21 Mar 2023
Eventually, these spreadsheets are used to drive decisions around asset management, portfolio management, reporting and valuations. These processes will then be transferred to other Excel spreadsheets with a reliable number of repeated errors that occur as a result of both manual transmission and the limitations of Excel itself in linking data and the inability of the program to include underlying property data. Consolidation and remediation are not its forte’ to say the least.
While Excel has continued to serve as the real estate darling for all things computational over the years, we need a better solution. Today’s marketplace is no longer the same as larger datasets, more complex calculations, and references to external data all form the core of new analysis platforms designed to aggregate and contextualize information across multiple sources and disciplines. Previously, Excel has been unable to provide the same level of multifaceted work within the context of meaningful accuracy and agility.
Today, new technologies, information, and data are being generated rapidly, requiring more sophisticated analysis using higher volumes of data. Simultaneously, the data and results are being pursued by multiple sources in a collaborative model. Excel, in its current form and by design, is no longer sufficient to provide the accuracy, speed, and connectivity required in today’s marketplace. Not to mention, machine learning and AI technologies have been an additional accelerant encouraging the use of proprietary platforms that are surpassing decades long reliance on Excel spreadsheets as a primary database or repository for information.
Moreover, in developing better and faster solutions to solve the needs in today’s industry,
John Macdonald, CEO Recognyte, a dynamic technology analytical platform designed for the modern world, has identified that approximately 25% of Excel sheets have errors. https://www.youtube.com/watch?v=5E2yHt7E4Lw#t=26m54s It’s also true that these type errors are precisely what can be avoided with a more appropriate and proper approach to handling and managing large datasets.
Pinpoint accuracy, connected IoT and the need to consolidate data quickly across disparate sources have all led to the obsolescence of Excel for larger and more modern information needs and processes. Structured databases are the professional answer now for what Excel simply cannot do. Specialist databases with flexibility and seamless shared data capabilities are the way of the future and are replacing spreadsheet reliance and other storage processes that have been anchored in Excel.
Stakeholders today need an analytical platform that is both sequential and parallel, leading to a single source of truth, or core for accurate visibility to analysis results. Very common Excel problems are inherently prohibitive to this process and instance.
To review, there are seven obvious problems, at a glance, where Excel lacks facility in its efficient use in the real estate industry. This includes the more obvious multi-user editing, shared workbooks, linked workbooks, data validation and data navigation, as well as security. The speed at which processes need to take place is also lacking.
In real estate, stakeholders today require transparency into their properties more than ever. Maximizing returns from yesterday’s methods and reliance on manually derived data from Excel is no match for today’s changing marketplace. For loan portfolios specifically, accurate data obtained quickly will prove to be invaluable for the speed at which the packaging of these loans for sale on both the primary and secondary markets can take place as investors shuffle quickly to retain value and maximize returns. Legacy systems cannot keep pace with the multifaceted and rapidly changing conditions in the market today, nothing is constant, at least not for the foreseeable future.
The post-pandemic world has proven to be a powerful motivator for data technologies that can exceed and outperform where Excel may fall short. Digitalization is rapidly occurring across the world. We may have seen this coming had we thought more circumspectly about the rise of the CIO, or Chief Information Officer. As higher volumes of data were being spun out and analyzed for discovery of new insights in future planning from a more informed perspective and with a more efficient process. It’s also true that AI needs to be undergirded with more advanced methods for computational processes. Today, proprietary analytical platforms based on modern computer languages provide the comprehensive and flexible features needed for analyzing larger and larger volumes of data with increasingly more sophisticated analysis needs.