A recent Ernst and Young report found that 81% of organizations embrace the notion that data should be at the heart of all decision making. Yet, in many organization the decision-making process is stalled because data is still kept in silos.
With data locked in various silos in different formats and configurations, getting a big picture of what is happening throughout the organization can be challenging. This leads to a longer decision-making process and poorer outcomes.
The key to unlocking this siloed system is an automation solution that brings together data from disparate sources, allowing it to be consumed and analyzed more completely.
BPA vs. RPA for more accessible data
Automation can make data more accessible by taking on the tedious tasks of collecting it from multiple systems, transforming it into a common format, and checking it for errors. Without that intensive data optimization work, the data is more accessible to the hands-on users who are closest to the business case, and non-developers can easily build additional automations with no-code or low-code tools.
With the greater autonomy provided by automation, these users can create processes and workflows with incredible efficiency and quickly produce useful data-driven decisions.
When we talk about automation, people tend to think of RPA or robotic process automation tools. These can be very helpful for accessing data that is only accessible via a manual operation, but they are hard to scale. Stitching together multiple processes into a workflow that can consolidate, clean, and standardize data from different sources is also difficult with RPA tools. RPA is best suited for regular tasks that don’t change very often, such as migrating data from one system to another or automating the collection of data from static legacy systems.
Unlike RPA, business process automation (BPA) platforms are more scalable and flexible. Analysts and business leaders can build workflows and create rules to manipulate and consolidate data. BPA platforms also make it easy to build reports and dashboards that present trends and can be used to build decision-making frameworks that leverage calculations and rules.
Most BPA platforms have an integration engine that can dynamically pull data from multiple sources such as application programming interfaces (APIs), prebuilt integrations, and even direct database calls. This capability can provide a more complete picture of what is happening across an organization.
Typically, these integration processes are available out-of-the-box and can be leveraged by non-developers. If the appropriate integrations are not available, most platforms will provide a software developer toolkit (SDK) so developers can step in to create custom integrations.
Overcoming integration challenges
While integrating data from different sources can be very helpful in seeing the big picture, issues still can arise with redundant and inconsistent data residing in databases and applications across the enterprise. It requires slightly more work and time to normalize data by building rules that check for errors, transform formats, and resolve common discrepancies in the merging process.
Customer, product, and supplier data exists throughout an enterprise’s various systems, which means data discrepancies can occur. This challenge can be avoided by creating a single set of agreed-upon key reference data—accessible by all systems—that ensures critical data is consistent across the entire enterprise. Creating universal key reference data for the business eliminates conflicts caused by having the same data in different places and formats and goes a long way to improving data quality and usability.
To achieve this single source of truth, many organizations are beginning to leverage BPA platforms with built-in integration engines, business rules, and workflows for master data management (MDM). These robust integration capabilities and the ability to automate processes that make data more available within a single platform make BPA platforms a good choice for managing MDM.
A few risks, plenty of rewards
While these automation platforms can be very helpful in enabling greater access to consistent and clean data, there are risks to consider. The first is vendor lock-in: Processes built in one platform cannot easily be migrated to another. With this in mind, organizations should take their time with vendor selection.
Another important factor to keep in mind is governance. Providing more flexible and robust tools to more of your employees increases risks, as people with less experience are working with sensitive data and tools. More controls and checks need to be in place to ensure strong data security and oversight.
Despite the risks, the right data automation strategy is the best path to giving a business the biggest and most accurate picture of what’s happening across the organization and where the strongest challenges and opportunities lie.