The goal of collecting data is no longer simply to have it as a record of past performance. Forward-thinking companies are increasingly aiming to monetize their data by harnessing the power of the latest analytics tools. At some point, companies must aspire to transition from collecting and organizing data to monetizing it if they want to acquire the benefits of better business outcomes. Examples include uncovering new revenue streams, tightening operational expenditures, reducing customer churn and more.
Data monetization involves solving actual problems by turning to insights derived from structured data. Information Week quotes one industry expert on this topic: “You have to look at the problem you’re trying to solve, whether that’s expanding into a new market, trying to leverage more from existing customers, or acquiring new customers or employees. Then you can start looking at how data plays into to that.”
Potential roadblocks to monetizing data include:
- Issues with data quality.
- Siloed data protocols.
- Legacy business intelligence systems with limited capabilities.
- Lack of compliance and cyber security infrastructure.
- Lack of support or involvement from leadership.
- A corporate culture that lacks data literacy skills.
While the industry has been promising self-service analytics to enable this data monetization, previous efforts fell flat. Fortunately, with the rise of AI-driven analytics, data monetization is more attainable than ever. Continue reading to get knowledge more about the ins and outs of going further with AI-driven analytics.
Business analytics has been continually evolving to give end users more control over their experience. Specifically, self-service analytics have empowered employees and partners across organizations to work directly with structured data rather than relying on IT or data specialists to act as gateway keepers. When companies implement self-service business intelligence solutions, they eliminate “the middleman,” so to speak. The result is better accessibility throughout an organization, which in turn fuels speedier and more informed business decisions.
In other words, self-service analytics boosts data democratization by empowering users to ask questions of company data directly. These employees can then use their findings to create reports and, more importantly, make savvy decisions.
Being able to query stored company data, especially from multiple sources, is a vital step toward monetizing data. But these queries are focused; users must know what they’re seeking from the outset. This begs the question: What data insights are lurking under the surface? What if business users could uncover the answers to questions they didn’t even know to ask?
The Advantages of AI
The latest data-driven analytics solutions utilize artificial intelligence (AI) algorithms to uncover trends, anomalies, causal relationships and more within data. This technology has the capability of running many queries on billions of data rows with dozens of algorithms—something that would take human data specialists many more hours to accomplish, and understandably so.
One of the most innovative aspects of AI-driven data analytics is its use of machine-learning algorithms to continually fine-tune the querying process by “learning” what’s relevant and what is not. Business users can “train” these algorithms to perform in a useful manner over time by offering simple feedback about the insights they receive. For many, especially those wary about a potential lack of transparency and oversight in AI systems, this is reassuring as it ensures human users stay in the loop and play an active role.
Monetizing data is a matter of using insights to make important decisions. The value of AI-driven analytics is that it makes it easy to uncover automated insights based on vast amounts of data—all in seconds. Business users can then leverage these insights to meet company goals like boosting revenue, reducing expenditures and improving certain processes.