Big Community: What is the sixth wave of automation and their implications for businesses?
Amr Awadallah: The sixth wave of automation is the automation of decisions. Through Machine Learning (ML), data and analytics, we can create algorithms to learn how humans make decisions and in turn, automate and repeat the decision-making process to make it more efficient.
The ability to automate decisions can empower organisations to be more efficient, productive and help them to lower costs. For instance, ML can mimic how a lawyer analyses a document, how a doctor diagnoses a disease, or how an engineer predicts when machinery is going to fail. These decisions can all be automated to deliver quicker and more accurate results.
Recently, Komatsu Mining Corporation deployed Cloudera’s solutions on its JoySmart platform, an Industrial Internet of Things (IIoT) based service, to ingest, store and process a wide variety of data from mining equipment operating around the globe. Leveraging Cloudera’s technology, they gained a more complete picture of the machines’ health and operations, enabling them to identify ways to improve equipment safety, productivity, reduce operation costs and even design the next generation of mining equipment. This helped Komatsu to achieve their goal of helping customers enhance the safety, productivity and cost efficiency of their mining operations – this is a perfect example of how the sixth wave of automation will advance business operations today.
Big Community: What are some of the best practices and tips for organisations on handling the big data problems of today?
Amr Awadallah: Many firms that have yet to begin their big data journey will want to jump the gun and get started right away. The golden rule that we advise all our customers to follow is to pick three rapid use cases to start with, including one around cost reduction and one new data source – this can be an existing source that your organisation has yet to take advantage due to data volumes, velocity or variety – or ideally something completely new like social media. Once you have this new structured or unstructured source, blend it together with others (a process often called “data wrangling”) and you will be well on your way to leveraging big data as a big asset.
On the other hand, organisations that have already progressed on their big data journey are now challenged to manage the rapidly growing volumes of data, especially in an environment such as the cloud. Business leaders need to keep in mind that this mountain of data being collected on a daily basis can be a gold rush of opportunities if it is mined and analysed effectively. Many enterprises face challenges in analytic environments that have resulted in constraints for both business analysts and the IT teams. Constraints on resources, data silos, difficulties in applying security policies and implementing proper governance are just a few implications that businesses are facing today. At Cloudera, we understand that collaboration across an organisation is key, and that is why we recently announced Cloudera Altus Analytic DB to bring the warehouse to the data. By providing the flexibility and power to a cloud-based analytic database, businesses can now have access to instant analytics and data consistency all without moving the data. This simplifies IT management, reinstates security and eliminates complex and costly data movement altogether.
Big Community: What are some of the potential pitfalls and mistakes companies make when using machine learning and advanced analytics in the cloud?
Amr Awadallah: The biggest mistake that we see CIOs make is trying to boil the ocean. Machine Learning should not be deployed just for the sake of deploying it and many CIOs end up restructuring their entire information architecture with no clear direction as to what they want to achieve. Technology is always the means to an end and CIOs should keep the end goal in mind when implementing new technology. For example, if business leaders are trying to predict customer satisfaction levels or the well-being of a patient, they can deploy to ML to enable a quick and effective automated decisions that will drive a business outcome. The key is to start with customers in mind, and then work backwards from there. Serving as a case in point, one of Cloudera’s customers, Globe Telecom, has 60 million subscribers in the Philippines and experienced a mobile data traffic growth of 85 percent in just a year. This propelled them to invest in Cloudera’s big data solutions, powered by Machine Learning, to harness the large amounts of data into one centralised location and apply real-time analytics to gain valuable insights to deliver targeted marketing campaigns and offers that enhance customers’ experiences.
Big Community: What do you think are the key trends for the big data market in 2018?
Amr Awadallah: Organisations have started to become more technical over the years with the adoption of new technologies that drive efficiency, cost effectiveness and revenue growth. This will have a great impact on the future of the workforce in the next ten years. We are starting to see organisations employ data scientists for almost every department of their business, but there is a constraint on talents as it is a fairly new technology. In 2018, we will see businesses invest more in their existing human resources to equip them with the skillsets needed for a ML-driven economy. Companies will take a three-fold approach: education and training; tools that makes it easier to work and adopt this new technology; and a rich partner ecosystem of consultants and system integrators that help to smoothen the entire process.
Big Community: Tell us about Cloudera’s growth since its days as a unicorn start-up and its plans for the future.
Amr Awadallah: We launched Cloudera in the U.S. in 2008 with four co-founders including myself and today, we have 1600 employees worldwide. Our growth has been rapid, and we started to invest in the Asia Pacific region just three years ago. Now, APAC is our fastest growing region and we have capitalised on this growth by expanding our operations in Indonesia, and most recently moved to a bigger headquarter office in Singapore.
We first developed our products to provide organisations with a data analytics platform for the connected world, helping them to predict business outcomes, drive customer satisfaction and revenue growth. Since then, we have really transformed the way we talk about the company and the business. Now we are predominantly focused on ML, data science and analytics that are optimised for the cloud, which was a pronounced change and a precursor to us becoming a publicly traded company. Prior to the IPO, we also made several acquisitions in the data science and ML realm, including Sensedata.IO which led to the development of the Cloudera Data Science Workbench – our fastest growing product. We also wanted to complement this product with data science talents and we did so through the acquisition of Fast Forward Labs.
Today, Cloudera is ranked as one of the fastest growing companies in North America on Deloitte’s Technology Fast 500 and we continue to enhance our products with our customers in at the core of our business strategy. We will continue to innovate our technology to deliver solutions that are imperative in the age of digital transformation, and help customers turn what is impossible today, possible tomorrow.
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