Another transformative technology, machine learning or deep learning, is providing companies with “the ability to learn things from that data in a way that could never be accomplished by humans,” Flannagan continued. For his part, Bill Schmarzo, CTO for the big data practice of Dell EMC Services, sees machine learning as the key to “helping organizations to uncover new customer, product, and operational insights buried in the growing body of transactional, web, social, mobile, wearables, and IoT datasets.”
Of all emerging technologies, “machine learning has, perhaps, the most transformative potential,” according to Nevala. “Machine learning algorithms are super-charging traditional analytic applications, including risk and fraud detection and hyper-personalized, real-time marketing. Machine learning also underpins both emerging cognitive computing and so-called artificial intelligence applications. Equally important, machine learning may play a pivotal role in the future of data management. It provides the means to interrogate non-traditional information sources, including text, audio, and video. Machine learning is also being applied to support the ongoing curation of data sources in, what is for now, a semi-automated fashion.”
Still, artificial intelligence (AI) itself is only in the earliest stages, Seth Dobrin, vice president and chief data officer for IBM Analytics, pointed out. “We’ve barely scraped the surface when it comes to the potential of artificial intelligence. How do we implement AI in a secure way? How do we maximize the use of AI leveraging the cloud? How do we connect remote locations, cloud, or on-premises, with the right telecom infrastructure globally? These are the questions business leaders must ask to realize the potential of AI.”
Mighael Botha, CTO of Software AG North America, pointed to modern architecture approaches such as microservices and event streaming having a profound impact on how we generate, consume, store, and view data, and enabling new approaches to data. With machine learning, it is now possible to put all this data to good use by continuously improving the types of questions the business side asks and the answers that are received by refining what we are learning every second of the day, he noted.
Blockchain is another technology initiative that is likely to make a difference, Schmarzo added. “It has the ability to fundamentally change the nature of transactions between sellers and buyers, and could unleash creative ways to leverage data and analytics to power new business and customer engagement models.” Combined with machine learning, the new technology “could provide the catalyst for disrupting business models and disrupting existing customer relationships.”
It doesn’t stop there. New modes of data storage also are enabling this advance. Alternative storage mechanisms—NoSQL databases, Hadoop, Casandra, and MongoDB—are “revolutionizing the way we store and process data,” said Botha.
IT automation is another important enabler of the data-driven business. IT automation solutions have pre-built and tested logic to deal with processes associated with structured and unstructured data, which saves time and reduces the number of errors that come from relying solely on custom scripts, said Mehul Amin, director of engineering for Advanced Systems Concepts, Inc. (ASCI). For example, IT automation can make it possible to handle and integrate Hadoop components such as Hive, Spark, and Sqoop into existing applications and technologies, as well as supplement scripts with pre-coded logic and actions. “This makes it possible to quickly get the right information in front of the right decision makers,” Amin noted.
There is also progress on the hardware side, as well. For example, “high-performance all-flash storage ensures that analytics tools can access data quickly,” related Michael Elliott, cloud evangelist at NetApp. “Enterprises should also evaluate converged and hyper-converged infrastructure solutions which build on the ability of IT to more simply deliver predictable performance, speed up time to market, share, and integrate hardware resources, and integrate solution vendor support.”
The ability to not only manage but move data will make the difference between a data-driven and a data-dragging enterprise. “One of the main issues that companies have in their effort to become data-driven is the fact that so much data analysis is done on separate systems using separate copies of the data,” said Tim Willging, distinguished engineer with Rocket Software. “This data copying or movement imposes a great cost and introduces latency of the data being analyzed. The bottom-line is that you can’t expect real-time analytics if you’re taking data off of your main system—no matter what you’re using—to crunch the numbers.”
Technology, of course, weighs heavily on data transformation efforts and gives rise to the question of whether enterprises need to overhaul or replace existing infrastructure, or can they build on what they have. Industry observers agree that wholesale replacement of existing infrastructure, tools, and techniques is not only impractical, but completely unnecessary. Recognize that “the best outcomes will be not from innovative technologies in a silo, but from innovative technologies working in concert with existing data and systems,” said Flannagan.