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Several years ago, I suggested to a client of mine that they needed to add a new position to their staff, a position I called the data records manager. What I meant by this was someone responsible for making sure all the data within the organization was being managed appropriately. Interestingly, since that time, the for-profit arena has seen the rise of a position called Chief Data Officer (CDO).

My client asked a very good question: “If we’ve gotten by for the past ten years without a data records manager, why do we need this position now?”

I answered the question of why in this blog post.

Click here to read the rest of the article.

Of course, if you’re already on my announcements list, you would have received notice of this new article right in your email box. Not signed up yet? Click here to sign up.

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I think instead of trying the limited options of free MailChimp service one needs to get premium from the day one if he is really damn serious to build an email list and earn money easily. OptinMonster is one of my tested email builder and its least irritating the visitor is the unique quality which a gem of blogger like Balkhi can develop.

Cado Magenge
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Many years ago I coined the Rule of 100 and 1,000, as it pertains to data conversions. (Click here to read about it.)

As it happens, a similar rule applies to ongoing data management. In a nutshell, if you’re managing under 100 records (e.g., small trade association with few members), you may not need to automate a lot of things, since manual processes for that size of a data set will work fine.

For example, I have a client who manages 47 chapters that pay monthly dues. Rather than going to a lot of trouble creating all kinds of set up and business rules within the primary database, my client manages these dues invoices directly in their financial management system.

Normally I would say “Manage everything in your AMS!” But sometimes, that just doesn’t make sense.

This rule can apply to any set of data smaller than 100 records. If the amount of effort to manage the records is greater in the AMS than it is/would be to manage them in, say, an Excel file, then it probably doesn’t make a lot of sense to force them into the AMS.

CAVEAT! There are greater considerations here, as well (e.g., are these smaller sets of data part of a larger set of data already in the database? Are we going to wind up managing redundant data all over the place?). So be very judicious about how you apply this rule. There may be times where the list is very small (e.g., board of directors) but the data overlaps with a lot of other data already managed in the AMS. In those cases, the rule does not apply. Committees should be managed in the AMS!

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I while back I recorded a webinar on database training called “Don’t Waste Another Dollar On Training: How To Ensure Your Training Dollars Are Spent For Ultimate Effectiveness.” This recording has been available for sale on my website for $49.

Because I believe so strongly in the need for good training, I’m making this recording FREE to anyone for the next 30 days.

Simply email me at wes@effectivedatabase.com, with the subject line of “Training Audio” and I’ll email you a link to download the recording.

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One of the more common requests I hear from my clients is that they’d like to be able to compare year-to-year membership data (e.g., current number of members vs. same time last year or same time three years ago).

What they often find, however, is that pulling historical data from their primary transactional database can be problematic. Data in transactional databases can change over time, which can affect historical queries and reports.

The best way to address this issue is to take regular snapshots of key data or reports. For example, many of my clients run a monthly report of members (including total count, count by category and company/names). These reports are then saved in a shared or network drive so that they can be accessed by anyone on staff. This way, when the association wants to compare today’s member counts to some time in the past, a query is run for the current counts and then compared to any of the snapshots that have been saved over time.

Transactional databases are good for transactions. They’re not as good at keeping historical data (the older the data the less accurate it will be). This is where snapshots can be a lifesaver.

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Every time I call my haircut place to make an appointment, I have the same experience. The person taking the call can’t hear me (because it’s so loud in the salon) and they have to ask me to repeat everything I say once or twice. It’s incredibly frustrating, like I’m talking to a three year old. I’d be willing to be they’ve never used a secret shopper there.

The concept of a “secret shopper” is simple: Someone poses as a customer and “shops” your organization in order to better understand the experience the customer is receiving.

Consider these questions:

  • Do you know what it’s like to call your association?
  • Do you know what it’s like to try to join, or register for an event, on your website?
  • Do you know how long it takes your staff to respond to inquiries?

These are all things that a secret shopper can help you determine. We all think our service is great and that the customer experience with our organization is wonderful. But there’s really no way to know that without experiencing it ourselves, first hand. That’s what a secret shopper can do for you.

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I often hear from my clients something along these lines: “We’d like that data but we don’t have it.”

I always respond with “Do you not have it because you’ve never collected it, or because you have no way to collect it and store it?” In other words, are you NOT doing something because you can’t do it, or because you don’t do it?

This is an important distinction, though often overlooked. And very often, when an association is not collecting certain data that staff thinks is valuable, the assumption often is that the data is not collected because the association is unable to collect it (can’t) rather than the data is not collected because no one is bothering to ask for the data (don’t).

“Can’t” means you need better technology. “Don’t” means you need different processes.

Understanding the difference between can’t and don’t will help determine how to address the issue.

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A smallish campaign with a homemade list would not be likely to yield much of a result. To achieve anything worthwhile, a much more aggressive effort is needed. Then, the age-old value analysis applies: projected earnings = margin on total projected sales – cost of campaign.
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Very fantastic and well-written post.Its extremely good and very helpful for me.Thanks for sharing this great post.

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On a range of database management issues. You can read it all here: http://bit.ly/1sMrmN0

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