In the current business landscape, traditional enterprises are looking for agile technologies to accelerate growth and digital transformation. Many of these organizations have petabytes of data stored in enterprise resource planning (ERP) systems and while ERP platforms can provide an overview of “what happened,” many organizations want to know “what will happen” or “what’s the persona of my user?” or “how much inventory should we order for next quarter?”
There is no question that ERP systems are critical assets for the enterprise. Since Gartner first coined the term in 1990, these systems have blossomed, enabling organizations to support back and front-office functions from forecasting to inventory management and even customer relationship management. ERP systems allow a company to procure data in order to provide a myriad of critical analytics including sales forecasting, inventory management, and human resources reports.
And these systems are continuing to proliferate. According to Allied Market Research, the global cloud-based ERP market is projected to reach $32.18 billion by 2023.
That said, while ERP systems are strategic for entering, storing, and tracking data related to various business transactions, for years the C-Suite and business analysis teams have struggled to extract and transform data from ERP systems in order to utilize the data for AI and ML applications.
So, how can enterprises invigorate ERP systems to maximize their data output and provide more actionable leading indicators?
AI Micro-Products Middleware
These days, the market is starting to support the concept of AI micro-products or toolkits that can be used to connect to ERP systems through middleware. These middleware toolkits must have the ability to link to data both within the organizations from the ERP systems as well as CRM or HR platforms and external data (such as news or social media).
The middleware can then feed into the leading and AI platform in order to develop, select, and deploy ML models to provide highly accurate predictions and forecasting.
Utilizing a Wide Data Strategy to Maximize ERP Systems
Any AI strategy must leverage a variety of relevant data. Instead of “big data,” think “wide data,” which is the ability to collect, unify, and ultimately process a variety of relevant data: structured, unstructured, external, and internal data. This allows organizations to provide meaningful training data for ML applications like predictions, forecasting, and valuable leading indicator analytics for optimal decision-making.
While big data is used for analytics that can iterate what happened, wide data provides diverse data sets that become a catalyst for AI algorithms with more actionable recommendations and predictions. This enables the data to help ML applications learn correlations with the factors beyond an organization’s control or beyond the limited set of data that is often used in man-made decisions.
This is crucial because, in the globalized economy, business performance depends on many parameters.
Here is an example: Take two manufacturing plants designing products in different parts of the country. The geographic locations of these two plants will have an impact on production, especially if there are natural occurrences, like rain or snow. Taking into consideration weather and several other disparate external factors such as supply chain issues and inflation, combined with internal data to feed the AI algorithms, will result in more accurate predictions related to inventory and supply chain management.
Ultimately, wide data can kickstart an AI journey much more rapidly and is crucial for helping organizations to contextualize the insights from a variety of small and large, unstructured, and structured data sources. As technologies evolve and develop, there will be no enterprise that will be spared from data. Data strategies need to be built around obtaining and analyzing a variety of data and can be the key to invigorating ERP data.