Navigating big (data) influences in 2017

Navigating big (data) influences in 2017

By Daniel Ng, Senior Director, APAC, Cloudera

Over the past year, big data technology has continued to evolve and become more enterprise ready in terms of usability, back-up, recovery, and performance. Beyond merely analyzing why something happened, big data has enabled us to proactively drive business results and make more meaningful decisions in real-time. Experts have also been studying and predicting the current and continued growth of the big data and analytics market across the globe, especially in data-rich industries such as financial services[1].

From governments using big data to defend against terrorist attacks to medical researchers predicting disease spread patterns and agricultural companies increasing crop yields, the big data revolution is now at a stage that is bigger than the past industrial revolution. While the industrial revolution taught us how to create things, big data continues to balloon, making the world more connected, smarter, and efficient than ever before.

What can industries, businesses, and individuals look forward to in 2017 and how can big data technology and the community create greater value for businesses and users? Here are some insights on how big data will create bigger influences this year:

As markets shift and rapidly changing technologies transform businesses, companies that do not have professionals with up-to-date skills will fall behind. In fact, big data and analytics are no longer just buzzwords, but one of the most desirable skills for professionals to get hired in 2017[2].

In 2016, we saw governments, public sector organizations, academic institutions, and private sector players come together to start building an environment where technology and data talent can better flourish and thrive. In order to keep pace and build the data workforce of the future, the importance (and awareness) of big data and analytics in the region continues to grow and take main stage[3]. Talent has now become a challenge that will impact countries, industries, and companies across the globe.

In 2017, governments will continue to launch open data initiatives and skills training programs, companies will focus on training their current employees, and academic institutions will work hand-in-hand with industries to ensure their students are ever-ready for their future careers. But the onus on technology players will remain; to make their platforms increasingly user-friendly and facilitate knowledge sharing amongst users.

As the world has gotten more connected, and “things” have gotten smarter, we see new opportunities for autonomous behavior as the next stage in the big data revolution. Machine Learning (ML) is the latest buzzword – an important ingredient for autonomous capabilities – which will enable the creation of complex algorithms to drive behavior that does not even require pre-defined business logic.

Thus far, processing Internet of Things (IoT) data has enabled users to derive at more data-driven and sophisticated insights in real-time. In 2017, big data will continue to play a big role in developing algorithms that will be the driving force for autonomous capabilities. With the volume of data required to make this happen, organizations will require truly massive data storage and computing capacities in order to generate and continuously improve the algorithms needed for autonomous devices. Big data will continue to be the raw material that makes us smarter and more efficient.

In 2016, we analyzed the impact of security breaches and importance of cybersecurity solutions for businesses today[4]. In 2017, big data will continue to play a critical role in protecting organizations and their assets from cyber-threats – the future of fighting cyber-crime will rely heavily on leveraging data for cybersecurity purposes.

As technology continues to evolve and the world gets increasingly hyper-connected, we see greater innovation that will improve day to day living (from home safety to medical care and ease of transportation). As such, more sophisticated security breaches and privacy issues will be brought to the limelight – those that traditional security boundaries and technologies will not be sufficient to prevent or resolve.

This year, we can expect organizations to focus on implementing cybersecurity platforms that are scalable across trillions of events, monitoring all of the devices connected to and accessing an enterprise’s network. Moreover, applying ML for anomaly detection will allow organizations to continue detecting suspicious endpoint behavior faster and more accurately than before.

Pairing modern platforms with ML will be key to the early detection of threats and issues in 2017, moving us toward a smarter and more data-driven, secure future.