4 Steps to Empower the (Compliant) AI Revolution with Automation
If your organisation has already implemented automation software and is experiencing issues with data compliance, handling, and processing, you’re not alone. Automation itself is not usually the problem; it’s the way the data has been integrated and set up.
Automation can result in problems for data compliance when disparate data systems don’t talk to each other. If you’re responsible for compliance tracking and reporting in your industry, these are the potential challenges to monitor and prepare for:
The Problem: Unexpected Errors & Data Management Issues
Detecting errors and data management oversights in your workflows is vital for ensuring customer and client satisfaction, SLA, and GRC management.
With automation solutions, you can receive warnings on potential errors or data management issues, including compliance issues. However, the issue that many businesses are facing, even ones with software solutions in place, is that the systems may not be providing a full organisation and system-wide viewpoint.
That means some issues may go undetected, which could lead to financial repercussions in the event of missing data. In fact, automation oversights in the past have been responsible for GRC issues for banks, resulting in fines.
If compliance officers don’t have access to the right information, then reports may be missing vital information. It’s the same for data scientists – without the right data, they won’t be able to accurately inform stakeholders and decision makers.
Why a centralised automation solution is the answer
As the cost of data has gone down, we’ve allowed it to grow and grow. The consequence is that we now have systems that aren’t connected to one another. There’s no common model between those systems.
So this is an old problem, and to add to the complexity, many organisations are now layering AI over the top.
Coming up with ways to bring all this data together is paramount for organisations to move forward and ensure that their systems and processes are data and SLA compliant. The end goal should be to match data sets from different systems. It’s also crucial to ensure that the right data is fed into the AI and automation models. You won’t want any bias or incorrect data entering the system.
It’s actually quite difficult for a lot of organisations to bring that data together, but there are automation solutions and integration specialists that can help through the following steps:
1. Migrating to a New System
If you’re moving from a mainframe or legacy system to a cloud-based system or in-house data warehouse, moving to a new system can be difficult.
A lot of the large banks moved from the mainframe to newer platforms, they had initiatives to start moving things around, and now they have AI layered over the top. It adds to the complexity of managing information and data pipelines and processing.
AI can help with the data and has been doing so for a long time already. It can support the processing of data pipelines, orchestrate workflows and make sure data is in the right place at the right time. This will also help your data scientists collect data for their machine learning models.
2. Running AI Pipelines
If your organisation is trying new ways of operating, inevitably, your strategies are also going to change. Being able to adapt to new strategies is important. A centralised automation platform can help with that. A centralised platform makes it easier to move data around. It’s also very easy to visualise where things are coming from.
If you want to stop focusing on one area of your business and prioritise another, you can quickly pivot to that and work across the entire organisation—even if you haven’t plugged in those data sources yet.
‘Having a centralised automation platform is a huge advantage. You want data to be as quick as you are with your changing circumstances to be able to pivot the organisation.’
Martin Hulbert | CTO at Ignite Technology
3. Empowering Tech Stack Changes
It can also help with the changing tech stacks. With a centralised platform, you can run data pipelines and change the technology stacks, regardless of whether it’s in a cloud system, legacy system or mainframe system.
Wherever our data pipelines are, we can visualise them. We can also run simulations to see what the impacts on SLAs are and how the changes will impact everything. It’s just a case of moving things around centrally, rather than having to redirect everything to make sure that data pipelines aren’t affected.
For SLA management, you want to make sure the data turns up on time and runs smoothly through the system. Another area is customer experience, and employee experience is around self-service. This enables your data scientists to have self-service, so they’re not worrying about logging lots of tickets. Seeing the whole end-to-end pipeline makes it easier to manage.
Centralised automation platforms can also run post-processing tasks, including scheduling and making sure everything lines up. You can condense task management and be selective with the data you’re bringing into the model to ensure efficiency.
4. AI Governance & Data Privacy
How do we control governance and data privacy? Once all the data is processed, make sure you have everything we need. It’s a brilliant way to align resources and bring everything together on one platform.
Fragmented scheduling processes make things incredibly difficult, especially if they’re time-driven. With a centralised platform, you can run tasks or systems one after the other, rather than having gaps between them.
Key Takeaways & Next Steps
Disparate systems are where the problem lies. Having too many SaaS applications working in isolation is a problem for organisations. It hinders their ability to be agile and adapt to changes.
Have a clear strategy for what you’re trying to do. What question or problem are you trying to solve with AI? Using automation ethically and sparingly is important for maintaining trust. Building guardrails to reduce risk is the next step forward for most organisations.
If you’d like any further information on how Ignite can support your Automaton and AI journey, please get in touch, or watch our recent webinar:
