Around the world, businesses are turning to artificial intelligence and machine learning to transform the way they operate and serve their customers. But before they can implement these technologies, companies must address several roadblocks: moving from analysing data in hindsight to making real-time decisions by rapidly accessing and analysing the relevant information amidst a sea of data; becoming faster and more agile by processing data closer to their source; and accessing solutions that reduce cost and complexity for smaller companies.
Real-time actionable insights are enabling new solutions in telecommunications, financial services, and transportation, leading to smarter business decisions, better understanding of customers and delivery of frictionless consumer experiences.
Staying ahead of threats
At the same time that Asia-Pacific (APAC) is driving global growth in big data and business analytics, the region suffered US $81 billion in lost business revenues due to cyberattacks in 2016. Singapore is nine times more vulnerable to cyberattacks than other Asian countries due to high digital connectivity. As companies face increasingly refined cyber threats, they must not only respond but stay one step ahead of attacks.
Telecommunications companies have been benefiting from new data technologies that improve cybersecurity defense. Telcos are especially targeted by cybercriminals due to critical infrastructure services, and as a result require up-to-the minute knowledge about ecosystem threats and vulnerabilities. In the new era of cybercrime, waiting for data to be sent and processed through large cloud solutions is inadequate. Instead, using platforms that enable continuous analytics with many data types and sources, companies can analyze information quickly and weed out deeply hidden threats with minimal false alarms. The ability to deploy the system closer to the source across multiple sites boosts response time and generates faster insights than mainstream solutions.
Similarly, forward looking financial institutions facing a global US$ 35 billion credit card fraud loss in 2020 are innovating new ways to detect and thwart fraud across multiple channels. With only a few seconds to respond, banks are using AI and machine learning that draw on historical and current data to better detect if a credit card transaction is “normal”. At the same time, financial institutions can avoid setting off false alarms and blocking valid transactions by drawing on external information such as location and social data.
The speed and precision gained by continuous analytics enables banks to reduce the time and cost of investigating fraud incidents, protect their reputations, and reduce friction for customers.
With an additional 90 million people in Southeast Asia moving to urban areas by 2030, cities are looking to smart mobility solutions to maintain sustainable economic growth.
iguazio has been working with companies like Southeast Asia’s ride-hailing firm Grab to leverage data in real-time for smart mobility and better customer experience. With massive amounts of data collected from sensors, mobile applications and external sources showing traffic or weather conditions in each market, Grab uses real-time heatmaps to distribute traffic efficiently. Drivers can be guided to locations where they are needed, as well as avoid having all drivers go straight to high demand areas, which is one of the reasons congestions are created around the end of a business day.
Additionally, Grab is able to innovate new models or run experiments to improve features such as the driver’s Estimated Time of Arrival by capturing metrics in real-time. Having access to high performing data technology at a fraction of the cost enables regional players to innovate and compete against global, well-funded players.
Looking Ahead to 2018
Despite the popularity of AI and machine learning this past year, Forrester found that 55 per cent of businesses that invested in AI have yet to achieve tangible results. Moving into 2018, three factors will impact the ability of companies to use predictive analytics and intelligent systems.
- Companies must shift to continuous processing of multiple sources of data in real-time in order to innovate and improve their services.
- Companies will need to process data closer to their source and generate faster and more accurate insights than what major cloud solutions currently enable.
- Data technology will need to become more accessible to smaller companies, rather than limited to tech giants.
Companies need tools to gather ever growing pools of data from as many sources as possible, break down silos, apply fine-grained security, and simplify the development and processing of information. Only then can they fully leverage predictive analytics and intelligent systems to drive innovation and gain a competitive edge in the new digital era.
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