As data analytics developed over time, a self-service model arose. That is, instead of relying exclusively on teams of data and IT specialists within a company to work with data, suddenly business users at every level could perform their own analytics. This development streamlined how organizations handle data queries, speeding up the process and empowering employees to take an active role in their own data pursuits. Overall, self-service analytics tends to deliver a better return on investment and boost accessibility across a professional ecosystem.
But in one survey of 500 executives, more than 85 percent acknowledged they were only “somewhat effective” at meeting their data and analytics goals. Getting the most out of data analytics looks different now than it did just a few years ago. Now, humans and machines are working together more closely than ever to extract the maximum value from structured data. Artificial intelligence (AI) and machine learning (ML) are two major contributors in this latest era of data analytics.
AI simply refers to technology performing tasks usually requiring human intelligence—in this case, data analysis. Machine learning is one application of AI and refers to computers learning how to act over time without requiring programming from humans. That’s not to say that humans cannot offer feedback to help ML algorithms refine their processes, but the point is that the system itself is capable of learning over time. This ultimately leads to more relevant insights that business users can act on to drive savvier decision-making.
Utilizing AI-driven recommendations
What advantages does AI data analysis offer an organization? The ability to create reports and data visualization models is foundational to any solid data strategy, to be sure. However, AI can go beyond this ability to actively inform decision-making. As ZDNet notes, “Data analysis can be used to deliver recommended next steps or even to automate actions sure to lead to desired outcomes” in areas like marketing, sales, HR, supply chain management, logistics and more.
Generating natural language narratives
Data is only as useful as it is clear and contextualized. Even the most advanced analytics platform would be next to useless if business users couldn’t quickly and accurately interpret results. That’s where natural language generation (NLG) enters the picture. NLG is the link between machines and people in terms of converting insights into understandable and usable statements, as “[it] takes facts that machines can understand and turns them into a language that humans can process and act upon.”
Without NLG, data insights risk getting lost in translation between AI algorithms and human users. Whether insights are pushed into existing chat applications or delivered via an analytics interface, NLG ensures that they are useful.
Uncovering insights faster
One of the major advantages of utilizing AI and ML in data analytics is that they drive speed to insight. Cutting down on the time it takes to analyze data in a meaningful way means the right people can act on the right insights faster. This is an especially crucial function in fast-paced industries like retail, manufacturing, healthcare and more. With the right AI data analytics platform, human users can uncover insights in seconds—a feat that would take teams of human analysts hours (or more) to complete. This leads to faster, more decisive action where it counts.
What does AI add to your data analysis strategy? Above all: Speed and actionable insights in a language well suited to human users. AI and ML applications in data analytics are ushering in a new, data-driven era—one with a goal of better business outcomes across industries.
