Stop the Daisy Chain of Bad Data Management

Stop the Daisy Chain of Bad Data Management

The root cause of bad data management for any organization can usually be found in the data management paradigm itself. The most damaging data management paradigm – one that is commonly found among nonprofits – is what I call “the daisy chain of bad data management”.

Ladies and gentlemen, we need to stop the daisy chain! And today is a good day to get started.

A quick primer on key terms

As the term applies to technology, a “daisy chain” is an array of computers, devices, and peripherals connected one to the other, forming a linear sequence or chain.

An alternative pattern to any technology network is “hub and spoke”, where all computers, devices and peripherals are connected to a central computer or hub.

So, in terms of your data – especially your donor data – following the daisy chain paradigm means that you manage your data by copying and pasting data updates from one database to the next, from the most recently updated to the oldest, in order to keep your donor data stored and current in multiple systems.

An example

Here’s an example of the problem through the eyes of a recent client (I’m paraphrasing).

Most of our fundraising is coordinated through our CRM software. We manually sync CRM donor data with our email marketing system every time we launch a newsletter or a multi-channel fundraising appeal. Our major gifts team uses a different CRM that they feel meets their unique needs, and they sort of operate independently of our regular fundraising appeals. But they always check our data for leads. We also have an online event management system. And we are testing two new fundraising initiatives – one crowd funding, the other via texting. Did I mention that we still don’t know what to do with all the social media data we are gathering? Anyway, each time we launch an event, newsletter, or fundraising appeal, we take the donor data from the most recently used databases and update the others. We can spend days updating and cleaning data across each system before the data is ‘right’ and ready for use.

What this client is describing is the “daisy chain of bad data management.” Every system is supported by its own database, any one of which can be the most current on any given day or week. Keeping data current is like a daisy chain – moving data from one database to the next, to the next, and to the next again. For a few more insights, we talked about this problem recently with our good friends at Barker & Scott Consulting.

Does this sound like your organization? Does your digital marketing campaign or fundraising appeal always seem to suffer from data problems that you can’t seem to put your finger on? Well, perhaps it’s time to put a stop to bad data management, and explore options for solving the problem.

What to do

One reason data problems like the example above don’t get resolved is because we lack sufficient IT expertise to get the work done. And let’s face it, “data” lives on a fundraising fun-to-do spectrum somewhere between boring, scary and “I didn’t take statistics in college for a reason!”

Recognizing that reality, there are two solution paths:

  • creating a data management plan
  • building a central data repository
The data management plan

If you’ve read past TSL blogs, you know that we are evangelists for data management planning. Separate your data management processes from your fundraising activities. Be proactive about data management. Get out of the rut of reacting to bad data and the service problems that bad data creates.

But in the context of stopping the daisy chain of bad data management, what are we really talking about?

First, the problem does not apply only to your fundraising data. So you will need to identify each type of data utilized within your organization. For example, financial, outcomes, fundraising. NTEN has a great resource to help you think about your data strategically – I highly recommend it.

Now, you will repeat the next steps for each type.

  1. Identify all systems where you use and/or store data.
  2. Assess your systems. Which are the most robust and have the capacity to store the most data vs. which do you use most frequently?
  3. Prioritize the one or two systems that are most important to your business – we’ll call these your primary systems. All the others are secondary. In the case of financial data, hopefully you are only dependent upon a single primary system (accounting) and perhaps another that feeds it (fundraising). In the case of fundraising data, however, you may be wrestling with three, four even five different systems – remember our client story above! – and you will need to figure out which matter most.
  4. Create a plan for proper synchronization of your data, following a set of controlled, manual steps. You need to upload secondary system data (from an event system? intake spreadsheets? an email marketing system?) to primary ones only when the secondary system is used. Upload primary system data (your CRM? a donor management system?) to secondary ones on a regular schedule.
  5. Update your governance policies and create a data management schedule that addresses all steps, all data systems,  independent of your fundraising and communications schedules. (Be proactive, not reactive, about data management.)
  6. Factor in time for data hygiene – normalization, de-duplication, appends, etc.
  7. Assign a staff member responsibility for following the schedule and keeping data current.

Follow these steps and you are off to a great start. Even small organizations with limited budgets, time and resources can make a big difference if they commit to a basic data management plan. You can learn more about data management planning on our website, or feel free to contact us anytime.

The central data repository

Think of the central data repository (CDR) as the embodiment or institutionalization of the plan. It is a system – a data warehouse – that follows the same logic as we laid out in the data management plan. The CDR enables an organization to store large amounts of data in a database where it can be managed – normalized, cleaned, even analyzed – and then fed to the operational systems that need the data to do their jobs.

Download (TIFF, 143KB)

The CDR solution follows the hub and spoke paradigm that we mentioned earlier. All of your systems – primary and secondary – are like the spokes connecting to the CDR hub.

In a nonprofit organization, the CDR solution path usually makes the most sense for fundraising and communications data – i.e., donor data. Donor data can reside on so many systems, and it is changing constantly. The daisy chain of bad data management creates the most problems for a nonprofit when it describes your donor data. This is because data that is constantly changing is susceptible to so many data quality errors that are exacerbated by the daisy chain approach. Why can’t we keep it clean? (Different blog post!)

The pros and cons of a central data repository

The challenges for nonprofits with the CDR approach are lack of familiarity and cost. This solution is a new idea to most organizations. And it requires the development of a central database, as well as multiple data exchange scripts to move data between the CDR and your other systems.

The pros, however, often outweigh the costs. The costs are felt more short term, while the CDR is being built and tested. Once implemented, most of your data management between systems can be synchronized and automated. Data quality will improve, as will accessibility. And your various fundraising and communications systems will always have clean, accurate, and up to date data ready for your next appeal or newsletter.

Summing it up

I hope this overview has been helpful and gets you thinking. Disparate data systems are difficult to manage … and even harder to keep in sync. Start with a solid data management plan. That is the most effective way to immediately begin improving your data quality – and enjoying greater fundraising success!

Next, consider the CDR approach. Talk to your leadership and your board. Engage experts if needed. Investigate your budget as well as funding options.  Improving your infrastructure with a central data repository might be exactly what you need.

Either way, the most important thing is to stop the daisy chain of bad data management … before bad data management stops you.

Please let us know if Third Sector Labs can help!

About Gary Carr

Gary is the founder and president of Third Sector Labs. With more than 20 years of experience delivering software and data solutions to a wide variety of clients, Gary turned his attention to the overwhelming problem of data. Third Sector Labs is committed to making sense of data for the nonprofit industry.

Follow Us

Subscribe to Our Newsletter