Fix the Inaccurate Data in Your Property Management System with the New Portfolio Exceptions Dashboards Feb 17, 2017 Bad property management system (PMS) data is an unfortunate reality for most property owners and managers. But you don’t have to settle for it. And in fact, you can actually turn the ship around with less effort than you may think. In our last couple of posts (found here and here), we’ve explored how inaccurate data happens in property management software (PMS) over the years. In this post, we’ll share how the latest release from Rentlytics can help you address and fix the problems in your PMS data once and for all. Why You Need Accurate Insights from Your PMS The data inside your property management system is the heart of your business. It’s the data you use to make important operational, financial and marketing decisions, and the data you use to report on the success and health of your business. However, for a majority of companies who have multiple stakeholders, manual data entry, data acquired through third parties and frequent business changes, this all-important PMS data is incomplete and full of mistakes. These mistakes can: Slow you down Lead to missed revenue Make it difficult to run analysis or make decisions Make it difficult to deliver a clear picture of performance to stakeholders How Bad Data Happens When too many people and departments are entering data into the PMS without the right training and guardrails, errors are bound to occur. (For example, someone might enter a lease date of 2117 instead of 2017 if the system uses an open text field instead of specific date options.) Errors are especially likely when you acquire a new property—and all the data that comes with it. And, of course, all of this is compounded when it’s been years and years of this bad data piling up. When you get to a place where so much of your data is error-prone, it becomes hard to even identify which data is the problem. Because we’ve heard from so many customers that these PMS data inaccuracies are a challenge, we’ve decided to tackle it head-on with a new set of dashboards that make it fast and easy to identify inaccurate—or missing—data in your PMS. Rentlytics Portfolio Exceptions Dashboards Make it Easy to Spot Errors in Your Data In our last post, we talked about some of the challenges of cleaning up bad data in your PMS, including manually combing through countless lines of spreadsheet data to find errors in the system. The newly released Lease Data Exceptions and Vacancy Exceptions dashboards solve these problems by exposing you to new opportunities for action, risks you might otherwise have missed and errors that are bringing down your averages. How it Works When clients log in to this set of dashboards, they see a series of charts, each highlighting an anomaly in the data. There are all kinds of situations where such anomalies can occur and where the new Portfolio Exceptions dashboards can help. Here are just a couple: You’ve got a tenant move-in date that’s just a week away, but no move-out date for the current tenant. You have a unit that’s a model and for-show, but that fact gets hidden and all you see is an empty apartment continually without a lease. With the at-a-glance insight you gain from Portfolio Exceptions, we hope to help Rentlytics customers: Focus their energy and attention on the most impactful actions Gain efficiencies through managing by exception Avoid the costly mistakes that occur when occupancy, vacancy and rent data is missing or inaccurate Make reporting fast and easy Make property management system (PMS) data more accurate If you’re interested in learning more about how Rentlytics can help you clean your property management system data, feel free to schedule a demo. We’d be happy to schedule an analysis to share the opportunities for optimization hidden in your data. Go Recent Post Customer Spotlight: South Oxford Management Introduction to Rentlytics 11 Multifamily Industry Trends to Look Out for in 2018 Subscribe Tell us your email address and we'll add you to the list. Subscribe If you are human, leave this field blank.