I was talking with a COO a few days ago about the evolution of business intelligence and rental housing’s approach to data. We started talking about buzzwords like “big data,” and it reminded me of a great Harvard Business Review (HBR) article back in December 2013.
Written by Jeanne W. Ross, Cynthia M. Beath, and Anne Quaadgras, You May Not Need Big Data After All talked about how companies can “learn how lots of little data can inform everyday decision making.” This article has always resonated with me with respect to rental housing because “big data” applications typically involve massive datasets…terabytes or even petabytes of data.
The reality for us in rental housing is that we really have small data sets…megabytes or maybe gigabytes (actually, sometimes just kilobytes!) of data. That makes reviewing the lessons from this HBR article more important, and still relevant 8+ years after its publication.
Based on their studies of several companies, their premise is that most organizations are not equipped to truly leverage “big data,” but all are in a position to do a better job of leveraging data to improve performance—and those who displayed a culture of “evidence-based decision-making” outperformed those who didn’t.
Their research suggested that “companies with a culture of evidence-based decision making ensure that all decision-makers have performance data at their fingertips every day.” These companies also exhibited four key practices which we will explore in more detail below.
The lack of a single source of truth (SSOT) is probably the single biggest barrier to an analytically-driven decision culture. If you’ve ever experienced three different reports showing four different occupancy numbers, then you’ve experienced this. Even if the differences are small, the lack of consistent data undermines the validity of the reports and encourages people to continue to follow their intuition more than the data when making decisions. After all, if we can’t trust the underlying data, then we can at least trust ourselves and our experience.
Establishing an SSOT takes a very purposeful set of collaborative actions by both the technical and the business teams. As we’ve discussed in one of our white papers, data projects led by technology only rarely meet the business needs, and business teams alone rarely have the technical skills to create an SSOT—thus the imperative for collaboration between the two.
The authors point out that one of the best ways to give decision-makers near-real-time feedback is through the use of scorecards. I would add that, in the eight years since this article was published, the technology around scorecards, dashboards, and other visualization tools has made it easier to not only create and publish scorecards but also to back them up with detailed dashboards allowing decision-makers to dig into more detail on any given score.
It is important to design scorecards that provide information on key metrics that managers can control. Merely reporting high-level financial results may be informative but typically leave users at best scrambling for more data to understand what actions to take and, at worst, completely unsure of what to do about the metrics. Similarly, scorecards that present a massive number of metrics can be overwhelming which is why it is usually better to present fewer metrics that will drive behaviors.
Performance goals vary—by season, by year, when changes in economic conditions are detected, and a variety of other reasons (e.g. maturity in the fund cycle, changes in ownership goals, etc.). And the reality is that our businesses are run by dozens (even hundreds) of explicitly stated rules and hundreds (thousands?) of rules that are implied (or more dangerously, inferred).
The article’s third key point is that businesses with a culture of evidence-based decision-making are continually reviewing, updating, and communicating changes in the rules. They also ensure that the dashboards and reports reflect the data necessary to understand and implement these rules. They explicitly embed these into the reports which is one of the reasons that scorecards are such an integral part of this kind of BI platform.
The first three key practices entail varying levels of technology and strategic prowess. This fourth key practice is all about the human and tactical side of the business. The authors point out that providing data and reports is necessary, but not sufficient, to build the desired culture.
It’s not enough to tell people to make evidence-based decisions rather than intuitive ones. Leaders need to teach and coach associates to understand and embrace this new approach. Here are just a few tactics for doing so:
Changing the culture of an organization takes time. This isn’t a simple “roll it out and we’re done” program. In my 20+ years of implementing BI solutions in rental housing, I have found it’s best to start with a focused program around a key repetitive task (e.g. monthly reviews, renewal strategy decisions, or something else that is highly impactful). As participants get more comfortable and see the fruits of their labor, then you can expand the platform and business processes included. You know you’re successful when the proverbial “grapevine” works to your advantage and you start getting requests from other parts of the organization to participate!
Donald is CEO of Real Estate Business Analytics (REBA) and principal for D2 Demand Solutions, and industry consulting firm focused on business intelligence, pricing and revenue management, sales performance improvement and other topline processes