This article will discuss the most common mistakes that occur when evaluating, implementing, or using mortgage business intelligence (MBI). Today’s article covers defining upfront requirements for all departments, as well as selecting the best industry specific platform for your needs.
Focusing On One Department, Instead of the Whole Enterprise
This comes up more often than one would expect. It’s typical for software initiatives, particularly in the mortgage industry, to be approached quite thoughtfully. Much care is typically given to polling each and every department for requirements before doing a vendor search. Extensive demonstrations and exhaustive evaluations generally follow to decide upon the best possible path forward.
When it comes to MBI, this process doesn’t always come into play. Given the wide array of systems that profess to fall under the category of MBI, this important initiative can manifest itself in a variety of different ways. A user in secondary marketing could appear with a free evaluation copy of a data visualizer that amounts to nothing more than an advanced graphing tool. Someone in operations might latch onto a data services provider for strategic benchmarking or peer group analysis. A compliance user might gravitate toward tools centered on customer surveys and complaint resolution.
To avoid getting stuck with a system that might later prove to be incomplete, give even the smaller, introductory offerings and above all their providers the same treatment that’s given to loan origination systems in terms of thoughtful analysis and evaluation. While some may be integral parts of larger platforms that can function at the enterprise level, many are ultimately limited in scope.
Using Generic BI for MBI
This is another unfortunate misstep that occurs more frequently than most would assume. I’ve seen firsthand a number of would be MBI systems exposed as general BI platforms, and this exposure generally doesn’t happen until lots of money, time and effort have been spent. There are quite a few popular BI systems on the market that offer a high level of versatility along with a low to moderate price tag. These are sold in the MBI marketspace by referencing one or more users that happen to be mortgage firms.
Sooner or later, the truth emerges, and most projects are abandoned and must be restarted with a more suitable system, if they’re restarted at all. To be clear: efforts such as sales directors leveraging a BI tool to help their loan officers target realtors based on a business maturity index derived from their license number to gauge their tenure alongside a host of other open source data is in fact a business intelligence exercise. But it’s not unique to the mortgage industry: collecting open source data for analysis to drive strategic alliances occurs across multiple industries.
What mortgage companies need is a platform that can scale to the level of their enterprise, and guide them through every aspect of the mortgage lending process with prebuilt solutions, business analysts and consultants with a high degree of mortgage lending experience, and above all, a large client base of other mortgage companies with whom they can exchange ideas.
Implementing without a data integrity plan
This is a perfect example of putting the cart before the horse, and the effects can be as aggravating as they are costly. With all of the money spent on mortgage technology year after year, little if any is directed toward data quality initiatives. There are still a large number of lenders whose data integrity efforts are limited to policing loan level data only, and this has left many with a lack of awareness and understanding of the databases that ultimately store and help manage that data.
While a good MBI provider can certainly come in and help you clean up your data, there are reasons why you don’t want to delay instituting a data integrity program. Data quality is a reflection of business process quality, and reengineering these processes takes time. Leaving these concerns on the table to be dealt with during an MBI implementation can bog the project down. Users won’t know if they can trust the data they’re seeing, and user adoption will suffer, even in the wake of a good analytics conversion effort. Help your MBI provider help you by taking the time to understand the state of your data and which contributing processes need attention before you begin your implementation.
Failing to account for remote users
Another common late breaking realization that often occurs sometime after an MBI rollout is that remote users can’t access the system, or that their user experience lacks functionality or performance compared to that of users on the network. This syndrome isn’t just limited to loan officers, as might be assumed. As time goes on, more and more loan participants are becoming at least part time field operatives, and people in management roles often find themselves in settings where they’re not connected to their corporate network.
Beyond gauging current remote access needs, it’s a good idea to make provisions for future eventualities as well. Ideally, you’ll have a wide variety of options to connect full or part time remote users with their data. Automated email delivery of dashboards, scorecards, and other data should be a standard feature of any MBI platform, and the better systems will make certain that each user only receives data that complies with their security privileges.
To ensure they’re keeping pace with best practices in terms of system architecture, look for MBI vendors who adopt a ‘mobile first’ philosophy, in which the system will adapt to whatever device a user happens to be using. This progressive approach entails beginning with the smartphone experience in mind, and retrofitting that design to more robust devices like tablets and eventually PCs, instead of trying to force views designed for PC screens onto a mobile platform.
Using a single data source
Like any technology undertaking, MBI projects can plateau, keeping the potential for advanced functions and practices unrealized. Even after groundbreaking achievements in efficiency and profitability, users can become complacent, and won’t get around to considering other areas where additional value can be realized. Once you’ve transformed the full spectrum of your operational dynamics, applied scorecards to your branches and individual originators, and implemented TRID monitoring, it’s time to consider data sources beyond your LOS production data.
Bringing in accounting data can set the stage for accurate loan level cost tracking. Blending in data from marketing or lead management platforms can dramatically increase the effectiveness of advertising campaigns as well as tighten up your lead handle time, leading to higher conversion rates and increased volume. Folding in customer survey data can reveal valuable opportunities to enhance service levels, leading to more repeat business and referrals. And combining data from secondary marketing can help managers maximize gain on sale by focusing on recreating the common elements of the most profitable transactions, along with shifting their product mix or applying pinpointed product training for the offerings that generally yield less.
Placing limits on usership
Another interesting development I’ve often seen crop up is the tendency of users to hoard their mortgage business intelligence. This territoriality has a number of potential causes. Most often, managers believe that staff level employees simply don’t need the data. They feel that having converted their own analytics and having much more time to consider and make effective decisions is all that they need to optimize production levels. In other cases, I’ve heard managers express the opinion that granting production staff access to another system will lower their performance levels by reducing the time they spend in their production systems.
In both of these cases, nothing could be further from the truth. Every MBI installation I’ve worked with clearly demonstrates that there is a direct relationship between MBI usership and profitability. Organizations with larger user bases always exhibit more efficiencies than those with just a handful of users. This reveals the true power of MBI, namely to induce staff to reflexively gravitate toward peak performance, and there are two reasons why this happens.
The first is the observer effect, otherwise known as the Hawthorne effect, in which people instinctively change their behavior by boosting their work levels in response to being continually measured, or observed. Even more impactful is the dramatic shift from task-oriented to goal-oriented workflow, which can only happen if employees are connected to their departmental goals through dashboards. Whether these metrics are on wall monitors in the operations division, on desktop PC monitors, or automatically delivered to individuals via email, connecting employees to their goals gives them the vision they need to prioritize their work. They’ll become more dynamic thinkers, they’ll be driven to hold more information in active memory, and will constantly look for ways to be more efficient.
About The Author