In the coming years, real estate agents will possess the ability to identify individuals planning to list their properties in a particular area, along with the expected pricing, thanks to the guidance provided by AI-driven insights products. Likewise, data will enable car dealers to discern the optimal vehicle and value-added products that will bring maximum satisfaction to a specific customer on their showroom floor, all while optimizing their profitability.

Unlocking the almost boundless value of data is manifesting in various sectors, such as real estate and automotive industries. This data-driven revolution is generating strategic and operational advantages, aiding businesses in enhancing sales across diverse domains like properties, vehicles, financial products, and virtually any consumer offering.

Over the past two decades, data has evolved into a crucial asset. Although not always easily commoditized and lacking “fungibility,” the strategic and operational significance of data is widely acknowledged. Global C-suite executives recognize that companies excelling in integrating data into their strategy, operations, and culture are surpassing their peers in terms of revenue growth and profitability, according to research.

The pivotal aspect lies in transforming data into actionable insights, as idle data holds little value. As British mathematician Clive Humby stated in 2006, “data is the new oil,” emphasizing its potential value after refinement, processing, and transformation into something useful.

In an increasingly complex and uncertain world, there is a growing demand for crystal-clear insights from data. These insights provide a more predictable roadmap through economic disruptions and political turmoil.

The strategic and operational value of data experiences exponential growth with an accumulation of historical data and diverse sources, whether externally acquired or internally generated by products and systems. The manner in which data is processed and transformed to unlock insights that positively impact the real world further contributes to its value.

In our context, the purpose of constructing data sets and extracting insights is to empower our clients with “superpowered” decision-making abilities. This, in turn, enables them to build a competitive advantage in their business using our products.

Creating Value from Data

The aggregation of customer and market data to gain a competitive edge is not a novel concept, although the process was once sluggish and confined in both scope and scale. However, despite its historical constraints, the value of data has grown over time. The accumulation of historical data provides a broader opportunity to identify trends and enhance interpretation by comparing datasets across different periods. For instance, evaluating deeds data at a specific date offers a limited view of the property market when compared to analyzing deeds data over our extensive historical property dataset.

The evolution of consumer technology has not only transformed how data is collected and stored but has also revolutionized the integration of diverse data sources. These sources, ranging from social media and GPS location to content choices and sales information, can now be seamlessly combined to generate profound insights into customer behavior that were previously unattainable.

Presently, AI and machine learning analyze an individual’s “digital exhaust,” revealing detailed information about interests, activities, and location. When thousands or millions of these “digital exhausts” are amalgamated, the strategic and operational value of the data experiences exponential growth.

In the realm of B2B, widely available generic data is losing its utility, but significant value can be appended to its usability by layering it with unique data. Some of this unique data is derived from proprietary products and systems, such as Lightstone’s Property Toolkit (the core delivery mechanism for our analytical products to property professionals), Lightstone’s EZVal (a home valuation workflow platform used by our banking clients), and Lightstone’s Signio (an online platform facilitating finance and insurance transactions in motor dealerships).

Ensuring the reliability, accuracy, completeness, relevance, and timeliness of data is imperative for effectively building algorithmic “superpowers.” Managing data is crucial, whether sourced externally or generated internally, and it is essential to develop tools that monitor the quality of these data sources and their impact on the decisions made by our clients when using our analytical products.

Harnessing the Potential of Predictive and Prescriptive Analytics

Data serves as a catalyst for enhancing operational efficiency and facilitating informed decision-making for businesses. Advanced AI processes and tools elevate user capabilities beyond merely understanding future outcomes (predictive analytics) to recommending the most optimal course of action (prescriptive analytics).

In the realm of “real-time” decision-making, machine learning technology plays a crucial role. For instance, a finance and insurance representative in the automotive industry must provide value-added products, such as extended motor warranties or roadside assistance, to a customer on the dealership floor during a transaction. Waiting for the algorithm to catch up with new information is not feasible in such scenarios.

The predictive and prescriptive technology is a result of integrating historical and real-time data to predict with mathematical accuracy the likely outcome of a given decision. In practical terms, this involves incorporating past value-added product sales data, real-time application-specific data, and integrating it with comprehensive vehicle and property databases, along with other usable data sources. All this information is used to train an algorithm that recommends the best course of action in real time to system users.

Ongoing pilot programs involve algorithms predicting not only what would be most profitable for the dealership but also what would enhance the end-consumer experience. The goal is not only to increase revenue during the vehicle sale but also to ensure customer satisfaction throughout their ownership journey.

In the property sector, Lightstone utilizes data and algorithms to predict homeowners likely to sell, enabling estate agents to focus on promising prospects. Combined with the Artificially Intelligent Automated Valuation Model (AiVM), Lightstone assists estate agents in determining an optimal listing price. The AiVM evaluates how listing a property 10% above or below the valuation model price affects its market duration, empowering agents to make informed decisions tailored to their client’s needs.

Lightstone’s initial analytical product, an automated valuation model of homes, evolved into the AiVM, incorporating artificial intelligence and machine learning for more accurate property valuations. This updated version utilizes new data sources, including satellite imagery, to algorithmically determine property values. The AiVM significantly reduces mortgage decision time, providing operational efficiency, and a substantial portion of these savings is passed on to the homebuyer.

Even in instances where a physical valuation is necessary, the valuer leverages Lightstone’s data and information for more precise valuations. This not only serves as a “superpower” for the bank but becomes a transformative force for the entire industry, enabling quicker and more accurate job completion for those utilizing Lightstone’s systems.

Pursuing a Competitive Edge

In today’s landscape, the routine gathering and utilization of data have become commonplace, and merely engaging in these activities is no longer sufficient to secure a competitive advantage. To fully realize the potential of what is often referred to as “the new oil” and truly attain a competitive edge, it is imperative to safeguard data, employ it judiciously, share it appropriately, and employ intelligent utilization to address real-world challenges and uncover opportunities.

The competitive value of data hinges on several key factors: the foundational integrity and utility of information, the integration of diverse sources of complementary data, the technology responsible for gathering and processing this data, and the caliber of the data science employed to generate insights and predictive algorithms.

This iterative process of discovering, refining, layering, and interpreting data is the catalyst for obtaining insights that, when acted upon effectively, have the potential to bestow transformative competitive advantages upon the industries in which we operate.