How to avoid data overload

Charities that are data driven are likely to be more effective than charities that are not. That’s because they make decisions based on cold facts – as revealed by data – rather than habits, gut feelings, or whatever seems to be the easiest option.

 

But many charities are discovering that there is a serious challenge to overcome when adopting a data driven approach: how do you prevent your organisation becoming swamped by the sheer volume of data it collects? If your charity experiences data overload, then it will be unable to use the data it collects effectively. This can lead to decreased productivity, resources wasted on data collection and storage, and poor decision-making.

 

So how do you manage a charity which is data driven, while at the same time avoid becoming overloaded by your data?

 

 

Manage your data wisely

 

The obvious place to start is to cut down on the data that your charity collects and stores. With less data to trawl through, you are less likely to become overwhelmed by the volume of data you are handling.

 

But there’s a problem with this approach. The goal of data analytics is to draw actionable insights from your data, and often these insights are unexpected.

 

Analysis may reveal an important connection between two things that had previously seemed totally unrelated. That means that it’s desirable to have as a good amount of data at your disposal, since you can’t always predict which data may important.

 

There are two possible ways to square this circle.

 

The first is to cut down on the number of sources of data your charity uses by trying to weed out data that has not proved useful in the past and which you believe is unlikely to be useful in the future.

 

Although an effective way to help avoid data overload, it is also risky because you may eliminate a data source that may provide you with useful insights in the future – if you had not eliminated it.

 

The second is to continue to collect data from your existing data sources, but to be more efficient at managing it and deleting it as it becomes old. This is known as data lifecycle management. Put briefly, this involves classifying all of you your data sources and deciding how long data from each source remains useful before it is should be considered “out of date” and therefore deleted.

 

For example, an online survey may provide your charity with address data which you may wish to use to send out fundraising materials by post. But given that many people change their address regularly, you may decide that address data that is more than, say, three years old should be considered unreliable and therefore deleted.

 

 

Keep your data clean

 

Data cleaning is another type of data management, and it involves hunting down incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data from your databases. The next step is either correcting it, where possible, or else deleting it.

 

Using data that is incorrect or incomplete – dirty data - can lead to bad decisions. That means deleting such data can make your charity more effective while simultaneously reducing the data that you have to manage.

 

 

Use data visualisation tools

 

Cleaning data manually is a tedious and time consuming task, but there are many data management tools that your charity can use to make the task very much easier. Among the most important are data visualisation tools – these make it easy to understand data by looking at graphic representations of it. These range from relatively simple visualisation tools built in to Microsoft Excel to specialist data visualisation applications from companies like Tableau.

 

For example, using a suitable data visualisation tool it is easy to spot outliers in a data set. These are data points whose values are very different to many of the data points in a data set. It then becomes a simple matter of investigating these outliers to see if the data is likely to be correct, or if it is clearly incorrect and needs to be deleted.

 

 

Give your staff the right skills

 

Having too much data for your charity’s staff to handle may lead to data overload, but it’s worth considering that you can reduce the need to streamline your databases if you upskill your staff with the data-related skills they need to handle large volumes of data.

 

Possible approaches include providing training on how to use data visualisation tools in particular and data analysis tools in general and educating them on the importance of regular data cleaning and lifecycle management.

 

 

Don’t ignore the problem

 

The bottom line is that data is vital for driving your charity’s decision-making processes. But having too much data can be as bad as having too little.

 

So to ensure that your data-driven charity is as effective as it can be, it is vital that you consider some of the steps above to avoid becoming overwhelmed by your data.