Artificial intelligence () technology is already revolutionizing certain workflows, changing how businesses approach previously manual processes. Data analytics is no exception. Using -driven data analytics tools, business users across an organization can now click to automatically uncover insights. Instead of having to outsource this time-consuming task to teams of data analysts, insight-detection algorithms are capable of querying large quantities of data at once to uncover insights.
It’s a win-win! Data teams can then work on higher-level tasks, while end users receive useful insights in plain language that they can use to fuel more informed decision making. But implementing these tools requires an initial investment. So, it only makes sense companies want to ensure they’re getting a good return on this investment (ROI).
It pays to ask: Is adding value to your business? Let’s take a closer look.
Getting Value from -Powered Data Analytics
Data analytics is moving increasingly toward becoming self-service because it’s advantageous when business users have direct access to the tools they need to query data. Tools like a relational search engine allow employees to enter queries and receive answers to their most pressing questions. However, this still leaves plenty of insights behind—although they’re not specifically sought, these trends, patterns, relationships, and anomalies could still be extremely useful in shaping decision-making and driving more favorable business outcomes.
According to research from McKinsey Global Institute, “ and deep neural networks improved performance beyond what existing analytic techniques were able to deliver” in 69 percent of use cases. algorithms are capable of diving deep into data, analyzing multiple sources and millions of rows of data or more for anything that may be “interesting” to business users. Machine-learning algorithms also refine this technique over time based on human feedback, so businesses get legitimately relevant and useful insights rather than extraneous suggestions.
Overcoming Hurdles to Data Monetization
When you put it like that, it seems like a no-brainer to include in your company’s approach to data analytics. However, like all emerging technology, there are certain challenges to address and missteps to avoid if you want to actually derive value from your analytics.
Consider these hurdles when you’re assessing the value of in business, particularly as it relates to analytics and business intelligence.
Lack of Context for Business Users
On one hand, the fact that business users don’t need technical knowledge about data is an advantage because it frees up many more people to use analytics tools. On the other hand, as ZDNet writes: “Algorithmic transparency is challenging.” It’s important to choose a solution with transparency—users should be able to track data back to the source so they can understand insights and contextualize them using source information. This will boost trust as employees get used to working with -powered software. […]