Innovation isn’t anything new to Experian, in fact we’ve been listed in Forbes Magazine’s Top 100 “World’s Most Innovative Companies” for the past four years. I must admit however to being slightly sceptical when the idea of introducing a “robot” into our team came up.
Let me explain further. As a Business Analyst in our data quality business, I use insight to explore new ways that we can embrace innovation to be more efficient and ultimately deliver a better experience for customers. So when a project came up to look at a more efficient way to support customers as they sign up for our products on a yearly basis, we began delving into the world of Robotic Process Automation (RPA).
I’m afraid that our robot isn’t quite the stereotypical vision you may have in mind, but the RPA technology we’ve implemented is very real and is evolving rapidly. To give you some context, our very own robot is essentially a way of automating the processes we wanted to improve. You might think that introducing a robot into your team would be a very technical (and potentially apocalypse risk inducing) exercise but I was surprised to find that my experience taught me much more about people, processes and data than robots themselves. Here’s some insight into what we learnt.
Lesson 1: Innovate to Innovate
RPA “bots” are relatively easy to configure and take away monotonous tasks without disruptive or invasive changes – great news for quickly realising ROI. However, to make a significant difference to your bottom line, the key is discovering what your human workforce can do with all that extra time.
It seemed ironic at first, but by adopting RPA I could give our human work-force time back to innovate further, focusing on human intensive roles the robot cannot undertake such as building relationships, meaningful customer contact and even more innovation. The value driven from these ensuing activities should certainly be the measure of success for any RPA implementation.
Lesson 2: People AND “Digital Workers” are the future
“The robots are taking our jobs!”. Get used to hearing this, typically in jest but with a definite undertone of sincerity. I quickly realised the need to bring our people on the journey of robotic automation. After all, the aim for us was never to replace humans. My brief was to put the customer at the centre – which tasks could we remove to enable our people to spend more time engaging with and supporting our customers?
I engaged the “humans” early on, involving them in process mapping and design, allowing them to impart their knowledge to the robot. We also ran a series of less technical (but more delicious) communications, involving a demo of a bot ordering a pizza. While light-hearted, our first “bot” is now accepted as an integral member of the team. From an operations perspective, this means the team are engaged and give me continual feedback on improvement ideas, as well as being receptive to effectively using their extra time.
Lesson 3: Fix it before you break it
RPA is great – it performs tasks at speed, consistently with zero error rates. In theory, this should improve your data quality assuming that what you told it to do was correct and the data it has available to work from was right in the first place.
I quickly learnt that the foundation of implementing RPA is going back to basics. You need solid processes and good data. Automating a broken process with bad data only multiplies the problem rapidly throughout your systems and databases, giving you more bad data and not doing the job you wanted it to do in the first place. Instead of efficiency, you’ll end up having to manually intervene to correct mistakes or do the job yourself by sidestepping the robot.
I’d recommend that the first step before leveraging any RPA tool is to assess the quality of your data and processes around it. Considering data governance as well as data quality will enable you to eliminate the problem of incorrect data being processed by your bots in the long run.
Get this right and the results will be evident. Get it wrong and you’ll have wasted time and effort, not just on the technical implementation but you’ll also risk disengaging your human workforce. Your colleagues and Board are only ever going to buy into innovation and change initiatives if they can see the results for themselves.
If innovation is on your radar I’d recommend going back to basics and considering your data quality needs as a first step. We’ve got a range of resources that can help you to assess and build a business case and implement on-going governance. Why not read more about our data quality solutions here or visit our resources page for advice and knowledge.