses Salesforce for Education to power much of its campus operations.
  
  
• Nina Brown is the Salesforce Administrator at Cloudy College. Nina has seven years of experience in Salesforce administration in higher ed and has been the Salesforce Admin at Cloudy College for the past four years. She frequently collaborates with colleagues across major departments of the institution including those in the offices of recruitment and admissions, advising and career services, and advancement.

Understand the Value of Data Strategy

As you work through this module you will take a deep dive into data strategy in education. In this unit, we start with the basics by answering two key questions:

• Why is a constituent-centric data strategy an asset to education institutions?
• How can your institution begin planning a successful data strategy?

Get a 360-degree view of your constituents
Good data is critical for constituent engagement. With a constituent-centric data strategy you can form a 360-degree view of your prospective students, current students, and alumni. What does this mean, exactly? It means you leverage the data you’re already collecting from constituents to truly understand their experience at your institution and what’s most important to them each step of the way. Consider these examples from Cloudy College.

Nina is preparing to facilitate a kickoff meeting to start planning Cloudy’s data strategy. She will be joined by representatives from departments like recruitment and admissions, career services and advising, and advancement. She has asked the department reps to pinpoint their constituents’ greatest needs, based on high-level feedback they’ve recently gathered. Nina is excited to hear from these teams because collectively they represent the entire student lifecycle at Cloudy College. She captures everyone’s replies and makes note of the Salesforce solutions she can offer to help fulfill these diverse constituent needs. Take a look at her notes below.

Nina knows an important part of the Cloudy College data strategy plan will be to include a way to track input and adjust as constituents’ needs change. She notes a few suggestions.

• Use feedback loops like surveys, Chatter groups, and email to collect up-to-date data.
• Use Salesforce reports and dashboards to track trends.

Improve business processes 
Business processes depend heavily on data completeness and correctness. With a solid data strategy in place, schools can streamline information flow between campus departments, standardize processes, and improve work quality for end users.

Nina has a unique perspective on the value of improved business processes at Cloudy. Because she frequently works across departments she knows there’s great work happening all over campus. She also knows there are duplicate efforts and data is getting siloed as each team follows its own protocol without a campus-wide strategy in place. Nina knows that implementing a data strategy may seem daunting at first, but she’s confident there will be buy-in when everyone sees how it will improve the constituent experience and streamline the efforts of faculty and staff.

Plan for Success

Once you realize how impactful a data strategy can be for your institution, you may feel tempted to race ahead and implement one, ASAP. But as with all great things, a little patience and planning are necessary to develop a strategy that will best serve your school. Let’s look at some important pre-work for you and your team of collaborators to complete.

Start with the Data Maturity Model
To plan for the future you have to have an accurate understanding of where you’re starting. The Data Maturity Model can help you get a clear picture of where your institution currently stands with data.

The Data Maturity Model framework includes four stages: Digitize, Describe, Predict, and Act.


When Nina applies the Data Maturity Model to Cloudy College’s data journey, she sees that Cloudy has completed most of the work in the Digitize stage and has some initiatives underway that fall in the Describe stage. The issue is that each department is currently approaching reporting and analytics individually.

Nina often fields questions from enthusiastic Cloudy stakeholders about innovations like artificial intelligence (AI), predictions, and algorithms. It makes sense to Nina why these questions and requests come up so often — this is where data work feels most cutting-edge and exciting. She’s eager to implement more of this technology, too! Nina is grateful to have the Data Maturity Model as a resource to share with the data strategy team. It helps explain how data work happens in stages, and how important it is that they not skip over the less glamorous work represented by the first two steps. The foundational work is critical to data fluency. The more Cloudy College invests in this preparation, the more useful and impressive their future AI initiatives will be.

Identify your data sources
The next step in your data strategy pre-work is to get a sense of where all of your institution’s data is coming from. Some data sources may be obvious and come to mind right away. You may have to do a bit of digging to find others. One of the meeting prep tasks Nina asked department reps to complete was an audit of data sources for their respective teams. She’s started to compile their answers in the worksheet below.

Get ready for integration
As you can see from Nina’s worksheet, one significant data source at Cloudy is existing systems. It’s really common for colleges and universities to use multiple technology tools and systems. Unfortunately, disconnected data prevents you from gaining that full, 360-degree view of your constituents and operations. It also leads to data inconsistencies that are expensive and time-consuming to rectify. The solution is integration.

What do we mean by integration? An integration is the process of linking together different computing systems and software applications to act as a coordinated whole. There are both technical and business aspects to data integration.

Integration is an ongoing conversation at Cloudy, and Nina has found a particular resource that’s been very helpful to her. It’s the Build an Integration Strategy workbook, created by Salesforce.org. (You’ll find a link to this workbook in the Resources section of this unit for easy access.) This is a free resource that’s helped various stakeholders at the school understand what integration is and how Cloudy can approach the work. For the data strategy meeting, Nina has focused on a particular table in the workbook, the Data Element table. She’s started to fill in Cloudy-specific systems and metrics.

Assess your institution’s data quality
The final best practice we suggest is to formally assess your institution’s data quality. Data quantity is rarely a problem. But assessing your data’s quality is a step that is often overlooked despite its major impact.

For this step, we recommend using The Healthy Org Workbook as a guide. This is another free resource provided by Salesforce.org, and you guessed it, you can find a link to this workbook in the Resources section below.

At the highest level, data quality analysis addresses two key issues: where collected data is going and how it’s being used. To drill down here, ask yourself and your collaborators the following questions about each respective data source:

• Who has access to this data?
• Which standard objects are in use?
• What are the custom objects or fields that have been created?
• Which automations are in use?
• How much data do we have?

It’s not uncommon for education institutions to have large volumes of some categories of data. If that’s the case for your school, we recommend keeping this resource handy: Best Practices for Deployments with Large Data Volumes. This workbook offers support with best practices, tips for optimizing performance, and case studies that provide insight into data volume challenges.

Nina completes some of the data strategy pre-work on her own, and delegates other tasks to fellow data strategy committee members. She feels confident about the structure the pre-work will bring to the first planning meeting. 

Keep reading to follow Cloudy College as they define constituent journeys and map them to their existing business processes.

Resources

• PDF: Build an Integration Strategy
• PDF: The Healthy Org Workbook
• PDF: Best Practices for Deployments with Large Data Volumes