Modern businesses operate in a data-driven environment. There’s a need to constantly access and process information and content that comes in from customers, suppliers, employees, potential business partners and other stakeholders.
Unfortunately, within this avalanche of data, lie a great many insights and opportunities that remain untapped, because some companies simply don’t have the human resources or tools to organise, understand and leverage all the data that’s available to them.
Automation to the rescue
Enterprise automation solutions such as robotic process automation (RPA) have emerged to help companies deal with large volumes of data, by providing tools to:
- Integrate and streamline multiple data repositories and content services to improve data accessibility
- Automate repetitive data processing tasks with greater speed to improve efficiency and protect data quality
- Remove the need for manual data entry and manipulation to cut the risk of human error and ramp up data accuracy
- Provide digital process logs that enhance data visibility, by tracking how, where and by whom data is used across the organisation – supporting compliance with data privacy and protection regulations
But there’s a catch
As mentioned in our first article in this series there’s one major stumbling block – conventional RPA solutions are best suited to managing structured data. This refers to any data that is formatted consistently and stored in a relational database or spreadsheet.
All the other data that flows into and around the business exists in multiple formats that do not conform to the same rules, making this content far more challenging to manage. This unstructured data includes everything from email and social media content, scanned documents and photos to videos, audio files and raw data generated by systems like IoT-enabled devices. And, according to research findings, unstructured data accounts for around 80-90% of all digital data!
Given the volume and variable nature of this content, many companies struggle to understand, organise and process it efficiently using the technologies they already have in place. It’s difficult to transfer this type of data into a traditional database, or search for and extract relevant information using automation tools alone.
Unfortunately, this often means that humans are given the arduous of formatting data before this can be used by RPA bots and other solutions. As a result, skilled employees frequently spend more time gathering, consolidating and cleaning datasets than working with the data itself to understand and extract its value.
This is not always the smartest way to employ human capital. It can also impact the quality of enterprise data. How can companies control data accuracy and consistency across departments if a substantial proportion of this data is collected and processed using manual means? No matter how conscientious employees are, there’s always a risk of ‘fat finger syndrome’ generating errors that can impact operational performance.
Unstructured data calls for an unconventional approach
All this considered, there is clearly a need for a new technology strategy – one that enables companies to decipher, organise and use all types of data more efficiently.
One such approach is intelligent automation, which combines the speed and accuracy of modern process automation systems with the cognitive power of readily available AI-enabled solutions.
Pulling AI capabilities into the process automation technology stack allows businesses to automatically translate unstructured data into structured data to increase the efficiency and accuracy of data-driven processes. Machine learning models can be employed to analyse and learn from that data, to provide new insights that can help businesses to serve customers better, keep employees happy and motivated, make smarter decisions and increase their profitability.
There are a range of technologies in the AI stable that can unlock more value from content. For example:
- Speech-to-text – these capabilities can be used to convert audio speech into searchable text
- Machine Learning – these models can be trained to recognise certain patterns and therefore identify, for example people, animals and other objects in an ocean of digital images. Natural language understanding and processing tools also play a valuable role, by enabling systems to ‘understand’ context and meaning, so that relevant data can be extracted from any type of content.
Putting intelligent automation to work
When an organisation is able to use AI to transform unstructured content into a structured dataset automatically and then feed this data into RPA-executed tasks – this creates endless opportunities for business optimisation.
A demo of automation in action – Watch Now
For example, companies that manage customer accounts are often inundated by emails from customers that contain a variety of requests. In some cases, customers may ask the company to update their address details or other personal and account-related information, while in other instances, customers may ask to suspend or cancel their accounts. Deciphering these variable instructions and extracting the data that needs to be processed can be a complex job – because, by nature, email content is unstructured.
Using the above example, here’s how intelligent automation could automate more steps in a process to allow this type of company to respond swiftly and accurately to these email-generated requests:
The Process Scenario
• The Customer Service team receives requests to update card status via Email
• The Digital worker processes email requests and downloads attachments
• The Digital worker Start ML model to process unstructured data
• The ML (Machine Learning) model converts data to a structured output
• The Digital worker processes structured data and updates card status on back office system
• The Digital worker send status email for review and exception analysis
Adding process orchestration and humans into the mix
Beyond managing unstructured data in a more efficient and sophisticated way, intelligent automation can also be leveraged to fix processes before RPA and AI are applied.
By integrating a modern digital process automation (DPA) platform into the technology setup, companies have the tools to refine or map new process steps and build custom digital forms for collecting data, if required. They can also design their own automated workflows that seamlessly route data and tasks between the various technology systems involved.
This type of technology can also be used to integrate people into workflows efficiently, by automatically assigning tasks and making the necessary information available. In many business processes, humans still need to be factored into the loop for exception handling or to review the information compiled by AI-augmented bots. A DPA platform can act as an intelligent automation engine, keeping data flowing between machine-learning algorithms and other AI-enabled tools, RPA platforms, existing enterprise systems and humans.
While AI and process automation are powerful technology categories on their own, they can be combined to provide exceptional business value and enable companies to completely rewrite their data management strategies.
Rather than batting to manage unstructured data, market-ready advanced technologies can be harnessed and incorporated into an intelligent automation framework that’s designed to meet specific content, process and business needs.