Companies around the world are investing tens of billions of dollars on artificial intelligence (AI) and machine learning (ML), and for good reason. These technologies have real business-altering potential, and that’s why Gartner’s "Enter the Age of Analytics" report predicts that by 2023, AI and deep-learning techniques will be the two most common approaches for new applications of data science.
Effective use of both AI and ML in business production use cases can help enterprises that use them jump far ahead of competitors in their sectors, because the technologies remove friction that gums up processes. But despite this promise, few companies have been able to successfully implement and deploy this technology as part of their overall data and analytics strategy. According to Gartner, 46 percent of CIOs have developed plans to deploy AI, but just 4 percent have made the concept a reality.
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The truth is that it will take years before many organizations realize the true potential of AI and ML, but it is never too early to lay the groundwork now for an AI-driven future. In fact, if an organization is not already thinking about what an AI strategy looks like, its competition is likely one step ahead. There’s no time to waste, so here are five important points to consider when getting started with AI and ML.
Industry information for this eWEEK Data Points article is provided by Ashley Kramer, Senior Vice President of Product at Alteryx. Alteryx claims that its product suite is the only platform in the market that simultaneously addresses the requirements of the data analyst, data scientist and citizen data scientist.
Data Point No. 1: Ask the right questions
There are four things organizations need to be thinking about when it comes to a future-proof data strategy. What data is available within the walls of my organization? What data do we need to acquire externally to drive differentiation? Is our data available in a way that can be readily available for machine learning and AI? And perhaps most importantly: Where can we upskill our line-of-business, what requires pure data science and AI know-how, and what can IT manage? The answer to these questions should serve as the foundation to your strategy.
Data Point No. 2: Take a multi-year approach
Successful AI/ML implementation does not happen overnight. The smartest organizations take a multi-year approach to data acquisition and strategy, focused on compiling data from different sources and silos—often built around a Center of Excellence (CoE)—and investing in the right technologies and people to lay the foundation. At the same time, these organizations look to cloud-based offerings from companies like Amazon, Microsoft and others to create intermediate data storage that can support diverse use cases as strategies progress over time.
Data Point No. 3: Always put humans at the center of the strategy
A recent study from ZipRecruiter found that “the most successful applications of AI have been when used in partnership with humans, rather than as a replacement.” That’s why, according to the study, AI has created three times as many jobs as it killed last year—and companies are continuing to invest in talent with data skills despite the advancement of automation technologies. The World Economic Forum predicts that data-related jobs will be the most in demand within the next four to five years, along with AI and ML specialists.
Data Point No. 4: Build a multidisciplinary team
A diverse team that incorporates AI experts, data scientists and line-of-business analysts presents a more holistic approach to AI/ML, as the overall project encompasses the data collection process all the way to the data mining activities, machine learning and automation. Those who are able to engage with the data gathering, processing and training will be able to optimize their contribution to their organizations, and seriously enhance their individual or corporate ability to achieve goals.
Data Point No. 5: Bridge the skills gaps
There is increased demand for any data worker, regardless of technical acumen, to do more with data, and organizations need to look for ways to up-level skillsets, build models in understandable and transparent ways and generally bridge the skills gaps across the organization. Since AI data design requires “data speak” to help build workflows, organizations must implement technologies such as augmented analytics that automate data prep, insight discovery and data science (i.e. autoML) all while communicating actions to roles with less AI know-how.
Data Point No. 6: Looking ahead
Artificial intelligence and machine learning undoubtedly will shake up the business world and life as we know it in years to come, and organizations need to empower each and every human member of their business to be thinking about how to leverage the technology. No matter how AI and ML evolves, data will always be at the forefront, and one of the most important drivers of success and true digital disruption. A smart approach to data now will guide the way for a successful AI-driven future.
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