Game-Changing Technologies Fueling the Data-Driven Enterprise

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“Never advocate for a full rip and replace—it’s not feasible,” advised Abhi Yadav, co-founder and CEO of ZyloTech. “Organizations should look for technologies that integrate with what they already have and make their existing delivery or call to action tools more powerful.”

At the same time, industry observers suggest more solution innovation is needed and is on the way. But innovation doesn’t only apply to hardware and software. Dobrin, for one, suggests more innovation is needed in the data governance process, as data needs to be unified across an organization and acted upon in real-time. The challenge is the “tools to achieve this simply don’t exist yet.”

Organizations may even need to focus on the data they already have, versus worrying about acquiring new sources. “There are external forces like IoT that are supplying more raw data, but enterprises haven’t come close to exhausting their existing data assets,” said Pasqua. “No one needs to wait for new data. They can capitalize on what they have. Moreover, many companies don’t have systems that allow them to effectively translate their insights into actions. Having insights is great, but unless they can be operationalized, they aren’t of much value.”

Enterprises need to re-evaluate and redesign their operating models to be able to keep up in a data-driven world, said Berkey. “Technology is not the inhibitor of scale or speed—operating models are,” he observed. The goal of an operating model should be delivery of data-driven insights when and where they are needed. “Organizations can take insight-powered action at speed and scale, driving disruption through analytics. While as-a-service capabilities make this possible for enterprises migrating away from legacy infrastructure, the technology shifts must be accompanied by cultural shifts.”


Evolving into a data-driven enterprise requires more than just investments in infrastructures, new technologies, and new architectures, however. “Even with the best tools and technology, data are just ones and zeros—they do not magically generate wealth on their own,” said Renze. “Organizations need individuals with backgrounds in data science and data engineering in order to transform their data into actionable insights.”

The most prominent skill needed in today’s fast-changing data environment is adaptability. “The ability to be a lifelong learner who is open to new trends and technologies is priceless in this industry,” said Dobrin. “One must also be willing to fail and learn from mistakes made.” From data science teams to the C-suite, lifelong learners are the ones who will propel data-driven enterprises, he observed.

Still, the success of a data-driven enterprise will depend on data science skills. “Data scientists are essential to help train machine-learning models which can then be deployed to identify actions to be taken in real-time,” said Botha. “We’ll see enterprises requiring new skills in data analytics but also creating small agile teams to apply these insights,” added Alex Robbio, president and co-founder of Belatrix Software. “Many organizations are struggling to find employees with the experience and skills in new areas, such as machine learning; as a result, we expect to see organizations forming partnerships with companies that can help them with these technologies. It’s worth noting that machine learning and neural networks don’t only require new specific skills but also a different mindset, which is hard to train for.”

A renaissance type of individual, with a polymathic set of skills, is needed to move enterprises to the data-driven era. “Historically, data was analyzed in a reactive manner, and then recommendations were made based on incomplete data or data trends,” said Scott Gnau, CTO of Hortonworks. “Data engineers tasked with analyzing this data will have to shift how they view their job, and the value they bring to their company by being more proactive opposed to reactive. They need to be both scientists—think algorithms—as well as artists, finding patterns, expressing connections. They also need to be detectives, sleuthing for new relationships.”

Jason Andersen, vice president of business line management for Stratus Technologies, concurred, stating that he sees “a new type of role I like to call ‘hybrid OT.’” These professionals “will have the same job responsibilities as operational technologists do now, but will have a far more technical background, particularly, with digital technologies. They will be less dependent on IT and will take on a more futuristic role where they will be the ones driving innovation forward.” He sees a convergence between this role and that of data scientists, as well. “We can draw parallels to the role of application developers when the cloud disrupted their space. They evolved into a DevOps role where they would now code, build, design, and more—things that app developers were not doing previously. I think we can expect a similar reaction as data collection and analysis disrupts the enterprise.”

Still, other industry experts anticipate there will be continued demand for traditional data skills. “This is going to sound terribly unsexy, but I continue to believe the two most valuable skills for an analyst to have are SQL skills and a fluency in translating business problems into analytic questions,” said Daniel Mintz, chief data evangelist at Looker. “SQL—because it’s the lingua franca of data and it aligns so well with the way analysts think. And then bilingualism—in business and in data—because that’s the hardest thing to find. Without people who can speak both those languages, you end up chasing interesting problems that don’t deliver any value to the business. I’d much rather hire a SQL person who’s bilingual but doesn’t yet know how to train a neural network, than a machine-learning specialist who can’t clean their own data and has no idea what kinds of models would help the bottom line.”  

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