Written by Kate Kupriienko
In today's data-driven world, the ambition to leverage insights for better decision-making is almost universal. Organizations, regardless of their size or maturity in data initiatives, are eager to harness the power of information. However, no matter how good your intentions are, significant data challenges can prevent you from reaching your strategic goals. We consistently hear from companies about the real-world impact of these issues, revealing common pain points that hinder progress and undermine potential.
Disconnected data systems
Imagine Company A, a large real estate investment trust (REIT), trying to calculate the true profitability of their downtown Class A office buildings. Their financial data, maintenance costs, leasing information, and daily operational details each reside in separate, unlinked systems. This forces analysts to manually extract, clean, and reconcile disparate spreadsheets, leading to a time-consuming and error-prone process.
The result is a struggle to generate accurate, timely reports and a lack of a single source of truth, making it incredibly difficult for them to make informed decisions about their property portfolio or identify underperforming assets. That's the reality for many, leading to inefficiencies and a constant struggle to reconcile conflicting information.
Lack of big-picture view
Despite having robust systems for finance, maintenance, and leasing, these disconnected tools create operational blind spots that impede the company's ability to understand overarching market trends, optimize portfolio performance, or identify emerging risks and opportunities across their entire real estate holdings.
This isn't just about having too much data; it's about data being segregated and isolated. The true power of data lies in its ability to connect the dots across an entire enterprise, revealing trends and relationships that individual systems can't. Without this holistic perspective, organizations are effectively operating with blind spots.
Limited staff
A common and understandable problem is simply not having enough people to do the necessary data work. With small teams or even individuals carrying heavy loads, the sheer volume and complexity of data tasks can be overwhelming.
However, the problem isn't just about having "more hands"; it's about needing specialized data scientists and engineers who possess the specific skills to bridge these isolated systems. Without a clear data strategy outlining how to integrate financial, operational, and leasing data into a unified view, even a larger team would struggle to generate meaningful insights. This often leads organizations to seek outside help to build up their data and analytics capabilities.
AI capability over-estimation
There's immense excitement surrounding the potential of Artificial Intelligence (AI) and Large Language Models (LLMs). However, this enthusiasm often precedes clear business goals or, more critically, reliable data. As Dmitry Orlovsky, Proxet’s technology architecture guru, stressed, putting messy or unreliable data into AI models will always lead to bad results. Without properly structured, categorized, and trustworthy data, along with a clear understanding of terms and definitions, AI initiatives remain stuck in the experimental phase. They might achieve small, isolated successes, but they fail to scale and provide real, company-wide value. The promise of AI is only as good as the data it's fed.
Performance bottlenecks
Even organizations that have invested significantly in upgrading their data platforms still face challenges with the speed and efficiency of their reports and analyses. This highlights an ongoing need for experts in data management and specialized teams. Upgrading infrastructure is only one piece of the puzzle. Without proper optimization, data governance, and continuous maintenance by skilled professionals, even the most advanced platforms can become bogged down, leading to slow query times, delayed insights, and frustrated users.
The journey to becoming a data-driven organization is fraught with challenges. However, by recognizing these common pitfalls — from disconnected systems and the crucial need for the right expertise to the critical need for clean data for AI and optimized performance — organizations can begin to develop more effective strategies. Understanding these hurdles is the first step toward overcoming them and transforming great intentions into impactful, data-powered realities, ensuring your efforts to leverage data are successful.