Business Intelligence (BI) is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information to enable more effective strategic, tactical, and operational insights and decision-making. In multifamily, there is enough confusion about BI, that we think it's easier to think of it in terms of what it isn't:
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BI isn't a way to organize and access your data; it is information that drives your company's business
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BI isn't just another piece of software; it is the underlying data model that drives value in your organization
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BI isn't an IT-led initiative; it is a strategic, business-led initiative that is IT-enabled
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BI isn't a "spreadmart" of Excel sheets; it is a single source of truth for your enterprise
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BI isn't a resource that requires the operator to analyze data to figure out what to do; it is a fast, efficient way to deliver actionable insights that improve performance
Business intelligence (BI) is a journey. The companies who do it the best graduate from basic reporting to analysis to monitoring to predictive analytics, with each step delivering permanent improvements to business performance.
The journey typically starts with getting data under control and delivering reporting capabilities. Reporting tells us all about what has already happened. While reports are useful, the data they contain is static, and usually doesn't allow users to dig more deeply and view the data in different ways.
The backward-looking nature of reports further limits their value for forward-thinking decisions. Reporting is important (even necessary) but rarely do reports make it obvious what to do next. Reports, queries and search tools give us an excellent sense of current or past state, but little more.
Analysis is the next step on the BI ladder, allowing us to focus on why things happened, helping operators to make better decisions. With analysis, we enter the world of visualization and Online Analytical Processing (OLAP), connecting data elements and presenting them in a way that makes their relationships more obvious. The critical difference between reporting and analysis is the ability to efficiently explore the data and relationships rather than being limited to a rigid view of the information.
Monitoring takes us another level because it tells us exactly what is happening now. Monitoring allows us to identify issues, intervene and correct in near real-time rather than waiting for a report to tell us how badly we did. Dashboards, scorecards and alerts allow us to make decisions proactively to create good results and avoid suboptimal performance before it accumulates.
Predictive analytics is the "holy grail" of BI. Predictive analytics process the data to predict what might happen in the future. While not yet widespread in multifamily housing, some predictive analytics are already in the standard technology stack. For example, credit scoring applications predict likely bad debt. Pricing and revenue management systems predict optimal rents to balance occupancy and yield.
The value of predictive analytics is obvious: if we know something about the future, we have even more opportunity to take action, or at least to prepare for it as part of our decision-making process.
Let's be honest; multifamily has a strange way of acquiring and implementing business intelligence (BI) systems. Over our two decades of experience in delivering multifamily BI, we overwhelmingly see two missteps that compromise success: the wrong stewardship, and treating BI as a software project (which it isn't!)
Executives begin their BI journey with the best of intentions, but often make the mistake of letting the wrong stakeholders drive the BI bus. Many (in our experience most) BI projects are led by IT. The IT team identifies all the data needed to deliver a BI platform, usually by creating a census of existing reports, and proceeds to build a data warehouse to store all the data. They then build reports, usually recreating the reports that are already there and developing an internal portal to deliver them.
This may seem natural since BI is so technology-driven; however, our experience is that allowing BI to be driven predominantly by IT results in limited success at best, and abject failure at worst. Read our full white paper to learn more about why this is so.
When executives mistake BI for a piece of software, eg by buying Tableau, Power BI or the BI software package provided by their PMS vendor, they have not solved for BI. The software is a necessary tool, but it is just that: a tool. To do BI well, you need the right data model for your business and a good understanding of how to process source data into the data model. Your team needs keen insights into visualization best practices and a detailed change management plan to ensure business adoption. Simply purchasing a BI tool may feel like a solution, but the only thing you know for sure is the expense, which too often results in a disappointing return.
While lots of multifamily operators get BI wrong, many also get it right! Best-in-class multifamily practitioners understand that the key to a great BI platform is its data model. Here are some features of excellent BI platforms:
- Granular data modeling (no pre-aggregation) future proofs your BI platform by allowing you to calculate any future metric, even if the metric wasn't thought of at the time of initial design
- Dashboards and reports that 1) are based on real-world experience, 2) are built the way operators, asset managers and executives run their business; and 3) present the key performance indicators that make it easy to see what actions to take
- Flexible user interfaces: Rather than forcing users to match pre-determined visualizations to existing roles, they can create their own dashboards to match their roles, or even apply their own visualization tools of choice on top of the data model
- An extensible data structure that allows users to incorporate whatever data sources they can. Bendor-controlled integrations should not limit users' ability to extend the data platform to include the range of partners
- Direct access to data that allows experts to create ad hoc analyses, including creating proprietary metrics, through direct access to all dimensions and metrics
- Cloud access to data, ensuring that users have no need to maintain any of the "plumbing" and can access data anytime, anywhere on any device
- Staff for analytic success. If you think metaphorically of business associates as “blue” people and IT folks as “red” people, then the thing that you need is “purple” people—i.e., people who can span the boundary between the two. These people are out there, but they are not always easy to find. Their current companies value them, so they aren’t often looking for new jobs.
- The business should drive data analytics projects with IT in an enabling role. D2 recommends the well-established approaches (including a proprietary twist on the Parmenter approach for determining key performance and results indicators, along with the Kimball approach to data modeling) to help the business state their needs and learn more about how to “speak IT.” Implement an Agile-style approach that emphasizes short, iterative steps to get to the final state rather than long projects designed to deliver ambitious up-front requirements
- Once the business needs are determined, then trust IT to make the technology decisions. With the right goal now in mind, they can analyze the vast array of BI offerings out there and use their assessment and negotiation skills to choose the best suite of products for the best terms.
