One thing is certain - we're all producing more data than we know what to do with. By some estimates, it's as much as 2.5 quintillion bytes of data each day from sources like phones, social media and 'Internet of Things' devices, and much of that is "dark data" - unstructured and largely ignored.
But there are growing efforts to change that. Companies are increasingly exploring and analyzing dark data for insights, and they're standardizing organization-wide analytics capabilities and acquiring skills from CDOs and data scientists to learn how to put data to good use.
Here are three ways that companies are putting dark data to good use.
1. Commercializing Data insights
Beyond using data to make more informed decisions, new research shows that companies are starting to monetize data insights, with 71% of organizations reporting that they're creating new forms of economic value and 42% reporting that these data insights will be a significant contributor of revenue in the next three years. IoT apps alone could generate as much as 507 zetabytes of data by 2019 - that's huge revenue potential.
2. Increasing Cognitive Capabilities
Advanced analytics have given way to cognitive computing - the use of adaptive, self-learning systems to garner intelligent recommendations. It's the next logical step in the analytics journey especially for companies looking to differentiate how they use data and derive data assets.
The global cognitive computing market is estimated to grow to $12 billion by 2019, representing a significant opportunity for companies in the cognitive space to provide analytics automation and APIs for quick development and to make a pervasive push for cognitive within organizational processes. Cognitive may not be for everyone though, and the challenge for many companies will be to figure out when cognitive makes sense and where to begin piloting initiatives that can take advantage of advanced real-time predictive and prescriptive analytics.
3. Infusing Emotion into Artificial Intelligence
For those further along the adoption curve, the next logical leap may be affective computing - systems that can interpret and simulate human emotions. Also projected to be a huge market, growing to $42 billion by 2020, affective capabilities tap into a huge pool of dark data that has yet to be explored - images, video and audio.
Pepper, a lovable cognitive robot, is already showing how aspects of affective capabilities can be valuable in conjunction with machine learning and artificial intelligence, not just to listen and talk but to understand the emotional element in human conversation and respond with context sensitivity. This can be applied directly in areas like customer service or health services.
With these advanced analytics capabilities, bringing dark data into the light becomes easier and more meaningful.
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