Crunching big data: Making sense of massive data sets

We’ve been hearing about big data presenting tons of opportunities for a long time. As the volume, velocity, and variety of data have exceeded the human ability to manage it, organisations in any industry have started implementing new storage architectures to gather as much data as possible, and have adopted solutions to crunch more data than people. The problem is that crunching does not mean making sense: too often businesses have found themselves wondering what to do with the vast amount of data they are amassing.

If you want to manage data, you must understand it

Speed and scale were once the most important features of computing power but the so-called 'information era' has radically changed the business drivers of any organisation, putting data at the centre of their strategies. Today, intelligent systems making sense of information enable the most effective data-driven decisions by bridging the gap between big data and decision-making processes.

Under the imperative 'if you want to manage data, you must understand it', we’ve also seen a renewed interest in Artificial Intelligence solutions driving the rise of fast, powerful, and more intelligent technologies. In particular, cognitive software based on semantics is redefining business processes in today’s data world, by harnessing technology speed and cloud scale as well as human intelligence.

Key trends for big data cognitive computing

Thanks to its human-like ability to understand the meaning of words in context – by combining artificial intelligence semantic algorithms and deep learning techniques – cognitive computing is influencing several business applications, including big data. Here are the most interesting cognitive trends that are dominating the enterprises’ big data scene.

Leveraging OSINT to mitigate risks

Identifying potential threats to mitigate risks, by gathering and correlating relevant information in real time, is one of the most important requirements for any organisation in the public and private sector, from small ventures to Fortune 500 companies.

Companies are exposed to threats on a daily basis, which can impact the supply chain, assets, compromise security and reputation, and cause legal or financial liabilities, product quality issues etc. Thanks to an effective Open Source Intelligence (OSINT) strategy, organisations can build better risk profiles of their vendors, suppliers, stakeholders etc., monitor the environment in which they operate and protect their information management structure to prevent threats, maximise cybersecurity, and mitigate risks before they become a reality.

Organisations are increasingly considering cognitive computing for their OSINT strategies for the capability of understanding the meaning of words in context, in order to extract knowledge from the multiple streams of information coming from websites, news, blogs, etc.

Reducing the complexity of processing data

'Limited findability is a strategic liability': we never tire of saying it. It is a common experience that when it comes to searching for information, eventually despite our robust enterprise platforms, we are often not able to find what we need.

Information constantly grows. There is an increasing need to reduce the labour intensity of processing text and there is clear evidence of the value of a more intelligent text analytics, creating competitive advantage beyond traditional research.

When analysing data, semantic cognitive tools help organisations organise their knowledge, identify and extract information when they need it, and make decisions and predictions more accurately.

Customer support and emotional understanding

Natural Language Processing (NLP) has been empowering cognitive systems to enhance clients' experience and optimise customer activities operations. While enabling a more satisfying dialogue with customers, the use of NLP and semantic analysis in CRM supports better organisation of the corporate knowledge base to provide faster and more accurate responses for all customer questions.

Making sense of big data

The combination of semantics and deep learning is driving the rise of AI technologies that make sense of big data. Cognitive solutions that combine semantic algorithms and deep learning techniques are making a huge impact on risk mitigation strategies, knowledge management, text analytics, and customer care. They improve data processing by reading and understanding terabytes and terabytes of data in a few seconds. They do not replace human thought (and will never do), but they do provide all the information and insight businesses need to improve their decision processes.

Luca Scagliarini, CMO, Expert System