Your current data system(s) don’t do everything you need. Maybe your data is all over the place. Maybe everyone is complaining that the one you have is too hard to use, doesn’t create the reports you need, or has terrible customer support. Whatever the reason, you and your staff are spending way too much time battling a system that should be making your life easier instead of harder. Are you tired of spending hours on the phone with customer support? Fishing around for days just trying to get the right information? Answering 20 questions a day from people who can’t get the system to work for them? Spending hours cleaning the same data over and over in Excel?

We need a new data system. The stuff of dreams and nightmares. Whether you arent storing the right data in your system, or you cant get what you want out of it, you either have considered a new data system, or you were in the process of implementing one. So you got everyone on board, figured out what you needed out of a data system, did an inventory of your current systems, lined up the funding, and now its taking twice as long and costing twice as much as you planned.

Everybody Needs Style. Why You Need a Style Guide for Your Data Visualization. We are big fans of efficiency at Inciter. We like to make our lives easier, and we LOVE to make your lives easier. If you are doing any kind of data visualization, this post will save you time and a lot of annoyance. And who doesn’t love to improve their data visualization design? What is a Style Guide?

We need a new data system. The stuff of dreams and nightmares. Whether you arent storing the right data in your system, or you cant get what you want out of it, you either have considered a new data system, or you were in the process of implementing one. Decide how to spend your technology budget is always hard. And moving to a new system will inevitably involve a huge cost because migrating your existing data to a new system is very labor-intensive.

Microsoft’s Access has dominated custom solutions for managing data and prototyping databases since it was unveiled in 1992. While it has been the de facto custom database for many Microsoft customers for almost 3 decades, it has not aged well in the era of “the cloud.”

Cleaning, wrangling, munging, preparation, validation, ETL, transformation, ELT these are all different names for making data more useful. Whether youre doing one-off analysis, scheduled reports analytics, or integrating two different data systems or applications, when you work with data, data cleaning is an important part of almost all data processes. Data cleaning is taking data as-is and changing the content, structure or format to be more useful. Removing blank rows and deleting invalid addresses are examples of data cleaning.

Whether you are getting feedback from program participants, members, donors or other stakeholders, an online survey can be the easiest and most efficient way to do it. Of course, online surveys are not a one size fits all solution for collecting data to answer your burning questions, but they can be a cost effective method to gather certain data for your organization. No matter your field or audience, you have to follow some best practices if you want to make use of your data.

Nonprofits are in survival mode. You work every weekend, you give everything to your clients and leave nothing for yourself, you can’t give anymore and yet it’s not enough. Systems don’t work, the team is unhappy, and the community isn’t sure we’ll be there when they need us. There’s never enough time or money to fix it, so we just hold on until tomorrow.

You want data at your fingertips, when you need it, in the right format. Who doesnt? But often when you go to get reports, the data is siloed and you cant bring it together, or some of its missing, or its not accurate, or you dont know what to make of it. Maybe you think technology is the answer. Its understandable. Machine learning. Artificial intelligence. Big data. There is all this amazing technology out there that can make cleaning, analyzing and reporting on data so much easier.