What a technology-based solution for data management looks like
There’s good news: A holistic, well-designed and effectively implemented technology solution and system architecture can dramatically improve the effort level, quality and cycle time of any finance team’s data management work. It starts with a commitment from the top to standardize policies and processes where possible. And it includes an organizational commitment to a formal continuous improvement program.
Since finance departments are so data-heavy, their organizational structure often reflects these data management needs. Team members with strong Excel skills are prevalent. They often become the stars, become critical and hold a lot of leverage which can create employee flight risk and drive up labor costs. Many accounts receivable departments are organized by customers because each customer provides unique data structures and challenges. It is not uncommon to see very elaborate spreadsheets built by an individual teammate over many years to deal with unique customer data. These scenarios are not efficient, scalable and are often risky.
There is a preferred order to an impactful data management technology solution:
First, integrate systems where possible to enable the native movement of data. Systems can be integrated through custom coding or prewired solutions such as API’s (Application Programming Interface), web-services or third-party software. Custom interfaces can be cumbersome, fraught with error and difficult to maintain. The mappings are custom alphanumeric data strings sometimes thousands of characters long that won’t work if not perfect. Pre-configured solutions should be considered first.
Next, ETL (Extract, Transform, Load) technology should be considered to map data wherever there are static source and target data files. This technology can also house very detailed, custom data validation rules designed to achieve maximum data integrity.
Then, fully use your ERP (Enterprise Resource Planning) system’s business rules and workflow functionality to handle how data is automatically reviewed for completeness and quality before moving to the next role/user in the process.
Additionally, where double input of data is involved, it may make sense to employ self-serve functionality to eliminate the second input. Supplier invoices are a good example.
Suppliers first input data to create an invoice. They then lock that data in the form of a PDF, send to an accounts payable department that then must reinput the same data. An alternative approach: Have the supplier enter the invoice data into a secure supplier portal and eliminate the need for the second input.
With the proper technology architecture in place, a significant amount of data movement and management should be touch-free. However, it’s inevitable that finance personnel will need to navigate from system to system to grab, request, validate, and input or upload data. A good process automation strategy, complete with high-impact use cases and a suite of automation technology tools centered around RPA (i.e., Robotics Process Automation) can help automate another significant group of data management tasks.
Supplier invoice input, customer invoice creation, and customer receipt applications are common areas that can achieve high levels of data automation but require an automation suite to supplement a proper architecture.
Performance KPI’s, dashboards and a dedicated continuous improvement team are critical to achieving maximized results.