Data Health Tips for Donor Accounts

There is data that is clean, right where it is supposed to be and meaningful to all who view it.  There is also data that is dirty, maybe a prospects State is misspelled, or perhaps a list of potential communication preferences has multiple values that mean the same thing.  And then there is data that is just plain in the wrong place, and therefore meaningless. I work on implementations, helping non-profits move from their old CRM to our platform, StratusLIVE 365 CRM.

It is not surprising that donor databases get filled with some bad data, but what always surprises me is the lack of knowledge as to how it got so bad.  And, now that they see it, what are they going to do about it?  Identifying the cause is a significant 1st step in cleaning it up and moving forward.

There are many possible causes for bad data, but my initial thought goes to staff turnover at the non-profit as a leading contributor to the problem.  Losing staff is never easy, especially when they have been trained and know what to do.  And once they are gone, along with them goes their institutional knowledge.

Also contributing may be that there is no database manager overlooking what is going on with the database.  Some organizations do not think they are big enough or have not identified that the role is important enough.  They have let the database become a free for all, with users putting data where they know they can find it.  Maybe just not anyone else can find it, or extract it, or use it.  No rules will lead you directly to dirty data.

And finally, letting just anyone play in your database may be a culprit to having bad data in your database.  Maybe you don’t have a CRM system with robust security levels, or perhaps you don’t know how to configure whatever security levels you do have.  But having precise security roles to correspond to specific job functions is vital to having a clean, as well as a secure database. That way, users are accessing only where they need to go, and typically, that means they know what the data should look like. A win-win so to speak.

Not all is lost!  There are some simple steps that can be taken to help avoid, or at least lessen, the messy data in the database conundrum.  My first recommendation is to create SOPs – Standard Operating Procedures.  With SOP’s, everyone should be clear on how and where data is supposed to be entered.  It is a good idea to split this out by Department (finance versus marketing versus major giving and development, etc.), so each area has the rules that apply to them.  And if you assign one person in each Department with the task of monitoring and updating those procedures as needed, you won’t be left scratching your head when someone asks “why do we do it this way?”.  A bonus of this – you have moved your on-boarding procedures in a positive direction since these SOP’s can be (and should be) used when training new employees.

You should also consider identifying a person whose role is (or includes) database oversight.  Consistency in how users approach the database, which this role should enforce, will go a long way toward having a cleaner, and therefore more reliable, database.  This role would also help with decisions as to what fields should be required, which will also improve data consistency, and that in turn leads to data cleanliness.  Finally, this role can furthermore enforce the SOP’s and alert Departments when they are not being followed.

And lastly, create robust security roles.  This will help streamline who enters what data.  By limiting what users can do, but still letting them see the data so they can make the decisions they need to make, you can prevent unwanted erroneous mistakes with your data.

Making your database a reliable source of information so you, your colleagues, and board members can make sound decisions is a great idea!  Go ahead and start taking steps to get your data health up and then implement the processes to keep it that way.

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