I have been using the term “data pantry” (somewhat tongue in cheek) to describe a curated, governed and readily accessible collection of enterprise data that business users can draw on to support the use of core business software. This includes ERP, CRM and supply chain management software, as well as essential business processes such as analytics, forecasting and planning. This form of data store is also a foundational element supporting artificial intelligence (AI) in training and maintaining AI models such as those for planning or anomaly detection, and for generative AI use cases.
People have wanted the fabled single version of the truth in computing systems practically forever, and the data pantry fits that bill. Because finance and accounting organizations rely
A pantry is an approach to data fabric concepts where the core design objective is to easily enable finance and business teams to consume data, in contrast to the aggregated technical byproduct of systems that characterize a data lake or data warehouse. For one, its semantic layer is intentionally designed for the specific users and use cases relevant to the business software platform, which is especially useful in limiting ambiguity as natural language processing is employed. Data labels conform to the terms and terminology of users to promote comprehension and precision.
Saying “data pantry” is a way of demystifying and simplifying a complex set of technologies to stress the importance of having a business-first view of data. In the context of a business software platform, this approach is preferable to data lakes, which are technically rich but hard for business users to navigate. You usually don’t want or need to boil the ocean to get a simple answer to a simple question. And rigid, pre-modeled data warehouses are slow to adapt to dynamic business requirements. There are multiple approaches possible in data architecture, and the “best” is the one that provides the greatest utility for the tasks at hand.
A data pantry may use data lakes and warehouses as information sources, but functionally, it sits between the two by being contextually canonical yet flexible in its application. Like a kitchen pantry, it contains all the necessary ingredients, labeled for easy access and comprehension. Data can be trusted and governed, easy to find and reuse, and suitable for multiple purposes. Curated, business-ready data is standardized, reconciled and enriched programmatically so that finance, operations and analytics teams can use it directly, without repeated data preparation. The key benefits of a purpose-built unified data store include:
Depending on the intended users of the application, the data pantry is designed and organized around the relevant business domain, such as finance, sales, marketing and manufacturing, rather than around source systems or technical schemas. A governed approach means that controls exist for data quality, lineage, access, security and definitions. Using existing controls in the application, access is flexible enough to enable self-service analytics, planning and AI-assisted analyses.
The data pantry is flexible enough to be reusable across multiple use cases on a given platform. For example, the same data set supports close and consolidation, management reporting, FP&A, operational analytics and AI models, all but eliminating duplication and the need to reconcile data from multiple sources. It is more precise and therefore more useful in promoting AI-readiness, with reliable, well-labeled data needed for trustworthy AI and agents. Business software providers are in an arms race to build out predictive AI, generative AI and agents in applications. A platform-specific data store is necessary to enable these technologies. Machine learning and predictive analytics require timely, accurate and relevant data to support continuous testing and ongoing training of models.
Technology can improve and ensure the quality of data used throughout finance and accounting. In particular, the right technology can ensure accuracy, timeliness and completeness of data while enhancing the productivity of the entire department. Technology is at the forefront of reducing a significant part of the department’s workload currently spent on repetitive tasks and mechanical processes, allowing staff to focus on the more valuable work that requires expertise, experience and judgement.
I recommend that buyers of business software focus on how well the provider has facilitated the availability of data for users of that application, not just on transactional functionality. This will enable them to more readily adopt a fast-follower approach to AI and agents. This approach is now a necessity because software designed for business, especially in finance and accounting, will evolve rapidly over this decade. It will be the fast followers who will have a very real advantage in utilizing AI technology, because they will be able to quickly take advantage of advances as they become available. Those that pursue a wait-and-see strategy—and there will be many—will fall behind and underperform their peers.
Regards,
Robert Kugel