Dee Houchen, Senior Director of ERP solutions at Oracle
The major trends we’re seeing in the ERP space are around embedding artificial intelligence (AI), IoT, chatbots and intelligent process automation into core business processes to reimagine the business. For example, AI and intelligent process automation in the context of financial close is leading finance teams to believe in the potential for an autonomous close. Chatbots and AI are enabling finance teams to delight employees with simple conversational user interfaces for expense reporting to reduce manual effort and improve accuracy.
At the same time, IoT and AI enables businesses to drive new revenue streams by feeding IoT data through usage meters to improve product insight and bring usage-based monetization models to market.
All of these trends support today’s most common business goals: increasing efficiency, freeing up valuable time, improving decision-making, and having a single source of truth for your data. Intelligent process automation automates route business tasks so that employees can focus energy on more strategic business priorities; chatbots streamline communications by providing instant responses to users’ most frequently asked questions; IoT provides a view of where and how your assets or products are operating so you can act on real-time data. But the transformational technology that underpins these emerging technologies is artificial intelligence, which will drive a new era of business productivity.
What should businesses be able to do with analytics today?
Today, organisations need to be able to access new data sources and determine what is valuable and what is noise. The ability to run personalised reports, fed by real-time analytics using pre-packaged data models should be par-for-the-course in 2020. Pervasive analytics help businesses unlock insight, improve decision-making, open new revenue streams and drive profitability. Eliminating data siloes and gaining the ability to integrate data across the line-of-business applications to deliver a holistic view of enterprise performance should be a key priority for CIOs.
However, current adoption doesn’t necessarily reflect the full range of possibilities. Companies on the leading edge are beginning to take advantage of “augmented analytics” – or analytics that incorporate machine learning. For instance, many advanced ERP systems now have embedded machine learning in the platform to not only analyse data, but actually drive smarter and more meaningful insights. Always on and working in the background, machine learning is continuously studying input data, increasing accuracy over time and allowing the system to unlock patterns, predict trends and provide unbiased recommendations.
What ERP analytics trends are we seeing right now?
After decades focusing on providing more dashboards to business users, leading organisations will distance themselves from the rest of the pack by driving the use of “Augmented Domain Analytics”
applications. These types of applications focus on the infusion of Artificial Intelligence into the Analytics process and enable companies to move from “Systems of Insights” to “Systems of Actions”. We will see analytics applications automate (or at least suggest) the execution of specific business process tasks based on insights and analytics. For instance, a finance employee might see his time to close radically reduced because the “system” will have triggered key actions based on its understanding of historical patterns. Such “Augmented Domain Analytics” application might recommend to an HR Manager that they enroll employees into specific training programs based on early attrition detection insights.
This type of capability cannot be improvised by traditional analytics vendors for it requires a deep
understanding of business process and the data models powering those insights. Traditional Business Intelligence vendors will be challenged by this new trend and it will move the field of analytics into its next evolution.
Why are organisations still struggling with real-time operations?
Many organisations still struggle with keeping data updated in real-time. There are a number of factors that cause this: Disjointed lines-of-business that are running on separate siloed systems; technology integrations that require re-implementation with each update, often taking so much effort that companies forego the re-integration process and let their systems operate in isolation; and financial consolidation associated with these disconnected systems.
Real-time operations can be hard if you don’t set your processes up for success. There are four key elements to do this:
A single integrated platform: It’s nearly impossible to have real-time data if your systems are siloed or ‘Frankenstein-ed’ together. That’s why a single, integrated platform is crucial to real-time operational success. Either choose a vendor with solutions for every line-of-business, so that each department can operate off one system, or select solutions that easily integrate with all other systems in your suite.
Software-as-a-service (SaaS): Running systems in the cloud is the easiest way to make sure that an organisation can operate in real-time. The benefits of SaaS are that systems are always up-to-date, have less downtime that could hinder your ability to run urgent reports, and demand fewer integrations with your other solutions.
Smart data: Analysis is only as good as the model and data it uses. Therefore, it’s critical that your data is not only unified across the organisation, but that it’s also clean and accurate. Think of data as nutrition – if you consume junk, a body will have low-quality output. The same goes for analytics and reporting.
Enterprise-grade AI: The use of artificial intelligence can greatly benefit your ability to operate in real-time, if it’s scalable and well-trained. A lot of businesses have the resources to hire data scientists to stand up a pilot, but balk at the thought of the long-term costs of building and maintaining AI projects at scale. The good news is that many vendors are now providing ready-to-go AI algorithms and supervisory controls to customers so they can implement and tweak AI without the significant investment in data scientists. These offerings are designed to serve the world’s largest organisations and provide an easy path to AI-powered insights that enable agility and real-time business visibility.
