Top 10 Reasons Why Large Companies & Government Fail at Crowdsourcing Data

Why Do Big Companies Fear Using Crowdsourced Data?

We have been crowdsourcing map data for over 15 years, long before the term "crowdsourcing" was coined by an article in 2006.  The more I speak with large companies about crowdsourced data the more I begin to understand why most large companies and governments fail at collecting and using crowdsourced data for their benefit.

1. Management fear of data having "errors" and lack of control
2. Lack of management leadership, passion, and communication for the problem
3. Poor filtering processes and requirements for contributed data 
4. Not sharing enough data that is collected by your users 
5. Lack of confidentiality and not allowing anonymous users to contribute data
6. No SEO / PR / marketing strategy to find data collectors 
7. Scrapping data from “like” sources is not crowdsourcing 
8. Lack of engaged community discussion on the data
9. Poorly defined niche categories and lack of a problem being solved 
10. "Too many cooks in the kitchen" managing data.  

Companies that are successful at crowdsourcing now and in the future are lean and mean.  They are self-funded, bootstrapped by advertising, or subsidized by a large parent company.  Some license data as well to other mapping companies but this is a tough business.  Owning and taking a risk on building data sets is the future of geospatial crowdsourcing.  

Buying versus building is a better solution for big companies in my opinion.  

In the era of big data, where information is often hailed as the new currency, one might assume that companies, especially large corporations, would eagerly embrace any opportunity to gather more data. However, a curious phenomenon persists: many of these big companies approach crowdsourced data with caution, if not outright reluctance. This hesitancy raises a pertinent question: Why do big companies fear using crowdsourced data?

Quality Concerns

One of the primary reasons behind the wariness towards crowdsourced data is the perceived issue of quality. Crowdsourcing involves collecting information from a large, diverse group of individuals or sources, which can lead to variability in data accuracy and reliability. Unlike controlled experiments or structured surveys, crowdsourced data often lacks standardized methodologies for collection and verification, making it challenging for companies to trust its integrity. This variability in quality can be a significant deterrent for companies that rely on precise, actionable insights to drive their business decisions.

Privacy and Security Risks

Another major concern revolves around privacy and security risks associated with crowdsourced data. Gathering information from a diverse crowd means potentially accessing sensitive or personal data from individuals who may not fully understand how their information will be used or protected. This raises legal and ethical considerations regarding data ownership, consent, and compliance with regulations such as GDPR or CCPA. Big companies, with their extensive consumer bases and regulatory scrutiny, are particularly cautious about inadvertently exposing themselves to legal liabilities or reputational damage through mishandling crowdsourced data.

Reliability and Bias

Beyond quality and privacy concerns, there is also apprehension about the inherent biases that can permeate crowdsourced data. Biases may arise due to the demographics, motivations, or geographical locations of the contributors, skewing the data in ways that are not immediately apparent. These biases can lead to flawed analyses and misguided business strategies, undermining the trustworthiness of the insights derived from crowdsourced sources. For companies that prioritize data-driven decision-making, the potential for biased data poses a significant risk to the accuracy and effectiveness of their strategies.

Integration Challenges

From a practical standpoint, integrating crowdsourced data into existing data infrastructure and analytics frameworks can be complex and resource-intensive. Big companies often operate on robust data management systems that are designed to handle structured, internally generated data. Incorporating crowdsourced data requires adapting these systems to accommodate unstructured, heterogeneous data formats, which may require significant investments in technology and expertise. The compatibility and interoperability issues further contribute to the hesitation among companies to fully embrace crowdsourced data as a reliable source of information.

Perception and Control

Lastly, there is a psychological aspect to the reluctance towards crowdsourced data among big companies: the perception of control. Organizations accustomed to meticulously curated datasets and controlled experiments may find it challenging to relinquish control over data collection processes to external, decentralized sources. The lack of oversight and direct influence over crowdsourced data collection and validation processes can be unsettling for companies accustomed to maintaining a tight grip on their data assets.

Conclusion

While crowdsourced data holds immense potential for providing real-time, diverse insights that can complement traditional data sources, its adoption by big companies remains cautious and measured. Addressing concerns related to data quality, privacy risks, biases, integration challenges, and perceived loss of control is essential for fostering greater acceptance and utilization of crowdsourced data in corporate decision-making processes. As technologies and methodologies for data validation and privacy protection evolve, so too may the attitudes of big companies towards harnessing the power of crowdsourced data in a responsible and effective manner.