The Industrial Data Problem? Poor Visibility

The impact of AI on big data is not theoretical


Big data is a single term used to describe two very different activities. The first is collecting and storing vast amounts of data. The second is mining and analyzing that data for relevant insights. Unfortunately, companies are struggling to manage both at once.

Over the past 20 years, more than 6 billion devices were connected to Internet of Things sensors, producing 2.5 quintillion bytes of data on a daily basis. By 2017, the total number of connected devices surged to 8.4 billion according to Gartner, with adoption rates climbing quickly. And thanks to the IoT revolution, users are able to easily collect data broadly and deeply.

As data sets become larger, they are more indicative of trends and patterns. Consequently, the sets also become harder to analyze. The volume of data now in play dwarfs any notion of human scalability. It is estimated that organizations will need 1 million data scientists by 2018 to bridge the big data supply and demand gap. Furthermore, the sheer complexity of that data makes relevant insights particularly elusive.

Forbes reports that industrial data volumes are exploding at a record rate, with more data being created in the past two years than in all of human history. With the size of data in play expanding exponentially, it's important to understand why data initiatives struggle so much to scale.

Poor visibility is at the forefront of the problem. As data sets grow, they become not only larger, but also more complex and less organized. Anyone searching for insights has to proceed almost at random. Companies are lucky to locate anything of value. And even when they do, the time and resources necessary significantly limit the strategic impact.

The most common response to this problem is to recruit a high volume of the best and brightest data scientists. But even a company willing to take on this massive new labor cost will end up frustrated. The competition for qualified candidates is fierce, and the demand is far greater than the supply. There are simply not enough data scientists on earth to manage the volume of data currently in storage.

With this in mind, the companies making the most of industrial big data recognize the need to look for smarter solutions rather than more staff. By integrating a vast data set with machine learning and artificial intelligence tools, it's possible to automate and accelerate the discovery process.

Even if a data set contains trillions of nodes of data, AI can comb through each one systematically. The ability of these tools to perform instantaneous comparisons and analyses means critical signals are discovered sooner and with more certainty. With machine learning capabilities built in, as well, data analysis becomes more effective as data volumes grow.

The implications of this from a management perspective are huge. The herculean task of data analysis becomes easier and more effective. But the real benefit is from a strategic perspective: Machine learning and AI transform data analysis from a reactive to a predictive, cognitive process. With this approach, previously invisible problems can be avoided in real time and in real-world settings.

Once this capability is in place, companies in industries ranging from manufacturing to oil and gas can gain substantial new strengths. AI is able to sift through real-time performance data to locate inefficiencies in processes or equipment. It streamlines operations in a way that cuts operational and labor costs. Also, it allows companies to mitigate issues and seize strategic opportunities with confidence.

In addition, the impact of AI on big data is not theoretical. According to McKinsey & Company, when this capability is applied to just the field of predictive maintenance, it will save companies $630 billion by 2025. When the economic impact is extrapolated across industries and use cases, it reaches into the multitrillions.

There is also reason to believe that estimates are undervaluing AI analysis. Because these tools streamline the analysis process, they democratize it as well. Users no longer have to possess a data science background to start leveraging data in meaningful ways. This capability will impact every company differently, but the most ambitious will realize that this is an opportunity to conduct every workflow more effectively.

Big data, AI, and machine learning are all powerful on their own. But when they are combined, the sum becomes much greater than the parts.

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