Automated business process validation can significantly shorten timelines and ensure quality
In a recent blog we discussed the remarkable return on investment of automated business process validation (BPV), and one area where BPV can provide enormous competitive advantage is in accelerating big data projects. Automated BPV can help companies address the “three Vs” of big data: volume, variety and velocity.
At the best of times, big data projects are massive and challenging. In today’s environments CIOs must also simultaneously ensure that critical business processes are not disrupted as they implement these projects in already-complex enterprise application landscapes.
Automated BPV plays an important part in project success because it accelerates your enterprise application projects and helps ensure that your critical business processes are not disrupted as you deploy new technology.
The benefits of BPV are even more pronounced when it comes to big data than some other IT projects. This is because your company is likely to make thousands of business-impacting decisions daily based on real-time, in-the-field analytics generated by your big data projects. Ensuring that the reports and analysis used to make these decisions is accurate then becomes critical and has to be tested on a daily basis. This is impossible to do manually. In a recent meeting with Gartner, the analyst observed that people make more real-time business decisions with real time data – and more people use it. When the value of information goes up, so does the risk and cost of a software failure.
That’s why large enterprises are using automation to make sure that analytics are accurate — and are putting in place daily business process validation to ensure sound decision-making is based on this information. Let’s take a quick look at the example of a typical SAP HANA project, and how automated BPV helps:
Before: Get Prepared
Before you begin your SAP HANA implementation, you might undergo several upgrade projects to get to the versions that support SAP HANA. Upgrading requires affected business processes to be identified, documented, and tested through several iterations of the project.
Using automation to document and validate your business processes can cut upgrade project time and costs by half compared to a manual approach. The beauty is that business processes are captured once and the resulting automated tests are reusable across all subsequent implementation and maintenance projects, multiplying the benefits several-fold.
During: Accelerate Implementation
For existing SAP customers, as much as 80 percent of the work in SAP HANA projects involves testing, because it requires changes mostly to the underlying database. This means that test automation can yield even greater benefits for SAP HANA projects relative to other SAP projects.
Automated business process testing allows you to uncover and resolve data transfer problems and system issues before production outages occur and business users are affected. And it ensures all changes are reflected correctly between systems, and helps you validate BI data versus source data in your ERP system. It also allows you to verify that reports are functioning and accurate—every time.
After: Drive Ongoing Business Agility
SAP HANA is a still relatively new technology, so there are frequent updates coming from SAP. Every time a new component or update appears, each business process and data flow needs to be tested. Having a test automation suite in place means this continuous testing and retesting is easy and inexpensive. With simple modifications, you can reuse your automation library to support ongoing updates and future projects.
You’ll need to set up ongoing data transfers from your existing database to SAP HANA and ensure that all transferred business processes and reports function correctly. Regularly verifying business processes across all required systems and interfaces is critical to ensure you’re getting what you want from your SAP HANA investment—and ultimately getting the most from your efforts to leverage big data.