The Three Big Data Waves: Managing Data Structures, Managing the Web and its Content, and Big Data Management

Big data is an overwhelmingly powerful force that is being generated, gathered, and analyzed at an ever-increasing pace. With the rise of the "internet of things," the amount of information being produced is growing at a logarithmic scale. However, despite the vast amount of data available, there is still too much information to process, and only a small percentage of the knowledge is currently being extracted from it. This highlights the need to find new and innovative ways to locate the unknown unknowns within the vast oceans of data.

The current methods of handling data are insufficient, and it is crucial to develop new ways to extract meaningful intelligence from the vast amount of information available. As the volume of data continues to grow, we need to focus on finding new approaches to processing and analyzing this data to extract the maximum possible knowledge. The ability to locate the unknown unknowns within the vast oceans of data will become an increasingly important task in the future.

The emergence of big data as a significant field has been a result of a gradual evolution of technology. It took a long time for big data to develop, and it is identified by its enormous volume, velocity, and variety of data. Each step in this process, which is characterized by the three waves of big data, has become more intricate and complex, culminating in the current state of affairs.

1st wave – Managing data structures

In the last 50 years, various factors have converged, leading to the need for new waves of innovation in managing data. The advancement of technology has played a significant role in driving this evolution of data. However, like any evolutionary process, progress has been hindered by challenges and incremental advancements, which in turn give rise to new problems that require solutions. Yet, it is often out of these problems that novel solutions arise, solutions that were not even thought of before. This interplay between the problems and solutions gave rise to the first big data wave, which focused on managing data structures.

2nd wave – Managing the web and its content

The need to store and comprehend unstructured data led to the realization that directly approaching the information was futile and inefficient. To achieve the desired results, it was necessary to create structure within the data. This insight drove the search for a solution to the problems of expensive storage and slow access, with the goal of long-term storage to reveal patterns and connections over time. As progress was made, new connections became easier to identify.

However, the sheer volume of data was still a challenge, and the limitations on handling such vast amounts of data became a hindrance to businesses' optimal functioning. Traditional methods of inputting data into warehouses were no longer sufficient for real-time business transactions. A new approach was necessary, and this was found by aggregating chunks of information into an addressable format that was easier to manage. This development marked the emergence of the second big data wave: managing the web and its content.

3rd wave – Big Data Management

The rise of the web in the 90s brought about a new challenge in the form of managing and understanding even more diverse unstructured data, including audio and visual materials. As a result, metadata emerged as a means to gain insights into the structure and format of stored data. However, the proliferation of computing and the explosive growth of data in various forms presented new challenges in terms of handling and processing this information quickly. This led to the third wave of big data management, building on the advancements of the previous waves to create comprehensive methods for analyzing massive amounts of data.

The culmination of years of data management evolution has brought us to a point where it is feasible to access and draw insights from the vast amount of information available. Even with the immense input size, we are now able to answer questions and uncover patterns previously unknown to us. However, the ultimate test of the results obtained from this vast amount of data is whether they accurately correspond to the real world when applied. It remains to be seen if the insights gleaned from big data analysis will have practical applications in the real world.

Conclusion

The evolution of big data management has been a gradual and challenging process, with each wave building on the previous one. The current state of big data is characterized by the availability of vast amounts of data, which is diverse and requires fast processing. However, the challenges of extracting meaningful intelligence from big data remain, and new innovative ways are needed to locate the unknown unknowns. As we continue to evolve and improve big data management, it will be interesting to see how it will impact various industries and the world at large.