Data makes the world go round. From an enterprise IT perspective, data is the fuel for most businesses’ decisions. With the right analytics, a company can take unstructured data and create something actionable out of it. Machine Learning (ML) and Artificial Intelligence (AI) agents are part of this process, but the real crux of the matter is the quality of the data. As brilliant as ML and AI agents get, they still can’t sift through data to figure out what works and what doesn’t. For a business to truly capitalize on these technological advances, it must have high-quality data sets. To get these data sets, it must look at integration programs for data management.
SAP is no longer a system sequestered from other data storage and analysis media. We’ve blown past the isolated years of SAP ECC and into a new future where SAP S/4 HANA is the standard – an always connected, cloud-ready server that can utilize data from multiple sources. Naturally, the move towards this sort of interconnectivity comes with considerations about data integration. There is more support for SAP-to-non-SAP integration than for SAP-to-SAP integrations at current. To define a data integration strategy that allows businesses access to the lion’s share of their data’s potential, these companies need to realize this simple fact. SAP systems can now integrate with non-SAP systems to provide value to both of them.
Integration Unlocks The Power of Aggregation and Insight
Integrating data allows businesses that have systems that a non-SAP native to offer their data to an SAP system for processing and analytics. A company with data collected from an independent source (like an IoT device, for example) can make that data available to SAP systems through these integrations. The data stored in the company’s data lake becomes more useful at this point since it can leverage SAP’s internal analytics engines and feed it the data, integrated to be read by SAP native systems. It’s important to remember that this doesn’t mean forcing a transition from one system to the next. Both methods can work in tandem and still provide valuable insights to the business. An API-led approach can collect and display this data in a view that can help decision-makers determine the best course of action for a business.
Capitalizing on ML and AI to Do More
With AI and ML, organizations have the unique opportunity to use these autonomous agents to develop new insights into data. As humans, we’re limited by our perspective, and we tend to focus on the trees and miss the forest. AI and ML agents given directives and trained to spot particular things can sift through the data lake and produce insights based on the available data. Additionally, the influx of integrated data can add necessary context for the AI agent, allowing it to fine-tune its view creation. However, an AI agent is only as intelligent as the rules used to define its output.
Including Stakeholders Into Integration Planning and Execution
It’s common to see businesses developing an integration strategy with a particular goal in mind. The aims of the company usually drive the adoption of the strategy. However, the underlying individuals that use the system are rarely ever consulted. Without looking at these stakeholders, a business risks having an integration strategy that only deals with one particular problem and may expend untold resources getting there. Stakeholders like on-the-ground users and IT personnel can offer valuable insight into implementation and acquisition that could save the business a lot of time and money.
Additionally, people are more inclined to use a system they had a hand in designing than one foisted onto them from management. Including people who’ll actually use the integration system in decisions can make for more efficient decision making. You could have the best CRM software in the world, but if your stakeholders refuse to use it, the integration strategy will simply fall apart.