An association’s data is its greatest resource. Yet, many associations, regardless of size, struggle with establishing an effective data management system.
Industry consultant Wes Trochlil shared his core rules for data management in a 2019 webinar hosted by ACGI.
Here, we recap his 15 tips for managing an organization’s database. These key rules can be immediately applied to any organization and association to improve processes and data management strategy.
There’s an old saying: “A business is either growing, or it is dying.” This same principle applies to data management: “Your data is either getting better, or it’s getting worse.”
Data–good or bad–can easily find itself in a constant cycle. If the information in your data management system is perceived as “bad,” staff and users will not want to utilize it. Therefore, they will create disparate systems and information silos.
This is what’s known as the “cycle of doom,” meaning if a data management system is bad, people won’t use it, which makes the data even worse and outdated.
Ideally, associations want a data management system that’s in a “virtuous cycle.” In a virtuous cycle, data is good–meaning it gets used and updated continuously. This leads to even better data that builds off itself.
If you’re looking to break from the cycle of doom, or maybe just want to continue on the virtuous path, these tips will help you in your goals.
These are two distinct documents that work to create long-term success for your organization’s data management system.
Data entry guidelines are rules set out in a simple document outlining how your organization handles data entry. For example, it clarifies what data goes into what fields. It also outlines how data should be entered into the system.
For example, in your organization’s address field, how do you format it? Do you spell out “street” or abbreviate it? Do you capitalize everything? This all becomes clear in your data entry guidelines document.
Data entry guidelines will also define organization-specific fields and their uses within the organization.
User guides are longer, more comprehensive documents that lay out how data is processed in the data management system.
These documents clearly define processes to create continuity between staff who use the data management software. This helps combat any “bad data” and standardizes processes for clarity.
Creating these guides is no easy or quick feat. However, the time and money it will save your organization in the long run is invaluable. So while it may be tedious work, it is critical.
Ultimately, organizations will spend less time creating user guides than cleaning up the errors made without them.
Association Management Systems and other software are managing fairly complex sets of data, regardless of how simple the organization or process may be. With any association, data management can get large–and complicated–quickly.
This is why training is essential for any staff that will be using a data management system within an organization. It’s unfair to expect staff to teach themselves or learn as they go. Instead, leaders must make training available as needed.
Data management training can come in two forms:
When training staff to use data management tools, it’s important to keep a few things in mind:
Training helps staff use data management software to its fullest potential.
If an organization is planning to implement a new procedure or process in its data management system, testing is a crucial component in this launch.
All new processes or updates should be tested before being rolled out in a live environment. This way you know it is successful and working as expected, instead of encountering errors along the way.
New system rollouts can be painful if testing isn’t completed effectively and efficiently. By showing updates to staff first, you can ensure peak performance for customers.
Shadow systems are any list of data that is actively managed outside of your primary data management system.
Shadow systems often mean data is updated and changed in these outside locations and may not be reflected in the primary systems. This can lead to a lot of headaches and disorganization in the future. In fact, shadow systems are a symptom of the dreaded “cycle of doom.”
Identify where shadow systems are occurring in your organization. Learn who is creating them and why. Then, establish how you can integrate that information back into the primary system.
By not addressing any shadow systems, an organization will sabotage efforts to keep data clean and central.
Often, managers will hear issues that apply to a few or even an individual staff member.
A common mistake in data management is creating excess processes that tackle these rarely-occurring issues. Instead, management should focus on issues that happen frequently and have a large reach.
Organizations should use their data to determine how often specific issues arise and what percentage of constituents they affect.
We may perceive certain processes as overwhelming and difficult. When in reality, analysis can reveal the issue happens so infrequently that facing it is much easier than previously thought.
Data collection will occur outside of the primary management system. That is inevitable. However, processes should exist to bring this key data back and integrate it fully into the main system.
Key data that does not exist in the primary association management system may come as a result of third-party systems.
Third-party systems are not shadow systems, but instead outside systems approved and identified for use. They often fill gaps in an existing management system. For example, Eventbrite is a common third-party system used for event registration in associations.
These third-party systems are collecting data that must eventually be directed back to the primary AMS. This helps maintain transparency for constituents. It also allows staff to be more knowledgeable about their membership.
Remember–you never want to be in a position where you have to ask a constituent for information that is already provided.
To be clear, your organization’s data management systems should be capturing financial and volunteer interactions already. This step covers everything outside of that.
These non-financial and non-volunteer interactions have value to you and your constituents. But first, you must determine which of these outside data is most useful. Then, you can work to capture and integrate them into the association management system.
For example, you may want to include data on members who have had meetings with senior staff. A record of these meetings can drive and personalize future interactions.
Capturing this information improves communication and overall relationship with constituents, allowing them to feel known.
Data integrity reports are reports that allow organizations to identify potentially erroneous data within their management systems.
You can build reports to help you easily parse through bad data. For example, noting that an email address in a given field must include an “@” sign. If it doesn’t, it will be flagged for automatic review.
Run as many reports as frequently as you’d like to find potentially bad data points within your system. By creating these reports, you are in turn creating good data–and cleaning it as you go.
If you’re using off-the-shelf software like ACGI’s software solutions, there may already be external user groups available. These are groups of people across organizations that use the same data and association management systems.
However, in addition to these external groups, it’s important to also establish internal user groups.
These are individuals within the staff of a single organization that use the same data management system. Your entire organization can benefit from having this group meet regularly to discuss updates and operations within the database.
Internal user group meetings open a line of communication within your organization. They allow users to discuss things such as: What changes are occurring? What challenges are we having? Are certain data points becoming important? Are we creating new data?
These meetings also help with information sharing, troubleshooting, and overall sync in data use and processes.
Your organization’s data will be more apt to be “good” and cleaned if teams are synced on its use and growth.
In database management, nobody cares unless there’s a problem. But as soon as an error occurs, there’s a huge issue.
That meaning, staff often does not pay attention to all the good a data management system does, and only takes notice when there’s a mistake.
Get on top of this threat with a database PR plan. By giving your data management systems a little public relations work, you are able to keep all of the successes of the system at the forefront of people’s minds.
You can achieve this through sharing success stories and positive growth stats at meetings or in a monthly email. This way, you create open lines of communication on how management systems are consistently affecting the company in a positive way.
We often focus entirely on direct costs when managing data and technology. How much will it cost to implement software? How much does customization cost? What is the price to clean our data?
Direct costs are the literal bill you will receive for certain products or services. And while important, they often overshadow another crucial element of business success: indirect costs.
Indirect costs of managing data and improving technology are plentiful. For example, take rule number two. Documenting all of your processes will take time and money. This is the direct cost of creating it.
The indirect cost of not creating these user guide documents is the time and money that will be wasted down the line when issues need to be fixed. Indirect costs are the money saved by these documents down the line.
Consider indirect costs in all decisions surrounding data management and software.
It’s important to acknowledge the inevitability of mistakes in your organization’s data management. Decisions being made today will seem wrong in a few years; that’s the benefit of hindsight.
How we react to these inevitable mistakes is what matters most. We can’t blame anyone, we can’t deny it, we just have to fix it.
Take all the new information and hindsight gained over time and apply it to your next data management decisions. This mindset allows you to be constantly aware of room for growth.
The key to long term success? Acknowledge mistakes, move on, and address them swiftly.
This quote from economist Thomas Sowell explains how every decision has some cost associated with it.
In the world of data management, this might mean the software you purchase will be top of the line, but it may also be really costly.
Or perhaps your chosen software can do everything your organization needs it to do, but it is convoluted in its user experience.
An important part of developing your data management strategy is being aware of these trade-offs in every decision. Then, you’ll be able to determine whether you’re willing to accept them.
Data is changing so rapidly. Chances are, your organization’s database is out of date at any moment.
This isn’t the fault of your data management system. It’s only because the information in our current world is constantly changing.
This means your data will never be perfect. And if perfection is impossible, that should never be the goal of your data management system. Instead, your goal should be to use whatever data you have at the moment to find success.
These 15 goals in particular should help you find this success, instead of striving for perfection.
Data is a key part of membership management systems, revealing important insights. Learn how to make this data work for you–and your bottom line–with this latest blog from ACGI. Click here to read!
Take full advantage of your association’s data and management with ACGI Software. Our experts are here to provide data management solutions for any organization. Contact us today to request a demo.
