Sophisticated algorithms have long been anchored in corporate strategies. But: predictive data analytics delves deep into an organization. Hence, these systems must have maximum security.
Predictive data analytics, the most important sub-discipline of big data analytics, is making inroads into organizations and companies more than any previous technology. U.S. forces on combat duty use a product by a major software vendor to choose their patrol routes for the next day. The algorithms point out potential danger spots along the routes – and do so extremely reliably. Other predictive data analytics solutions suggest new approaches to intracompany supply chains based on weather data, information from suppliers and infrastructure data. The results: massive savings and increased efficiency. Or take retail, where pricing lies at the heart of every business. Gut feelings and history data have long stopped being the key factors; predictive data analytics is making decisive inroads. When the data is analyzed, the predictions are much more reliable as to whether a product languishes on the shelves at EUR 10.99 or becomes a top seller for EUR 9.99. In a nutshell: there's a good reason why sophisticated algorithms are increasingly becoming an integral part of corporate strategy.
Fort Knox for digital assets
As a result of this penetration, however, the applications must have absolute integrity. After all, they represent the "crown jewels" of an organization: "And those crown jewels will be readily accessible in the cloud and via mobile devices across our hyper-connected enterprises – and not just by us, but by our adversaries as well," warns Art Coviello, Executive Chairman of RSA, a security company. Accordingly, companies have to analyze all their records and have the right context to shine light in the dark recesses. In other words, when a company delves deeply into predictive data analytics and makes it available to its field staff, for example, then that company's most valuable assets – its knowledge, its customer data and even patents and information – go mobile. And that's not all: in addition to these aspects of data security, privacy factors also play a role. Customer data or results from risk analyses, for instance, are not only mission-critical, but can also have a compromising aspect.
Security concerns are an obstacle to predictive data analytics
It should come as no surprise that a joint survey of German companies conducted by Bitkom, the German IT industry association, and the consulting firm KPMG, found that legal and security concerns are the greatest obstacles to the use of innovative data analytics. Moreover, 41 percent of those surveyed spoke of an uncertain legal situation. In particular, privacy concerns stand in the way of implementing advanced analytics for data with different structures and from different sources. Nearly a third (29 percent) of the participants also expressed worry of public criticism as a reason for not using data analytics. Nonetheless, companies are well advised not to let the analytics train leave the station without them. After all, when billions of devices are connected and exchanging data with one another in the Internet of Things, for example, and this data and its analysis comprise the value of a company, passing up the opportunities posed by predictive data analytics seems negligent at the least.
The joyful message: a solution is in sight
An unsolvable dilemma? Not at all, as a number of studies show. It's all the more reason to rethink security, especially IT security. For example, imposing strict policies for mobile devices, with remote maintenance to ensure that they are always secure and – in an emergency – can be wiped. Requiring and promoting close cooperation between users and IT security – including strict demands for encryption technologies, effective user authentication and, where needed, data anonymization. As such, there is good reason why analysts predict strong growth in managed security services in the context of data analytics. Sometimes companies need a strong partner that has big data security as one of its core competencies. Ultimately, predictive data analytics that is both effective and secure is a characteristic of well-balanced risk management: companies have to be able to identify their risks across the board and know where their digital risk zones lie. Only then can they apply maximum security to these hot spots.
The boom is coming – one way or another
The bottom line: predictive data analytics can be designed to be both efficient and secure. Ultimately, it's all a question of the resources. It goes without saying that security requires investments. At the same time, however, these expenditures are often reflected quickly in increased corporate value, as current figures show: in 2013, the market researchers of IDC predicted that the market for big data analytics would increase to 16.1 billion dollars in the following year and continue to grow to 32.4 billion by 2017. In fact, revenues from big data analytics amounted to 125 billion dollars in 2015, according to a recent report by Accent Technologies. That's an impressive figure – and certainly not just an end in itself.
Companies nowadays need a much broader and more exact database in order to address customers successfully across all sales and communications channels. Big data is becoming a key competitive advantage.