Integrating UX, Big Data and Cyber Security for Improved Protection

Big Data and UX are much more than industry buzzwords—they are some of the most important solutions making sense of the ever-increasing complexity and dynamism of the international system. While big data analytics and user experience communities (UX) have made phenomenal technical and analytic breakthroughs, they remain stovepiped, often working at odds, and alone will never be silver bullets. Big data solutions aim to contextualize and forecast anything from disease outbreaks to the next Arab Spring. Conversely, the UX community points to the interface as the determinant battleground that will either make or break companies. This disconnect is especially prevalent in cyber security and it is the user (and their respective companies) who suffers most. Users are either left with too much data but not the means within their skillset to explore it, or a beautiful interface that lacks the data or functionality the users require. But the monumental advances in data science and UX together have the potential to instigate a paradigm shift in the security industry. These disparate worlds must be brought together to finally contextualize the threat and the risks, and make the vast range of security data much more accessible to a larger analytic and user base within an organization.



At a 2012 Strata conference, there was a pointed discussion on the importance of machine learning versus domain expertise. Not surprisingly, the panelists leaned in favor of machine learning, highlighting its many successes in forecasting across a variety of fields. The die was cast. Big data replaced the need for domain expertise and has become a booming industry, expanding from $3.2B in 2010 to $16.9B in 2015. For companies, the ability to effectively and efficiently sift through the data is essential. This is especially true in security, where the challenges of big data are even more pronounced given the need to expeditiously and persistently maintain situational awareness of all aspects of a network. Called anything from thesexiest job of the twenty-first century to a field whose demand is exploding, there is no shortage of articles highlighting the need for strong data scientists. More often than not, the spotlight is warranted. Depending on which source is referenced, over 90% of the world’s data has been created in the last two years, garnering big data superlatives such as total domination and the data deluge.

Clearly, there is a need to leverage everything from machine learning to applied statistics to natural language processing to help make sense of this data. However, most big data analysis tools – such as Hadoop, NoSQL, Hive, R or Python – are crafted for experienced data scientists. These tools are great for the experts, but are completely foreign to many. As has been well documented, the experts are few and far between, restricting full data exploration to the technical experts, no matter how quantitatively minded one might be. The user experience of these tools is not big data’s only problem. Without the proper understanding of the data and its constraints, data analytics can have numerous unintended consequences. For instance, had first responders focused on big data analyses of Twitter during Hurricane Sandy, they would have ignored the large swath of land without Internet access, where the help was most needed. In the education realm, universities are worried about profiling as a result of data analysis, even to the extreme of viewing big data as an intruder. Similarly, even with the most comprehensive data, policy responses require a combination of data-driven input, as well as contextual cultural, social, and economic trade-offs that correspond with various policy alternatives. As Erin Simpson notes, “The information revolution is too important to be left to engineers alone.” David Brooks summarized some of the shortcomings of big data, with an emphasis on bringing the necessary human element to big data analytics. Not only are algorithms required, but contextualization and domain expertise are also necessary conditions in this realm. This is especially true in cyber security, where some of the major breaches of the last few years occurred despite the targets actually possessing the data to identify a breach.

So how can companies turn big data to their advantage in a way that actually enables their current workforce to explore, access and discover within a big data environment? A new tech battleground has emerged, one for the customer interface. The UX community boasts its essential role in determining a tech company’s success and ability to bring services to users. Similar to the demand for data scientists, UX is one of the fastest growing fields, becoming “the most important leaders of the new business era…The success of companies in the Interface Layer will be designer-driven, and the greatest user experience (speed, design, etc.) will win.” The user-experience can either breed great product loyalty, or forever deter a user from a given product or service. From this perspective, technology is a secondary concern, driven by UX. The UX community prioritizes the essential role of humans over technologies, focusing on what the users experience and perceive. This is not just a matter of preferences and brand loyalty; it’s about the bottom line. By one measure, every $1 invested in UX yields a $2-$100 return.

In fact, the UX community is increasingly denoting the essential role of UX in extracting insights from the data. Until relatively recent advances in UX, the data and the technologies were both inaccessible for the majority of the population, driving them to spreadsheets and post-it notes to explore data. UX provides the translation layer between the big data analytics technologies and the users, enabling visually intuitive and functional access to data. The UX democratizes access to big data – both the technologies driving big data analytics as well as the data itself. Unfortunately, the pendulum may have swung too far, with data perceived at best as “a supporting character in a story written by user experience” and at worst as simply ignored. The interface layer alone is not sufficient for meeting the challenges of a modern data environment.



The data science and UX communities are innovating and modernizing in parallel silos. In some industries, such as cyber security, they are unfortunately rarely a consideration. Although necessary, neither is sufficient to meet the needs of the user community. Customers are not drawn to a given product for its interface, no matter how beautiful and elegant it might be. It has to solve a problem. The reason products such as Amazon, Uber and Spotify are so popular is because of the data and data analytics underlying the services they provide. In each case, each product filled a niche or disrupted an inefficient process. That said, none of these would have caught on so quickly or at all without the modern UX that enabled that fast, efficient and intuitive exploration of the data. Steve Jobs mastered this confluence of technology and the arts, noting “technology alone is not enough. It’s technology married with liberal arts, married with humanities, that yields the results that make our hearts sing.”

It is this confluence of the arts and technology – the UX and the data science – that can truly revolutionize the security industry. The tech battlegrounds over machine learning and domain expertise or big data and UX are simply a waste of time. To borrow from Jerome Kagan, this is similar to asking whether a blizzard is caused by temperature or humidity – both are required. Together, sophisticated data science and modern, intuitive UX can truly innovate the security community. It is not a zero sum game, and the integration of the two is long overdue for security practitioners. The security threatscape is simply too dynamic, diverse and disparate to be tackled with a single approach. Moreover, the stakes are too high to continue limiting access to digital tools and data to only a select few personnel within a company. The smart integration of data science and the UX communities could very well be the long overdue paradigm shift the security community needs to truly distill the signal from the noise.

Graphic credit: Philip Jean-Pierre