Today, we’re always looking for ways to make us work faster and smarter, and intelligent automation is one of the ways to do that.
We’re successfully using it to automate repetitive tasks, speed up workflows, and make better decisions that are based on current data. As businesses look to scale these automation solutions, a lot of them expect to be even more efficient and save even more money.
But, while the potential benefits are more than clear, scaling intelligent automation is not that easy. According to recent studies, a lot of automation projects fail to get to the benefits because of issues with data silos and fragmented systems.
So, how can your company push through that and fully realize all that intelligent automation has to offer?
We’ll jump right into that!
Understanding the Problem: The Impact of Data Silos and Fragmented Systems
The first step in handling any kind of problem is to understand it, so let’s make sure you’re covered in this area.
Data Silos
Data silos occur when different departments or teams store their data separately, which makes it hard for information to be shared across the company.
For example, one team might use one system to manage customer information, and the other team might use a totally different system for billing.
Because these systems don’t talk to each other, the data is stuck in separate places. Over time, particularly in large businesses, each department starts using their own tools and sometimes, the systems are old or they rely on manual processes that make it hard to share the data easily.
According to a report from Gartner, poor data management resulting from silos costs companies around 12.9 million (USD) every year. This just further emphasizes the crucial need to address data fragmentation.
If you want intelligent automation to work well, it has to have access to all the data from your company. When data is trapped in silos, intelligent automation (IA) can’t access the full picture and the automated processes end up being incomplete or useless.
Fragmented Legacy Systems
Fragmented legacy systems are another problem. These are your old systems and technologies that you’ve been using for a while now, but they don’t work well with modern automation tools.
These legacy systems might still play a big part in how your company works, but they weren’t built or made to handle what today’s automation technology needs or has to offer.
One of the main issues with legacy systems is that they can be expensive and hard to replace. A lot of companies don’t upgrade because it will take a lot of time and money to switch to a new system.
But unless they don’t, they won’t be able to integrate the old systems with new automation tools, which will slow down or block automation altogether (and you definitely want to avoid that).
Regardless of the expense, Gartner predicts that by 2025, over 50% of business-critical data will be created/processed outside traditional data centers or the Cloud. This will result in integration across systems even more challenging than it already is.
How to Overcome Data Silos and Fragmented Systems
Now that you know exactly what the issues are, let’s see how to handle them.
2 Ways of Breaking Down Data Silos
1. Process Mining
This is a powerful tool that helps companies map out their existing workflows and understand how information moves across systems. It analyzes data from different sources and spots where data silos exist and departments interact (or don’t).
One of the key process mining use cases is visualizing processes that are disconnected and allowing a company to see where their information is isolated.
2. Steps to Eliminate Data Silos
The first thing to do is to map out where all your company data is. When you know which system contains which data, you can pinpoint silos and see how automation can unify data flow. After you’ve done this, standardize data formats and create shared access.
Tools like data lakes and middleware allow for different systems to exchange information and all departments can access the data needed for automation. In the end, the idea is to implement a data governance strategy to keep data consistent and accessible to everyone and set up cross-functional teams to create rules on how to handle and share data.
This will support automation initiatives and improve the overall quality of the data.
3 Ways of Modernizing Fragmented Systems
1. Using Intelligent Document Processing
Intelligent Document Processing (IDP) converts unstructured data (your emails, PDFs, documents, etc.) into data that’s usable for automation.
This will help you integrate data from older systems and transform them into formats that modern automation tools can process.
2. Middleware Solutions
Middleware acts as a bridge between legacy systems and modern automation tools and allows data to flow between them.
This means that you can retain your existing infrastructure but still advance your automation efforts. Middleware can extract data from fragmented systems and feed it into automated workflows, and this way, there’s no need for immediate replacement.
3. Adopting a Phased Modernized Approach
If you want to modernize without causing disruptions, update your systems little by little. You can start by automating specific processes using new, scalable solutions while still keeping your critical legacy systems.
This hybrid approach will make your work more efficient and set the stage for when you need to completely replace your outdated systems.
Conclusion
You’re now well-equipped to handle everything scaling IA throws your way; there’s no problem that can’t be handled if you approach it strategically. Intelligent automation has a huge amount of potential, but you can’t use it to the max unless you’re prepared to make some changes.
This doesn’t mean that you need to replace all your systems and infrastructure overnight, but keep in mind that modernization is the key to progress.
