The Road Ahead for SEO Optimization
In Attention Deficit Hyperactivity Disorder: The Science Behind It (Dareaux, 2021), six prominent psychologists- psychiatrist Jan Bigler, David Norton, Lisa Pelham, and Michael Schlossman, Ph. –attempt to survey the effects of big data on attention deficit disorder symptoms using data from the Internet. Although the authors are experts in this area, they also acknowledge that their results may not generalize to all individuals. Also, they admit that their study is only a preliminary examination and that more research is needed.
The book is organized into five sections and each addresses one of the core problems related to attention deficit disorder. The first section discusses what big data makes us different. In this section, the authors explain how technology drives the relationship between people and their work. Our ever-evolving information culture and increasing reliance on multiple sources of data to make achieving statistical relevance increasingly difficult. In addition, information can become stigmatizing and lead people to perform in ways that are not aligned with their core values and interests. Finally, because of the potential to distort or destroy meaningful relationships, workers can be susceptible to workplace threats and pressures.
The second segment examines what we can learn from big data when it comes to achieving excellence at work. The authors identify several areas where there is currently no accurate way to measure skill and performance. They describe five technologies that, if adopted, will help to eliminate measurement error, including event-triggered, event-based, and personalized intelligence. In addition, they describe ways in which new technologies can facilitate collective intelligence.
The third section describes the fourth phase in the big data revolution. These authors claim that emerging technologies enable organizations to exploit natural and human intelligence. The four technologies they identify include cognitive networks, social networks, knowledge stores, and agents that access and leverage data. These authors also claim that these technologies will enable organizations to take advantage of the Revolution by building better information centers and by deploying advanced software at each level.
The fifth and final chapter looks at the second wave of the revolution, which is the transformation of data into value chains. The authors identify three types of value chains and identify two types of value chains that have specific roots in the big data era. The first type of value chain is the traditional value chain. The second is called the data-driven value chain. The authors briefly discuss the meaning of value chains and how they relate to the big data era.
The sixth chapter looks at the first fruits of the big data era. It starts with an introduction to distributed systems and then looks at four specific areas related to big data analytics. These areas are workforce management, supply chain optimization, digital dashboards, and enterprise optimization. The focus of this chapter is to introduce the readers to distributed systems and their characteristics. It then goes on to discuss the value chains and their implications for organizations.
The seventh chapter looks at how the Big Data revolution will affect SEO practices. This chapter focuses on seven topics that the authors feel are critical to understanding how SEO practices should change. The topics are search engine optimization and content analysis, link building, content relevance, data sets, and ranking. The authors conclude by describing the results from a recent study on the impact of Google’s Big Data affluence. The study concludes that SEO practices need to change because the abundance of available data sets makes it difficult to effectively achieve good rankings.
The eight and ninth chapters look at different scenarios in which Google might adopt different practices. One such scenario concerns the possibility that Google might combine ratification with other data sets such as customer data. The authors claim that combining data will help Google derive more useful information. In addition to combining data, they also suggest that Google might use text mining and other forms of artificial intelligence. Based on the results of another study by the Center for Automotive Research, the authors claim that the combination of data and text mining will be beneficial for the collective.