Augmented Analytics

Data is growing rapidly. However, the question is: how can you extract this value from the data in order to benefit from it?

Danny Pieters

Business Development Manager

Augmented Analytics: the power is in the mix

Nowadays we all produce enormous amounts of data in many ways. Whether the data comes from sensors via Edge, portal systems and/or business applications or third parties via APIs, we can be sure of one thing: data is growing rapidly, is available everywhere, and is valuable to your organisation. However, the question is: how can you extract this value from the data in order to benefit from it?

Moving towards data-driven with the right questions

Before you start working with Augmented Analytics, the business case must be very clearly defined. The business case should create value. As an example, it should influence customer behaviour in order to align supply and demand, or it should improve the product range in order to increase your return.

Once the business case has been determined, we look at existing data together with the stakeholders such as management and business analysts, and we add more data if this is possible and necessary. We then determine which algorithms we can use, and we train them with the data (machine learning). We analyse these results, and we use and integrate them again in SAP, provided that the quality is good enough. This is an iterative process and ensures your organisation becomes more and more data-driven, step by step.

We combine four things in Augmented Analytics: data, machine learning, analytics and process integration. By using this mix intelligently and continuously, an organisation becomes much better at making predictions itself and intervening in business processes sooner. They do not need to employ a data scientist for this.

New insights thanks to Augmented Analytics

We have developed a framework to avoid having to reinvent the wheel. For this we use an agile way of working we developed based on our experience and an integrated approach to SAP applications: SAP HANA, SAP Analytics Cloud and Data Intelligence for data storage, integration with source systems, Python libraries, Workflow and Analytics tools.


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