The seasons remind us that all environments change. When it comes to analytics, the season for technological change is becoming a stream of information to learn and for metrics to adjust from their initial roles in representing human activity.
Natural language processing, an active subfield of artificial intelligence (AI), is changing what professionals consider valuable analytics. Metrics were originally conceived as diagnostic criterions to website structure meant to represent people through interaction with a code element in a website or an app.
Now metrics must account for which digital-related behaviors are trending consistently. Machines are increasingly mimicking human "cognitive" functions, such as "learning" and "problem solving". These activities reflect a series of decisions, and differ from a click of a button. This kind of measurement lends support for predictive analytics and deep learning modeling.
The evolving perception is an extension of where analytics has been headed in the last few years. Analytics providers have been integrating data sources, making many analytics solutions a central dashboard. Moreover, building the right infrastructure to store and process massive amounts of data had to occur as well to make AI ripe for development.
A/B testing is a good result of the current development trend. A/B testing is meant to highlight when one web element is preferred over another. But Adobe announced an AI influenced feature to Target, its A/B testing platform for its analytics solution. That feature is Auto-Target, a machine learning automation protocol that automates personalization testing of elements to determine the preferred individual experience with media. The marketer can select and automatically test various layouts, images, and texts that define not only presented offers, but also impact the larger customer experience.
Many long-time analytics experts see AI moving analytics and data usage within a business organization into alignment with customer experience needs. Dennis Mortensen, co-founder of the company x.ai, has refined his analytics skills since its early days -- he created Yahoo Analytics in those days -- and is now a major player in AI solutions with Amy, an AI-powered personal assistant that is now exiting beta. According to Mortensen, “We’re moving away from a setting in which software assists you in teasing intelligence out of data (say, fooling around inside Google Analytics) to a new paradigm in which you describe your objective, and an AI agent does this for you …All the value is in the question."
So how do organizations build the right infrastructure to uncover efficiency opportunities and business advantages from the data? One way is to look at metrics as a way to help answer the following questions:
- What answers were you looking for from the metric?
- Where in the reporting did you look for your answers?
- Did you discover something else?
- Are there any gaps in information in the story being told?
Another way is to look for better visualizations that serve the storytelling needs of the data examined. Better visualizations arrange data so that storytelling can be established. Solutions such as Google Data Studio and Neo4j offer simplified visualization that serve the same purpose -- to provide a digital scratchpad with user-friendly interface to ease data updates and graph changes meant for different reporting audiences.
No matter what digital season you are in -- beginning or more established -- the AI advances correspond to the next logical step in analytics, to derive the story and insights from the data based on accurate predicted outcomes. Those who harness these opportunities will see a significant competitive advantage.