Digital Transformation with Application Platforms

Analyst Viewpoint

Digital transformation is achieved by using digital technologies to create new (or modify existing) processes and methods to meet evolving business and market requirements. For finance departments the term is associated with, among other changes, using digital technologies to accelerate schedules, provide organizations with actionable insights, devise more meaningful performance assessments, and have the capacity to report and communicate from a broader palette of information. For IT departments, it means enabling internal clients to effect digital transformation in their department or business unit in a way that reduces IT maintenance chores and other “keep-the-lights-on” workloads so that personnel can spend more time on “build-the-business” initiatives.

Data management issues are often a root cause of roadblocks to digital transformation.

Digital transformation sounds like a great idea, but organizations wonder where to start and then struggle to make progress. Data management issues are often a root cause of roadblocks to digital transformation. For example, our Finance Analytics Benchmark Research revealed that 68% of organizations spend most of their time on data preparation tasks while only 28% are able to focus their attention on what they’re supposed to do: analysis.

Applications that incorporate flexible data platforms are a relatively recent innovation, supporting the digital transformation objectives of finance, IT and operating units. For example, finance departments are finding the need to access a wide range of data from multiple systems, many of which do not reside in their department’s systems. They need the information to create reports and communicate with both internal and external audiences. In addition to the department’s ERP, consolidation, planning and close management systems, they must access operational sources such as CRM, supply chain management and manufacturing systems. To an ever-greater extent, external data including foreign exchange rates, competitor pricing and industry data are needed for benchmarks, context and performance assessments. Environmental, social and governance (ESG) reporting is currently in its infancy and will require its own set of non-financial data sources to assess performance and address disclosure requirements.

Finance organizations often use spreadsheets to collect, refine, analyze and report data from multiple systems and sources. This approach consumes a great deal of time that could be spent more productively, and inevitably creates problems of data accuracy, consistency and timeliness that produces cries for a single version of the truth. Data warehouses—and more recently, data lakes—evolved to amalgamate data while addressing these data management issues by centralizing their collection and accessibility. But these data structures are designed by IT departments to address the needs of an entire organization and are often difficult for the finance teams to use on their own.

Rather than a using a warehouse or a lake, financial planning and analysis (FP&A) groups, accounting, compliance, risk and others in the department are turning to applications with a built-in data platform that connects the software with persistent links to a broad range of authoritative data sources from across the entire organization and even from external data feeds. The advantage of this approach is that the data needed by users of the system is immediately accessible and clearly labelled in terms understood by finance department users of that system.

Finding what’s needed in a warehouse involves time spent roaming endless aisles of things stacked high. For finance teams, boiling a lake of data for meaning is only slightly less daunting than boiling the ocean. Data warehouses and lakes are extremely useful for many use cases but do not provide finance teams with the independence and agility they need. The alternative, spreadsheets, enable them to operate independently and with the flexibility they need to perform a wide range of tasks, but they pose serious data quality issues. An application that offers a data platform achieves data quality, departmental self-sufficiency and ease-of-use objectives.

Incorporating a data platform in a specific application has become practical recently. One reason is the ongoing refinements to application programming interfaces (APIs) and other technologies that automate data movements and transformations from multiple source systems, whether on-premises or in the cloud. This includes low-code or no-code capabilities that put the power of APIs in the hands of the finance team. A second reason is that where people must be involved (for example, in making approvals), simpler process orchestration is possible using workflows. These eliminate the need for individuals to periodically download data from one or more systems and perform some analysis or transformation before reporting the result or uploading it into another system. Using APIs and other automation methods means the data used by the department is accurate, consistent and timely. These technologies require far less time to establish, expand and maintain connections between source systems and an application’s data store. As needs evolve or as the scope of the required data expands, a data platform is flexible enough to easily adapt to new demands. Moreover, since this type of business application focuses on specific set of users and use cases, making changes to the data sources or to its structure do not necessarily need to account for the impacts and requirements of diverse interests.

IT and finance departments should investigate how applications that have data platforms can serve their digital transformation requirements. They should actively explore opportunities to use this type of software to spend more time on higher-value, strategic efforts by spending less time on unproductive work.