Building a data-driven future: strategies for gaining organizational buy-in
Data science and generative AI offer significant gains in commercial real estate. Yet, valuable innovations don’t always translate to business advantages. Resistance to new approaches is common, particularly when current processes are deeply embedded.
While technical possibilities excite, implementing data-centric processes is a significant change management challenge. What is the key to unlocking value from data science and AI? Nurturing buy-in across organizations
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Here are six key steps to achieve this:
1. Build small wins
Start with use cases that make tangible differences, even if minor. For example, you could focus exclusively on tenant retention by analysing feedback and maintenance request data to address issues proactively. Or building predictive analytics to identify tenants at risk of non-renewal and implement targeted retention strategies.
Highlight successes to build trust.
2. Be outcome focused
Don’t create solutions in search of problems. Start with business-recognized issues or desired outcomes. For example, if operational costs are high, focus on management systems that monitor building performance, optimize energy usage, and predict maintenance needs.
3. Ensure buy-in from the top
Leadership sets the example. Make senior leaders power users of new tools. If tools don’t work for them, adapt or provide training. Remember Sun Tzu: “A leader leads by example, not by force.”
4. Emphasize the critical human factors
Position data science and AI as tools to augment, not replace human judgment. Emphasize that you are building decision support systems, not automating decisions.
Use data tools to enable teams to focus on where they offer the most leverage – to concentrate on what is unique, idiosyncratic, or unquantifiable about the assets or markets in play.
For example, in property valuation, machine learning models can process sales data and market trends, allowing professionals to focus on understanding the subtle nuances of individual assets.
5. Collaborate during the build phase
Create team outputs. Data science is too important to be left to the data scientists. Those with the best sense of smell should lead on the sniff tests as you build your models. So involve real estate traditionalists in model development.
For example, if building market and asset selection models, give future users a say in variable weighting. A widely used, slightly less accurate model can have a more significant impact than a perfect model that people avoid.
6. Deliver people-centric outputs
Meet users where they are. Enhance existing processes; don’t disrupt them. Integrate new tools into current workflows, whether that be lease management or investment analysis. Build user-friendly dashboards and intuitive visualizations to reduce barriers to adoption.
Final thoughts
Building a data-driven powerhouse in commercial real estate requires prioritizing people. This vision is human-centric: it enhances decision-making, augments capabilities, and enables professionals to focus on high-value areas. Secure buy-in to transform data science and AI into powerful allies in your quest for excellence.
A version of this article originally appeared in The Property Chronicle. To read other articles I have written for The Property Chronicle, please click here.