With so much potential value to be realized, is there really such a thing as “too much data”? Delve deeper into the data paradox, and learn how embracing agility and technological innovation can help your business overcome the information overload.
Big Data has been heralded as the agent of a fourth industrial revolution; a harbinger of profound and systemic change that has fundamentally transformed the human experience. Rapid developments in digital technology, information, artificial intelligence and the ‘Internet of Things’ have had a dramatic impact on societies across the globe, effecting social, economic and political shifts of mammoth proportions. This digital revolution has permeated all facets of daily life, proving a disruptive force that affects organizations across sectors, industries and economies. The digitization of global society and the ubiquity of modern computer systems – from mobile phones, to smart watches, to computerized medical implants and cloud-connected national infrastructures – has ushered in a new era for humankind, supporting and underpinning almost every aspect of modern society.
Dramatic technological breakthroughs in computing, communications and information processing have propelled contemporaneous society into the digital era, giving rise to new digitized markets, goods and services. Big Data is a foundational driver of this digital transformation. Technologies that produce and utilize data have become ubiquitous, accessible and inexpensive, linking billions of individuals, businesses and devices in an interdependent digital ecosystem. The new digital marketplace established through these technological innovations opened the floodgates to a torrent of data generated by billions of individuals, devices and organizations. This data, when contextualized and analyzed through advanced artificial intelligence (AI) driven analytics and machine learning algorithms, translates into important strategic assets that accelerate further development. The sheer power of digital and data processing is reshaping nearly all aspects of the global economy, displacing incumbent firms and legacy technologies.1
The realization of the inherent potential within these extensive information troves relies on the ability to extract value from mammoth datasets through artificial intelligence and machine-learning analytics. These technologies allow for the sensemaking of raw and unstructured data, translating incoherent, varied datasets into actionable intelligence.2
This potential, if properly realized, augurs enormous benefits for operational and intelligence processes alike. More organizations are moving to leverage Big Data and digital technologies to transform business operations, enhance productivity and improve existing SaaS workflows. In parallel, intelligence teams attempt to process and analyze the enormous quantities of raw data to derive intelligence insights that could prove crucial to national, cyber and personal security.
This new data-centric economy, centered upon digitization and digitalization, generates, demands, collects and analyzes more data than ever before. Herein, however, lies the ‘Data Paradox’ – the massive influx of data has given rise to data overload, whereby the inability to extract meaningful insights from data has emerged as the third highest barrier to digital transformation.
Accordingly, while Big Data and the digital revolution creates profound benefits to daily life, it also presents significant risks and liabilities, as well as a myriad of new threats, opportunities and challenges to the intelligence process. As technology evolves, so too does the threat landscape. As economies, governments and societies migrate to the digital sphere, so too do terrorists, foreign intelligence services and cybercriminals. Digitalization, while having propelled humankind to the next phase of industrial advancement, has also created a multitude of inherent vulnerabilities and possible attack vectors for nefarious actors to exploit. Exploitation of these vulnerabilities by threat actors on cyberspace have severe implications for national security, businesses and individuals.
In the depths of the digital underground, threat actors convene in a malicious cybercriminal ecosystem, exploiting the deep and dark web to recruit members, plan attacks, share malicious hacking tools and services, transact illicit items, exfiltrate sensitive information, execute cyberattacks, and engage in cyberespionage. Within this rapidly changing threat landscape, Big Data provides an essential opportunity for intelligence professionals to address these complex security dilemmas. The digitalization of daily life has dramatically increased the amount of raw information available to intelligence teams, offering unprecedented analytical opportunities that may provide answers to crucial questions of national, organizational and personal security. These opportunities, however, are counterbalanced by significant challenges to the intelligence process itself. The “information overload” resulting from the volume, velocity, variety and ubiquity of Big Data has disrupted the traditional tools, methods and paradigms of intelligence as a practice. Conventional organizational structures, practices and processes are proving inadequate in the face of the Big Data disruption, obstructing analysts from discerning the true import of the data that lies before them and withholding the tools and methods that would help them. The paradigm shift driven by the Big Data revolution requires innovation and creativity in order to truly leverage its benefits, leveraging the power of artificial intelligence and machine learning technologies and solutions to meet the pace of the digital transformation.
Today’s world requires innovative technologies, structures and processes to facilitate timely, coherent and relevant threat analysis, translating raw data into actionable knowledge through the application of machine learning analytics. This would be impossible to achieve in the absence of a radical transformation of both tradecraft and technology, rebuking the outdated, inefficient and siloed approaches of the past in favor of: accessible and integrated data architecture; advanced automation and machine-learning capabilities to expand, automate and sharpen the collection and analysis of high-volume data in real time; AI processing to triage and prioritize information for specific analysts/missions; and task automation for categorization, summarization and visualization. Leveraging and assimilating Big Data, AI and other disruptive technologies into the intelligence cycle will require radical innovation to reconfigure the institutional culture, skillsets, processes and structure to support this technological integration.
Within the rapidly accelerating cyber threat landscape, whereby new technologies, processes, social structures and markets are constantly emerging as incumbents lose their vitality and appeal, adaptability is crucial for any organization to remain relevant. To further explore the impact of digitization, the resulting data-overload on the cybersecurity industry, and the best practices to circumvent these data-driven challenges via innovation, technology and workforce discovery join Cybersixgill at Re:Con22.