Three Pillars for Successfully Commercializing Data Use
Given the market challenges so far in terms of establishing successful revenue streams to offset the expenses of providing connected car technology, OEMs are still hunting for a business model that will work—for customers, partners, and the OEMs themselves. Trends in the automotive industry point toward always-on data capture and distribution over the lifespan of a vehicle. Tesla has successfully integrated this approach into their brand, strengthening their tie to customers by focusing on personalized experiences. Over years, the company accumulates a wealth of data about each vehicle’s operational history—from both a vehicle and driver behavior perspective.
The value of the services and intelligence that can be derived from this data is the key. Data plus analytics yields intelligence and that intelligence has tremendous value across many markets. The considerable challenges of profiting from connected car subscription services has troubled the industry since these services were first introduced. OEMs, however, can monetize the data collected in a variety of innovative ways—as long as they manage certain essential considerations well. The value of deep insights about the drivers of their vehicles is a bonus beyond the basic monetization of the data.
The data commercialization challenges faced by automakers typically involve three major pillars, all of which must be resolved as a part of an effective business model for providing connected car services.
Pillar 1: Data Management: Integrity, Security, and Privacy
Building a business model that involves the capture, validation, provisioning, and distribution of massive volumes of vehicle and driver information requires considerable expertise and a supporting infrastructure. Under the umbrella of data management, several specific areas of data handling must be addressed to meet the concerns of OEMs.
Every byte of data about a vehicle and the driver operating that vehicle must be subject to full transparency and consent. The driver must be consistently informed of what data is being collected, how it will be used, how long it will be stored, who else will have access to it, and what prerogative does the driver or vehicle owner have for terminating consent given for the use of this data. The most effective Big Data applications associated with automotive use
assume an always-on model that can capture vehicle data throughout its cradle-to-grave lifecycle, whether it is in motion or parked, and includes a very wide range of parameters. The vehicle operator must give consent for the collection and use of every one of these parameters—from vehicle location to data capture of driver operations (speed, braking, the G-force of turns, and so on) to identifying the person behind the wheel at any given moment.
Different jurisdictions around the world have varying levels of regulations and mandates in terms of an individual’s data privacy. These laws and mandates can be complex and in some case overlap depending on the region and even the state. Data privacy is important both for legal and regulatory measures with which the OEM and any partners must comply, in addition to the basic business principle of respecting and protecting all information that relates to their customers.
Failure to pay attention to data privacy could subject the OEM to fines or loss of their stature in the industry, as well as casting a dark shadow over the way that they are perceived by customers. When working through a service or intermediary that is handling data on behalf of an OEM, the OEM must ensure that strong data privacy protections are included in any agreement and followed consistently.
Ensuring secure data exchanges is integral to any data commercialization effort. Part of this involves monitoring and tracking where data is being sent, where it originates, and whether encryption is used consistently to secure data while in transit. Any areas in the data path that potentially allow intrusions or are vectors for abuse should be remediated to mitigate the risk. Risk has many different dimensions and security should always be implemented anywhere that a risk potential is identified. With rigorous, secure data protection mechanisms in place, hacking becomes a non-issue, rather than a concern for OEMs.
Required Vehicle Equipment
Many different mechanisms exist for capturing and transmitting vehicle and driver data. The hardware and software supporting this effort should be factored into the plan for commercializing data. The range of hardware and vehicle types supported introduces a layer of complexity. Of critical importance to any commercialization program is the capability of collecting, cleansing, normalizing, and unifying collected data so that irrespective of the OEM’s hardware decision, the vehicle and driving information is delivered to each beneficiary in a uniform, usable format. Commercialization demands that the management of data across this spectrum of devices in a consistent, verifiable manner. To maximize the data value, this management should include the earliest devices in the market, all those operating today, and new devices as they are introduced (always maintaining backward compatibility).
Pillar 2: Sufficiency of the Data
For Big Data applications in the automotive sector to be successful, certain criteria must be met. Simply collecting and aggregating huge amounts of data, without being sufficiently selective or qualifying the nature of the data, is unlikely to lead to the kinds of useful insights that will identify patterns, reveal trends, and derive statistically significant results.
Going into a project involving automotive data, key questions should be resolved at the earliest stages of planning such as:
- Considering the full range of data being captured, what data points are most relevant?
- How much data is needed to drive the analytics and provide value?
- Over what time period should data be collected to ensure that the analytic results can be trusted with a high degree of confidence?
Partnering with an organization experienced at the collection and processing of vehicle and driving data can answer these questions and provide assurance that you will get results from the data consistent with the project objectives.
Pillar 3: Uses of the Data
At the point where the mechanisms are in place to capture the data, permissions have been granted for acceptable uses, the question arises: what can you do with this data? This is the area where innovation, imagination, and cross-industry cooperation converge—the point where new opportunities emerge and fresh services can be developed. At this stage, the value of the data depends on the kind of usage and the area of interest. For example, city and state government organizations have an abiding interest in traffic control and safe vehicle road use. Emerging applications that track a vehicle’s movements over specific roads or turnpikes can institute fair-use road charges to drivers traveling these roadways—based on actual data. Other data from vehicle locations can help reduce traffic congestion by intelligently manipulating traffic signals and automating passage through tollbooths.
Clearly, insurance companies are very interested in data revealing driver behavior patterns, vehicle safety features, vehicle maintenance, and driver lifestyle. This data has considerable value to them and arranging a data exchange will benefit the OEM, the insured party, and the insurer.
The OEM ecosystem also benefits from this data. Automotive service facilities with access to diagnostic and status data associated with a vehicle can use predictive analysis, incident logs, and historical data from similar vehicles to communicate with drivers about issues that require quick intervention or near-term attention. Other applications include understanding the driver’s habits, preferences, and lifestyle to better match the vehicle’s features, maintenance, and service programs to the owner’s requirements.
Emerging IoT Applications
Beyond uses envisioned for government organizations, vehicle service centers, and insurance companies, numerous opportunities exist—many still in the infancy stages of planning—for data that indicates consumer preferences, lifestyle matters, and daily routines. These opportunities often require a convergence of information from different sources, as is typical of emerging Internet of Things (IoT) applications.
The more related data points from various sources that can be pulled into the analytics, the more intelligence can be derived from the data. Automakers can capitalize on providing highly personalized services to prospective car owners. Some of these kinds of capabilities have already been introduced in the industry, generally as piecemeal programs. The full potential, however, will only be realized once a dynamic ecosystem is brought into the process to serve as a data exchange and conduit for different information sources.