Did you miss a session at the Data Summit? As data becomes the new oil, organizations across sectors are racing to leverage their (often siloed) information management systems and drive value across functions with viable AI applications. The need has surged the demand for competent data scientists and analytics professionals, but even with the proper talent, bringing data-driven products to life can be quite a task. According to two Gartner reports, just 53% of AI and ML projects make it to production and 85% of the lot typically fail to deliver on their intended results. Despite the challenges, including data quality issues, Gap, which is one of the biggest clothing and accessories retailers in the world, has come out with the successful implementation of data and AI to solve key business problems.

By Vikram Labhe It is a truism at this point to talk of the centrality of data for organisations. According to IDC, the global datasphere will rise at a compound annual growth rate (CAGR) of 23% between 2020-2025, highlighting the importance of responding to the surge in storage demand. For businesses to leverage data insights and drive growth, they must coordinate the dependencies and execute the different tasks on their data journey in the desired order, all while ensuring minimal impact from potential errors. Whether an organisation favours extract, transform, load (ETL) or extract, load, transform (ELT) will depend on their specific needs. Orchestration is fundamental for modern data processes, but for many businesses a modern data stack makes specific orchestration tools redundant.

It's difficult to go online these days and not be inundated with uses of the word "data." Common variations include big data, data security, data privacy, data analytics, data science, data camp, data fraud, database and many, many more. Then there are the statistics about the ever-increasing quantities and uses of data, such as that90% of all data was created in just the last two years. All of this can feel a bit data-whelming, and unfortunately many tech companies tend to exacerbate the problem by piling an endless stream of jargon into the mix (clouds, lakes, AI/ML, RESTful, etc.). Both of the above are cyclical in nature, meaning they feed off each other.

The importance of data to today's businesses can't be overstated. Studies show data-driven companies are 58% more likely to beat revenue goals than non-data-driven companies and 162% more likely to significantly outperform laggards. Data analytics are helping nearly half of all companies make better decisions about everything, from the products they deliver to the markets they target. Data is becoming critical in every industry, whether it's helping farms increase the value of the crops they produce or fundamentally changing the game of basketball. Used optimally, data is nothing less than a critically important asset. Problem is, it's not always easy to put data to work. The Seagate Rethink Data report, with research and analysis by IDC, found that only 32% of the data available to enterprises is ever used and the remaining 68% goes unleveraged.

Laura Timms, Product Strategy Manager at MHR Analytics gives us a breakdown of the future effects of artificial intelligence on the human resources space. According to a recent survey, 82% of HR leaders believe their roles will be completely different in a decade's time. Big things are happening, with Artificial Intelligence (AI) taking a starring role. More than a third of the 500 companies we recently polled said they had adopted some form of AI in the past year, and almost half of the HR leaders we surveyed said that machine learning – a form of AI – will improve their HR function. AI is already being put to work in key areas such as recruitment, onboarding and employee development.

Augmented reality (AR) is nothing new. Ask anyone and they will surely remember last year's Pokémon GO craze, which engaged thousands of people globally in a gamified mixed-reality experience. And the AR hype is not going anywhere – with innovations such as the recent release of Apple ARKit in the iOS 11 software update, mainstream adoption of AR is expected to continue growing exponentially. The beauty of AR is its inherent ability to extend beyond what actually is and visually depict what could be. This gives app developers endless new opportunities to engage consumers in a plethora of conceptual possibilities, illustrating stories that intermingle and interact with the environment around us.

In 2008 we were looking for methods to enhance our capability of monitoring the vibration of Generating Turbines through dynamic speed ranges. To solve this problem, we created an anomaly detection algorithm that was generic, scalable, and easy to use for non-data scientists and could take in many more inputs than just vibration. So, we plugged it into the company PI system and SpheriCAL was born. Over the years we have applied SpheriCAL all sorts of assets, across all the main generation technologies: CCGT, OCGT, Coal, Hydro, Wind, and even Solar. We have increased our capabilities adding more machine learning and developing our SMART systems, culminating in the Virtual Analyst.