I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First. ...Full Bio
I work at ValueFirst Digital Media Private Ltd. I am a Product Marketer in the Surbo Team. Surbo is Chatbot Generator Platform owned by Value First.
Success story of Haptik
585 days ago
Who is afraid of automation?
585 days ago
What's happening in AI, Blockchain & IoT
586 days ago
3 million at risk from the rise of robots
586 days ago
Big Data and Search: The Time for Artificial Intelligence Is Now
Businesses have been relying on search and big data analytics for many years to gain insight into their data. In recent years, these technologies have evolved rapidly and now incorporate machine learning and artificial intelligence, and they increasingly allow enterprises to more fully integrate their big data results into business action, whether it be for customer service, assembly line production, precision medicine or any of a number of other business use cases.
Big data applications today must be built to provide real-time results for businesses to compete in today's fast-moving marketplace. Powered by search and big data, artificial intelligence (AI), machine learning, natural language processing (NLP) and cognitive (or "intelligent") search are leading the way.
As we enter this new age, the time is near for1 businesses to incorporate these technologies - though they must be cognizant of the challenges that exist and the resources that will be required.
Using Search to Deliver Big Data Analytics
As the result of the growing demand for AI, machine learning and NLP applications, a tremendous amount of data is being pulled into big data platforms at massive scale. And, as the volume of data grows rapidly, organizations are increasingly seeking intelligent information discovery and analytics platforms. Navigating a traditional SQL database or typing in keywords is no longer enough!
In recent years, we've seen a shift toward search engines becoming the preferred mechanism for the output and delivery of complex big data applications. Examples include visualization dashboards and question-answering systems. In 2017, we stepped into a new era where search engines have become an integral part of big data, providing natural, total and proactive search and insight discovery.
New Technologies for Better Big Data
Artificial intelligence has been around for a long time, but for many enterprises seeking to extract more meaning from their data, the future is now. A recent Gartner report finds that expectations will soar for AI-enabled assistance as it becomes pervasive in consumer services and customer/citizen-facing applications using virtual support assistants. And, according to this report, AI and machine learning investments are already in the top five CIO priorities.
We're also seeing natural language processing, an enabler of AI, becoming an essential technology to many new business functions, from chatbots and digital assistants (think Alexa and Siri) to compliance monitoring, business intelligence and analytics. And with all the unstructured and structured content that exists, such as emails, video and social media content, NLP tools and technologies can bring a business significant insights by helping to process, analyze and understand all this data. The result is a business that can operate more effectively and proactively.
The Path Is Not Easy
As we move into 2018, although the pace of adoption continues to increase within enterprises, keep in mind that for most companies, these technologies are still novelties. Even within organizations that have AI-enabled applications in place, rigorous testing and gradual improvements are more common than pushing for the next innovative features and total reliance on a new system. While Gartner projects that AI implementations are a top priority for CIOs, we are only at the beginning. There's a lot to overcome, and these advanced technologies are not suited for every business.
Ask yourself if your systems and people are ready to support and adopt these new technologies. For example, in the case of chatbots you'll want to determine if your business systems are ready to integrate with them and if most of your data is commonly understood. You'll also want to consider what languages the data is in, and if a user interface is ready or planned. And, most importantly, for these advanced technologies, do you have the necessary resources (internal and external) that will be required to implement them and support high performance?
While many challenges still exist, applying these new technologies now will undoubtedly bring a competitive advantage. And as data grows and AI-enabled technologies accelerate, the most forward-thinking businesses will need to address an emerging concern: AI systems that produce false or biased results could adversely impact business outcomes and reputation. In fact, recent reports, including the one from Gartner and Fjords Trends 2018, have placed an emphasis on "trust in the digital world."
And as Fjord Trends suggests, a developing approach to ensure trust and transparency in data is blockchain. Blockchain addresses the lack of transparency by securing records of information and making it resistant to modifications, unless there's consensus. Blockchain is still in its early days, but it has the potential to be a major disruptor across all industries and areas of business.
Getting there won't be easy, and there is a lot to consider and learn about what is right for your enterprise and what might not make sense. But we do know that as businesses become more data-driven, applying search and big data analytics will bring the intelligence and AI capabilities that are truly transformative to the way they innovate and compete.