The current pandemic has led many companies to rethink their strategies and consider a shift to deep automation absolutely necessary so as to achieve more with less. Deep automation encompasses the following processes and platforms: no-code development, artificial intelligence (AI) as a whole, data lifecycle management as well as process automation. It can be rather tricky to differentiate these terms so this article will give you a brief overview of what they all mean.
No-Code Development, the first step to Deep Automation
No code is often referred to as a platform enabling the democratisation of artificial intelligence. Indeed, it is a great opportunity for non-tech users to use AI and machine learning without necessarily being experts in the field. Yet, that does not imply that citizen developers will be able to build an app in a jiffy either. Creating a no-code platform still requires basic IT knowledge. It essentially enables small businesses with a limited budget to still build software apps without having to go through the entire traditional coding mechanisms. Some of the key characteristics of no-code platforms can be seen on the graph below. If you wish to learn more about the difference between no code and low-code platforms, this article is here to help you.
(Source: Maruti Techlabs)
Other advantages of no-code platforms include its rapidity as well as its ability to counter Shadow IT.
AI has become an essential part of our current world and many companies have decided to take a step forward and use it more, particularly since COVID-19 hit, causing some concern as to the rapidity at which AI is spreading. The efficiency of AI has been applauded by businesses in finance for instance which were emphatic in saying that AI solved significant industry issues. The study Thriving in an AI World conducted by KPMG revealed that up to 96% of finance business leaders were “confident in AI’s ability to detect fraud in the financial services industry”. To give you a clearer idea of what AI consists of, it is a subset of computer science which tries to replicate what humans can do, only faster, and in a smarter way. Within AI can be found machine learning and deep learning subsets that are both incredibly useful processes.
Equally interesting are the recent developments of AI which ensure your company’s durability and stability. Thanks to them, companies can now perform better by using their algorithms. Indeed, they structure the information a lot more efficiently, enable you to analyse your data to extract high value-added information (DocReader), to measure market yields with regards to your brands and products (Sentiment Analysis), to analyse your data histories (Time Series Analysis), and a lot more.
Data Lifecycle Management (DLM)
DLM is a subset of Data Management. Gartner defines Data Management as “the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.”
DLM is essential to Data Management’s progress. As a subset, it essentially collects data and transmits it. Let’s use an example to illustrate this notion. Imagine that a business sends you an email with data regarding a product they would like to buy, the name of their firm, the project they have, the date at which they would like to receive the product and their budget. Once that email received, you will then forward it to the sales representative who will use an app like HubSpot to store the data. In turn, the sales representative will write the data in an Excel spreadsheet and eventually send this to the manager who will also update the files.
Ultimately, the data you have received has gone from one person to the other. This is better known as data lineage. This helps give visibility to the entire process. It also makes it easier to trace mistakes back to their sources.
To put it simply, why is DLM an asset for your business? Here are a few benefits attached to DLM:
- Conformity: DLM makes sure that your company follows the law and that its data practices do not infringe it.
- Less data loss: DLM is behind data storage and data sharing. Thus, it can make sure that data loss is prevented by creating backup support files.
- Efficiency: DLM contributes to making data management much more effective.
Process Automation, the last part of Deep Automation
With deep automation comes the common fear of computer science eventually taking people’s jobs away when really, it enables employees to be more productive and to waste less time on repetitive and dull tasks. When it comes to Process Automation, two categories are especially important: Business Process Automation and Robotic Process Automation.
Let’s start with Business Process Automation (BPA). What does it consist of?
BPA refers to the automation of manual, draining and repetitive tasks. It consists of using technology to improve the efficiency of the company and to enable employees to focus on more interesting aspects of their work.
What about Robotic Process Automation (RPA)?
RPA’s goal, pretty much like that of BPA, is to automatise business processes. The way in which it differs from BPA is that, in order to automatise those processes, with RPA, your company can choose to “configure software or robots” to become even more performant, to create “automatic responses to emails” for instance or even to entirely “automate jobs” as the CIO think tank states.
Both BPA and RPA lower operational costs by increasing the time dedicated to tasks with high value-added. It is also beneficial for return on investment and provides great results.
2OS: A winning combination of no code and AI
2OS is a no-code platform which is a mixture of various technologies, including AI, Big Data, analytical tools, integration, etc. This whole process is behind 2OS’ success.
Regardless of the subject (financial AI, documents analysis, etc), 2OS will efficiently respond to your needs and solve your issues.
The 2OS platform also provides you algorithms with which you can analyse your documents and data so as to extract useful information with strong value-added (DocReader). You can also measure the market yields of your brands and products (Sentiment Analysis), and learn how to better communicate with your customers (Know Your Customer/Name Screening), etc.