Merkle is a leading data-driven, technology-enabled, global performance marketing agency that specialises in the delivery of unique, personalised customer experiences across platforms and devices. We call it'people-based' marketing, and with over 25 years' experience, we are proud to be recognised as a global leader. Merkle's heritage in data, technology and analytics is the foundation for our understanding of consumer insights that drives our people-based marketing strategies. Combined with our expertise in performance creative and media, we can then offer our clients content-driven, contextual and compelling customer experiences that drive business growth. In 2016, the agency joined dentsu, one of the world's biggest media companies to form the Customer Experience Management (CXM) Line of Business.

As part of enterprise artificial intelligence (AI) initiatives, data engineering teams are using a wide range of data analytics techniques, ranging from streaming analytics to machine learning to deep learning. This diversity in techniques has led to a corresponding diversity in software platforms and tools. Most data engineering teams are using data ingestion frameworks, such as Kafka; a combination of machine learning tools, such as Hadoop, Splunk, SAS Analytics, Spark, Python and R; and open-source deep learning packages, such as TensorFlow, Caffe and PyTorch. In traditional data analytics pipelines, data flows into enterprise environments from various internal and external sources and gets pre-processed and cleansed. Enterprises commonly use a "staging area" to store intermediate representations of pre-processed data.

As part of enterprise artificial intelligence (AI) initiatives, data engineering teams are using a wide range of data analytics techniques, ranging from streaming analytics to machine learning to deep learning. This diversity in techniques has led to a corresponding diversity in software platforms and tools. Most data engineering teams are using data ingestion frameworks, such as Kafka; a combination of machine learning tools, such as Hadoop, Splunk, SAS Analytics, Spark, Python and R; and open-source deep learning packages, such as TensorFlow, Caffe and PyTorch. In traditional data analytics pipelines, data flows into enterprise environments from various internal and external sources and gets pre-processed and cleansed. Enterprises commonly use a "staging area" to store intermediate representations of pre-processed data.

The hype around AI is justified by its transformative potential for organizations of all sizes right from SMEs to large enterprises to governments. While its ability to automate and hence reduce costs is well understood, AI's real potential comes from being able to increase the top line through enabling innovation and improving employee efficiency. According to Gartner, 70 percent of organizations will integrate AI to assist employees' productivity by 2021. And more and more companies are using AI to shorten the innovation cycle, for instance in drug discovery. And yet, according to Gartner, 85 percent of AI projects won't deliver for CIOs.