For businesses looking to establish a single system from which to drive everything, Enterprise Resource Planning (ERP) has long been the answer. As a natural evolution of business resource management processes, ERP systems became the bible of electronic records, incorporating data and resources in such a way that businesses could track, manage and plan their next moves reliably and repeatedly at enterprise-scale.
At the core of Enterprise Resource Planning sits a desire to better automate operations to increase efficiency. For some, ERPs represent a well-oiled machine — collating a record of all business processes and data sources to ensure operational decisions are executed in the best possible way.
As we move into an era that presents us with more data than ever before, ERP systems need a long overdue update to help businesses take the best actions. What’s important to remember is these vast quantities of data also come with a variety of contexts — you rarely have exactly the same information about your customers in Japan as you do in Mexico — and businesses increasingly need to account for a disorderly, unpredictable world. This is where decision intelligence comes in — technology which could, and should, replace traditional ERPs as the central cog of the enterprise tech stack.
Evolving decision-making landscapes
For the most part, analytics and reporting based on ERP work on the assumption that businesses know, roughly, what the data from the ERP means and how it can be used. Since its inception in the 1960s, ’70s, and ’80s, the ERP mindset has steadily marched across the operation, gradually standardising processes and unifying transaction capture for the processes they serve.
But this presents the problem. A new challenge now presents itself in the form of large and unruly data, driven by the exponential growth of data generation and availability. Available data has increased at a monumental pace, with over 50 times more data produced now than in 2010. We’ve now entered an era where ERPs are needing to play catch up.
One issue driven by this mass of new data is that it is disconnected and disparate. Existing ERP systems typically interpret and use data they create themselves, leading to a siloed departmental view instead of a holistic view that enables analysis across an organisation. Likewise, with the vast quantities of data on offer, it’s difficult for businesses to know which data to focus on. We know data-driven decision making is wrong, simply because all this extra data should have led to vastly improved organisational performance. Are companies 50 times more productive than 2010? Clearly not. Instead, we need to focus on the decision first, and therefore work out which data will be most useful.
Business processes once largely managed through ERPs are no longer as efficient or optimised as they were. With vast quantities of data now available, and taking into account the increasingly turbulent landscape businesses now operate in, change is needed. ERPs cannot offer the increased levels of decision making capacity, and greater understanding from data is required to make better decisions.
Decision intelligence uses AI to bring together various decision making techniques — alongside the modern data stack — to model, execute and fine-tune business decisions. Dubbed an intelligence layer, it does not seek to replace conventional ERP systems, but instead complement them, sitting alongside existing internal infrastructure. In short, decision intelligence tools add a new layer of meaning, helping business leaders draw in different contexts, operational landscapes and business-specific insights to make better decisions.
Despite decision intelligence complementing ERP systems, it is also an accompaniment to human decision making. Focusing on answering what is happening, why, and what could be done about it, decision intelligence makes predictions by encouraging business leaders to move beyond data towards a deeper, contextual understanding. It helps humans to access better insights and make more accurate, optimised decisions by observing, understanding, deciding and acting on data.
This is in contrast to traditional ERPs, which tend to operate by only ‘what they’ve got’, leaving the human to try and make sense of it using their best judgement (and more often than not, many, many dashboards). But with the complexity and scale brought on by masses of ‘new’ data, spotting patterns and looking ahead are nigh on impossible within the ERP paradigm. Decision intelligence, on the other hand, adds a new level of meaning to data, unlocking insights unavailable via ERPs or human comprehension alone.
Converging ERP and DI
As global challenges arise and business conditions become increasingly unpredictable, traditional ERP systems cannot address each challenge — or interpret every disparate data source — to make positive decisions at speed, and with precision. As a result, organisations need a new approach to their decision making, adopting processes that go beyond the qualities of the traditional ERP and augment them with decision intelligence.
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