As you have probably heard or read, IBM's Marketing Cloud recently published that "90% of world's data today has been created in the last two years alone." Growing daily at 2.5 quintillion bytes of data daily, this number will only explode over the next few years. This may seem impressive, but much of it is simply raw data. Nonetheless, you may point out that with all this data we are advancing technology, improving outcomes, enriching lives and making better decisions. However, how vastly improved these outcomes could be if all this data was enriched?

It's a simple concept and many large enterprises will recognize the power of blending behavioral data with transactional and CRM data. Some may have already landed log files into their big data environment (whether that's GCP, AWS, Azure, Cloudera, or on-premise Hadoop), however it's unlocking the hidden behavioural context in this data which remains the biggest challenge. Being able to create a unified schema and ID graph as well as blend all sources in the privacy and security of your existing architecture is where SYNTASA will start to add value fast - by speeding up the process of transforming data into real-time intelligence and ensuring this remains robust and scalable. Next, applying AI Assisted use case models allows you to create your own actionable intelligence hub. These use cases are limitless but often start with: propensity scoring, content/product recommendations, call centre deflection, multi-channel attribution, and omni-channel customer journey analytics.

As you have probably heard or read, IBM's Marketing Cloud recently published that "90% of world's data today has been created in the last two years alone." Growing daily at 2.5 quintillion bytes of data daily, this number will only explode over the next few years. This may seem impressive, but much of it is simply raw data. Nonetheless, you may point out that with all this data we are advancing technology, improving outcomes, enriching lives and making better decisions. However, how vastly improved these outcomes could be if all this data was enriched?

The concept of machine learning has been around for decades, primarily in academia. Along the way it has taken various forms and adopted various terminologies, including pattern recognition, artificial intelligence, knowledge management, computational statistics, etc. Regardless of terminology, machine learning enables computers to learn on their own without being explicitly programmed for specific tasks. Through the use of algorithms, computers are able to read sample input data, build models and make predictions and decisions based on new data. This concept is particularly powerful when the set of input data is highly variable and static programming instructions cannot handle such scenarios. In recent years, the proliferation of digital information through social media, the Internet of Things (IoT) and e-commerce, combined with accessibility to economical compute power, has enabled machine learning to move into the mainstream.