With so much more data available, there is a growing appetite for reducing the time spent on the admin part of data gathering to using that time for analysis and interpretation.
Many organisations are looking to automate their processes. But where these types of projects often become difficult is to know where to automate, and at which part of the process to start.
Lots of commentary around data management is about Artificial Intelligence (AI), Bots and Machine Learning. So that might seem like a good place to start, surely?
Having behaved like a petulant teenager since being discovered in the 40’s and 50’s and only really starting to meet expectations in the noughties means the world is still finding its way with AI.
There are sci-fi-like stories that pop-up from time to time, like the one where Facebook had to shut down robots after they created their own language that humans couldn’t understand. And the one where Twitter taught a Microsoft Bot to be racist and sexist.
AI in whatever form looks likely to be the future and will continue to grow as it starts to mature and we understand the best ways to use it to help, and that is exciting.
I’m just not so sure that we should wholesale pass over to complete automation until it has grown up a bit, and for many organisations, it might be a bit too risky if they haven’t got their data in order to start with.
In that case, where should an organisation start?
From the Loaded Dice perspective, we look at automating the admin side of the process to remove the burden of collection, collating and reporting. For those collecting data through manual emails or other manual collection processes and using spreadsheets or semi-manual legacy databases, this is where the quick win to save resource can be found.
By removing the admin burden on teams that are manually collecting and collating data and automating this routine work, an environment of learning and investigating is created. Time is automatically freed and given back so teams can focus on discovering what is behind the data, instantly, and with a human touch.
And the best bit, really understanding the data an organisation holds by structuring it well means teams will start to see gaps and learn where AI and Machine Learning might help them to improve future data output.