Addressing Data Management Challenges for Data Intense Processes in SAP Ecosystem

By Kumar Singh, Research Director, Automation & Analytics, SAPinsider

Integrated challenge of process and data management

A key challenge that organizations operating in complex technology ecosystems like SAP face is managing their business processes seamlessly and efficiently. From  a systems perspective, business processes within an ERP system span across several modules and hundreds of tables. And data is being generated every second for these business processes not only within SAP ERP but also across other point systems. Obviously, process automation seems to be an answer to manage this complexity but another key aspect or challenge that is associated with these business processes, specifically business processes that are more data intense, is data management.

It is not news for anyone that there is an explosion of data happening in companies of all sizes. As organizations scramble to implement enterprise and point systems, they are now generating significantly more data in their processes. And with this explosion of data comes many additional challenges. One of those challenges is data management. Data management encompasses all the processes that pertain to collecting, cataloging & organizing, and ensuring data access. And hence, it is obvious that in order to generate an optimal value from your data, you need to have a robust data management process in place, as well as tools and technologies that help you develop a modern, best of breed data management system. Of all the sub-processes that fall within data management, data collection is one of the most critical. Its critcality had grown in today’s fragmented and siloed data landscape within organizations, where data is continuously generated by disparate point systems, in addition to central ERP systems like SAP.

To get additional insights on this critical topic, SAPinsider recently invite an expert in the domain of SAP process and data automation, Andrew Hayden, Senior Product Marketing manager with Winshuttle, to share his perspectives on the challenges and nuances of process and data management automation in SAP landscape. You can watch the video of the conversation here:

https://masteringsap.com/winshuttle-data-management-video-q-a/

What are data intense processes and why are they important ?

A key question at this point may be, how do we characterize data intense processes ? In simple terms, a data intense process is one that generates critical data, discrepancies in which may may lead to issues. Both the volume and complexity of data are high for these processes, the interaction with these processes is fragmented across regions and geographies, these processes are ever evolving and last, but not the least, these processes fall under the purview of many regulatory standards.

While a key aspect that is focusing the limelight on this area is obviously the significant increase in amount of data that these data intense processes are generating, several other factors are coming into play as well. In SAPinsider State Of the Market (SOM) research published in February 2021, process automation and data management were among the top three areas organizations were looking to invest in. In addition to data explosion, this urgency is also driven by the way data is managed for these business processes. As Andrew Quotes: ” If you look at the way enterprises today are working, they are really trying to focus on digitizing as many processes as they can. And many companies still use manual processes to manage their data, whether they are managing it through forms or whether they are managing it through Excel files, or other manual ways.” Process complexity, data explosion and manual data management for data intensive processes, all of these are ingredients for creating inefficiencies and pain points. And those are precisely what many organizations in the SAP ecosystem are experiencing.

What process aspects are organizations looking to build ?

In SAPinsider Data and Analytics State Of the Market report published in April 2021, data integrity and quality emerged as a key pain point for respondents. Combine this with insights from our June 2021 process automation report, where process agility was highlighted as a key requirement, you will start developing a sense of what are key capabilities that organizations today want to develop in their processes. As Andrew highlights: “Companies are looking to improve their business processes in three ways. First, they want to build better process agility, and if you look at the pandemic, it is a great example of why they want to do that. Second, they want to have higher initial data quality. The third is, they want their processes to adhere to compliance and governance standards. “

Of all the aspects that Andrew has highlighted, the most critical one, is data quality. An acronym frequently used in the world of data and analytics is GIGO (Garbage in Garbage out). What this essentially stands for is that if your underlying data quality is bad, you will not get results from any best of the breed analytics or process intelligence tool. The quality of insights generated is directly proportional to the quality of the data that feeds into these tools. The unfortunate fact is, many organizations across industries still leverage manual touchpoints or processes to manage data for their data intense processes and the results can disastrously impact data quality. This was emphasized by Andrew as well in his quote: “Organization want higher initial data quality. When you have got manual processes, they are not only slow but they are also highly error prone. And being able to eliminate those manual processes and get the initial data quality higher, means a couple of things. One, your process becomes really more efficient since porocess and data are interweaved. Second, it costs so much more to fix bad data in the backend that getting that initial data quality improved is a significant help for the business.”

How can a solution help address these challenges ?

One of the challenges that many solutions in this area had was that they were eithr process management solution or a pure play data management automation solution. However, what organization needed was to have both of these capabilities in the same platform. As quoted previously, process and data are intertwined and hence the need to have both the capabilities mentioned in the same platform became prominent. This need has been answered by many solution providers in last few years however, many SAPinsiders have indicated that they find some of the tools too complex to use. This brings the trending topic of “self-service” into play. A consistent theme in most of SAPinsider research has been the imperative to build data driven organizations and a key strategy to attain that is to leverage tools that can be used by non-technical functional employees. This aspect has percolated in the area of process automation and data management tools as well. Organization are increasingly looking for no-code or low-code solutions that they can put into the hands of non-technical users.

What does this mean for SAPinsiders ?

Organizations using or planning to use data management automation solutions must:

Keep an eye on your data intensive processes. Identify your data intensive processes as consider them as processes you need to be very strategic about. As indicated earlier, a data intense process is one that generates critical data, discrepancies in which may may lead to issues. Both the volume and complexity of data are high for these processes, the interaction with these processes is fragmented across regions and geographies, these processes are ever evolving and last, but not the least, these processes fall under the purview of many regulatory standards.

Explore “Self-Service” options. Data and analytics democratization is the key to attaining success from your data and analytics initiative. Hence, it is imperative that you build a portfolio of tools that provide “simple solutions to complex problems”. The good news is that there are no-code and low-code process and data management automation platforms available in the market, that makes them easier to use by non-technical users.

Make solution sourcing processs strategic. Leverage RFIs and RFPs as strategic tools. Requests for information (RFI) and requests for proposals (RFP) are primarily used as formalities in technology procurement, whereas they could be leveraged as strategic technology procurement tools. Rather than using a template to build your RFI and RFP for automation technology procurement, customize it to get specific inputs for your customized automation technology procurement roadmap.

Measure your improvements. Before you embark, make sure you capture your current state effectively and measure your inaccuracies and inefficiencies. This pre-implementation evaluation will not only help you quantify the impact your new initiative has made but will also help you develop a methodology around consistent monitoring and control of key data movement and management workflows.