Enterprise data used to be simple. Number of units sold, regions where sales were higher, cost of production compared to revenue, etc.These days, though, data is more complicated—not just in what data is available to organizations, but also the sheer amount of it. The proliferation of connected devices has created an explosion in unstructured data, and smart organizations are putting all this new information to work.
These organizations are unlocking their data capital. And more importantly, they’re turning that data capital into actual capital. How? Through advanced analytics—specifically, artificial intelligence (AI) and machine learning (ML).
The great incubators
Data at rest is just a sea of ones and zeros with no real purpose. It just takes up space.
While most organizations have invested heavily in hardware and platforms to create that space, they have yet to figure out an actual use for a lot of the data they’re collecting.
Apply AI and ML models to this resource, however, and over time your unused data starts to reveal new and unexpected insights.
Previously invisible correlations between data sets can be unearthed, and many of these correlations can lead to new products, greater efficiencies, and new opportunities for revenue streams.
AI and ML also help organizations achieve two goals that often separate companies that thrive from those that fail: better customer service and the ability to make smarter decisions quickly.
Examples of AI and ML at work
Despite being relatively young technologies, AI and ML are already being put to work by organizations across industries.
- AI chatbots are being deployed for customer service, providing organizations with a way to answer customer questions and address problems without the need for always available support
- ML powered by sophisticated algorithms is able to examine massive amounts of data in order to identify and flag potential instances of suspicious activity, such as attempted purchases made with stolen credit card numbers
- Retailers can use AI to connect customers to new products and services they may be interested in based on their previous purchases, search history, or customer product ratings
Even just having the capability to apply AI and ML can lead to new opportunities.
Nike, for instance, was able to develop a suite of mobile apps for customers to track things like their golf scores or the number of miles they run.
These apps not only inspired brand loyalty, but their use by customers also created new streams of data the company could use to guide their equipment and apparel lines.
From data capital to economic capital
The examples above are really just scratching the surface of AI and ML’s potential.
As models become more sophisticated and specific, the organizations that stay ahead of the competition will be the ones that are able to apply the findings of those models quickly and effectively.
If you want help ensuring your organization is one of them, click here to learn more about how Redapt can help you leverage AI and ML to transform your data capital and surge ahead of the market.
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