Just Sociology

Navigating the Big-Data Value Chain: Companies Power Relations and Skills Gap

The increasing amount of data generated worldwide has led to the emergence of the big-data value chain, which encompasses all aspects of data collection, analysis, and utilization in various fields. This article provides an overview of the big-data value chain, focusing on the types of companies involved, power relations between businesses, hybrid data companies, data analytics, and the skills gap.

The Big-Data Value Chain

The big-data value chain comprises various stages that involve the collection, processing, analysis, and use of data to create economic value. It consists of different types of companies that play distinct roles in the value chain.

Data-collection companies collect and store raw data from various sources, including social media platforms, IoT systems, and connected devices. Twitter is an example of a data-collection company that generates vast amounts of user-generated content, while Teradata specializes in data warehousing and management.

Jetpac, on the other hand, collects data on public images and videos shared on social media.

Data-analytics companies specialize in processing and analyzing large datasets to generate insights, patterns, and trends that can inform business decisions.

Such companies include SAS, IBM, and Oracle.

Data-ideas companies focus on identifying the potential value of under-utilized data by developing new use cases or business models.

They help businesses harness the latent value of their data.

Power Relations Between Businesses

The organizational landscape of big data is shaped by power relations among businesses, with some companies leveraging their data resources to create competitive advantages. Companies that adopt a big-data mindset and invest in the value from data tend to outperform their peers.

The value of big data depends on how well a company can leverage it to create business insights, competitive intelligence, and customer understanding. Companies that fail to embrace big data tend to fall behind.

Hybrid Data Companies

Companies such as Google and Amazon have developed a hybrid business model that combines data-collection, analytics, and utilization. Vertical integration of these processes enables these companies to optimize value creation through their data resources.

Such companies have become dominant players in the big-data market, given their vast resources and expertise across the value chain.

Data Analytics in the Big-Data Value Chain

Data analytics is a crucial aspect of the big-data value chain, occupying a prime position in the value chain. The shortage of data scientists is a persistent challenge that has slowed the growth of the big-data industry.

According to a report by the McKinsey Global Institute, there is a projected shortfall of 1.5 million data professionals by 2018.

Google’s chief economist, Hal Varian, argues that data scientist positions are among the hardest to fill in the tech industry.

The skills gap is attributed to the multidisciplinary nature of data analytics, which requires a combination of technical, statistical, business, and critical-thinking skills.

Exaggerated Claims of the Shortage of Skills

However, some scholars, such as Kenneth Cukier, challenge the notion of the skills shortage, pointing out that there is a gap between the demand for data scientists with advanced skills and the supply of such professionals. According to Cukier, the skills gap is closing, as more universities offer data science courses, and the trend toward automation reduces the burden on human analysis.

Real Money in Big Data Innovators

The real money in big data lies in innovations that help businesses control access to their data or charge more for access to it. Companies such as AppNexus and Rubicon Project have developed platforms that facilitate programmatic advertising by leveraging big data.

These platforms allow businesses to target specific audiences based on their data profiles, making the advertising process more efficient and effective.

Conclusion

The big-data value chain comprises various types of companies that contribute to data collection, analysis, and utilization. Power relations between businesses are shaped by the level of investment in data-driven decision-making, with hybrid data companies dominating the market.

Data analytics is a crucial aspect of the big-data value chain, and the shortage of data scientists remains a challenge. Nonetheless, as more universities offer data science programs and automation continues to reduce the demand for human analysis, the skills gap is expected to narrow.

The real money in big data lies in innovations that help businesses control access to their data or charge more for access to it. Expansion:

3) The New Data Intermediaries

The rise of digital networks and the proliferation of the internet of things has accompanied the emergence of new data intermediaries who facilitate the exchange of third-party data across different sectors of the economy. These intermediaries leverage their ability to gather and aggregate data from different sources and offer solutions that create value to businesses and consumers.

Some sectors and companies benefit greatly from third-party data intermediaries. For example, third-party data intermediaries can help travel firms identify the preferences of potential customers, enabling them to make customized travel recommendations.

Banking and financial firms can use third-party data to execute risk analysis and fraud detection, enhancing their customers’ trust in their services. Furthermore, new startups can combine data from different sources to create value in the market, facilitating trust between various sectors.

Realizing the potential of third-party data intermediaries, various sectors are exploring the possibilities of expanding their reach through partnering or investing with these firms. Large businesses, such as Google and Amazon, are also positioning themselves to offer third-party data to consumers, creating an ecosystem in which these large businesses are most likely to dominate.

The Demise of the Expert

Data analytics requires multidisciplinary skills. Traditionally, data science has been an exclusive domain reserved for experts who possess rigorous training in mathematics, statistics, programming, network science, and other essential skills.

However, there has been a significant shift in recent years. Today, the general trend is towards the democratisation of data, with the rapid development of new tools and software that enable non-experts in data analysis to participate actively in data analysis.

Moreover, to be truly effective, data analytics should be grounded in foundational skills beyond just data analysis. Technical skills are essential, but uniquely valuable are interpretative skills that allow analysts to translate data into meaningful insights.

Another critical attribute is humility, an attribute that allows skilled professionals to consider multiple perspectives, acknowledge and learn from their mistakes, and confess ignorance when necessary.

4) Winners and Losers in the Big-Data Value Chain

The big-data value chain has seen large data companies soar, with Google and Amazon dominating the market. These companies have vast data resources and expertise across different facets of the data value chain.

They can leverage vertical integration to optimize value creation: combining data-collection, analytics, and utilization makes it possible to create a data-driven ecosystem of business innovation.

For small-world data companies, the challenges include the cost of data storage, management, and analytics, as well as increasing competition from larger firms.

As such, alternative options have emerged. For instance, spinoffs from academia, which involves knowledge sharing by universities and research institutions can leverage the knowledge-base of these institutions to create unique and specialized data products.

Similarly, open source platforms that allow for API data-sharing to communities of analysts have emerged, making it possible to share data on a diverse range of topics. Other winners in the big-data value chain include smart and nimble start-ups.

A fundamental feature of such start-ups is that they are agile and possess the ability to develop and test new products at a faster pace than large organizations. Consequently, they can take greater risks, iterate more quickly, and capture emerging opportunities.

They can also leverage their ability to capture and store consumer data, which can be sold to large companies.

On the other hand, individuals can also benefit from the big-data value chain by selling their personal data through intermediaries.

This process allows consumers to have more control over the value created from their data. Otherwise, they can be aggregated and capitalized upon by large data companies.

They are offered personalized recommendations, exclusive offers, or special deals in exchange for their data.

Alternatively, third party firms can emerge, offering services such as mediation, data exchange, and customized data provision.

These new entrants have the potential to disrupt the market, creating unique opportunities for consumers and businesses. Thus, the big-data value chain offers ample opportunity, including established businesses and start-ups, individuals willing to sell their data, and firms that offer mediation between different parties to create value.

Conclusion

The expansion of digital networks and the rapid growth of big data have created new opportunities for businesses, individuals, and third-party service providers. The value created in the big-data value chain is underpinned by multi-disciplinary skills, including foundational skills such as interpretative skills and humility.

Large data companies dominate the market, but new entrants such as smart start-ups and third-party firms have found ways to disrupt the market through data-based innovation. Consumers have the power to profit from their data by leveraging third-party intermediaries or exchanging it directly with large companies.

Overall, the big-data value chain is diverse, offering opportunities for established companies and new entrants alike.

Conclusion

The big-data value chain is a complex and rapidly evolving landscape that involves various types of companies specializing in data collection, analysis, and utilization. The emergence of third-party data intermediaries and the democratization of data analytics are set to disrupt the status quo, creating new opportunities for businesses, individuals, and third-party firms.

The significance of multi-disciplinary skill-sets is emphasized while acknowledging the dominance of large data companies in the market. Nonetheless, the big-data value chain remains a dynamic and diverse ecosystem, offering ample potential for established companies and new entrants to innovate and create value.

FAQs

Q: What are the three types of companies in the big-data value chain? A: Data-collection, data-analytics, and data-ideas companies.

Q: How can third-party data intermediaries facilitate trust between sectors? A: By combining data from different sources to create value in the market.

Q: What are the essential multidisciplinary skills required in data analysis? A: Skills in mathematics, statistics, programming, network science, interpretative skills, and humility.

Q: Who dominates the market in the big-data value chain? A: Large data companies such as Google and Amazon.

Q: How can start-ups benefit from the big-data value chain? A: By being agile and leveraging their ability to capture and store consumer data, which can be sold to large companies.

Q: Can individuals profit from their data in the big-data value chain? A: Yes, by leveraging third-party intermediaries or exchanging it directly with large companies.

Q: What is the significance of the big-data value chain? A: The big-data value chain offers ample opportunity for innovation and value creation for businesses, individuals, and third-party firms.

Popular Posts