Effective Data Visualization is a Journey
"The process behind data visualization is as important and potentially as valuable as the goal itself"
Data visualization as a tool for timely and informed decision-making is based on the premise that data is well-defined, trustworthy, and sufficient in volume and variety. To meet these objectives, technology leaders must work with business leadership to define the what, where, and why of mission-critical data. Organizations must face head-on the underlying challenges of data quality and standardization, data governance, and client confidentiality in addition to answering the primary questions of how this information will be delivered and consumed.
Our history of delivering data visualization solutions at Cushman & Wakefield can be viewed through two lenses. The first is looking at the visualization itself as a tool to empower and elevate business decision-making capabilities. The second is recognizing data as a core organization asset and facilitating the maturation of the processes and solutions through which this data flows. Our own journey followed seven core steps and principles. These helped drive an effort to create a production and revenue tracking visualization tool for our Valuation & Advisory group, successfully resulting in coordinated data strategy and improved processes across a service line with global reach and complexity.
Seven Steps in the Journey
1) Start with the end in mind.
What question is the business trying to answer?
Often, organizations begin “big data” initiatives by gathering information and then thinking about questions they want the data to answer. Start instead with the complex questions your clients want answered.
2) Define data and understand complexity.
Data is often contextual in delivery and opaque in nature. Challenge rule of thumb, and identify and clearly define data through business subject expertise. Deploy standards when possible, and create process to manage complexity. These practices ensure accurate visual representation, provide delivery efficiency by reducing the need for data manipulation, and offer significant residual benefits by creating a framework for architecting, processing, and managing core data.
3) Drive service and insights together.
IT and business working in isolation to deliver client service and insights won’t work. We must collaborate to address important client questions. Availability and collection of data must be coupled with a true understanding of our clients and their specific practices and interests.
4) Park intuition and experiential biases.
“In God we trust; all others must bring data.” W. Edwards Deming claimed. When business and technology leaders are constrained by prior experience, bias can overly influence analytics and create a barrier to new ideas. Or, leaders cannot objectively contemplate an analytical model because their opinions get in the way of fact.
5) Watch the pot.
There is a certain level of information the typical person can absorb in one visualization. Effective data visualizations tend to be simple and utilize only the most valuable data. Rather than overflow the pot, tell your story via multiple visualizations.
6) Drive client profitability.
The end game is not the data visualization itself. Our technology energy focuses on what drives client profitability. A more profitable client makes for a more favorable business relationship. That is our end game.
7) A picture is worth a thousand words.
The strongest data model and most compelling insight will fall flat if the visualization is not good. While we should not focus so much attention on fitting the question and data into a pretty sample visualization chart, we do need to get this right. A successful visualization can cultivate insight and foster deeper intelligence by exposing relationships beyond the traditional lens.
As real estate markets increase in complexity, helping our clients build their strategy and make decisions becomes much more imperative. We must help them leverage descriptive analytics to assess prior performance and learn from the past, while also leveraging new models that help drive decisions for the future. Information is key, as are the quality of data and the right delivery models. The reward is rich: creating value, identifying risks and presenting opportunities, and opening new possibilities for competitive advantage.