Data analytics is rapidly becoming one of the most vital business processes, so it makes sense that data professionals are striving to leverage some of the most advanced technologies to boost their insights. Augmented analytics driven by AI is becoming popular.
Still, unless a business leader is confident in both realms of data analytics and artificial intelligence, such tools may be beyond their grasp. Fortunately, this guide to AI-driven analytics may help some business leaders gain the understanding they need to utilize this invaluable tech properly.
Artificial Intelligence 101
Artificial intelligence is a massive field within the technology industry, and many different AI experts will define this advanced tech in different terms. Generally, the goal of artificial intelligence research is to design machines that demonstrate a capacity for autonomous intelligence and improvement.
At present, the only functional AI is called artificial narrow intelligence (ANI), which means that AI programs can only complete a single specific task — like running data through an algorithm or model. In the near future, AI researchers might create artificial general intelligence (AGI), which simulates human-level intelligence in a machine.
Then, sometime later, we might even develop artificial super intelligence (ASI), which goes well beyond the human capacity for knowledge and learning.
AI software functions differently from regular digital tools, often providing superior performance in meaningful ways. Typical software tools do not have any ability to learn from their actions and improve their processes; only through patching and updates from human software developers can regular digital tools change how they operate.
In contrast, AI tools can learn and grow through their exposure to new data. Though AI software does require careful maintenance from human staff — especially during training of the AI — these tools are much more adaptable than traditional software. Thus, AI tools can be much more useful in data analytics, where data analytics company need flexibility in the type and quantity of data as well as the goals and outcomes of their analysis.
AI Analytics Glossary
Many business leaders eager to use AI-driven analytics have little experience in either field. This simple glossary of terms related to analytics and AI can make it easier to navigate in this high-tech environment:
Predictive analytics: Using historical data to make predictions about the future
Prescriptive analytics: Using historical data to make predictions and prescribe behavior to address those predictions
Augmented analytics: Automating steps of the analytics process, such as data preparation, to reduce workloads for data scientists
All three of these types of analytics programs are most likely to utilize AI to reduce errors and ensure the accuracy of insights. Thus, business leaders looking to leverage AI in analytics can look for keywords like these to identify software powered by AI and machine learning.
Use Cases for Analytics AI
At this point, business leaders know more about AI in general and about the words used to describe how AI might function in a data analytics program — but why bother with AI in analytics at all? Here are a few use cases that demonstrate how invaluable AI can be in the analytics arsenal:
Predicting Business Outcomes
Business leaders are paid high salaries because they are responsible for making major decisions that can impact the course of business success. With the use of AI analytics, business leaders can have more information about the potential outcomes of their decisions, which can lead to even greater organizational success.
Providing New Insights
Consumer and market behavior is not always easy to see or understand. AI-driven analytics allows business leaders to ask questions and receive answers that may be surprising and lead to different organizational behaviors.
Recent fluctuations in demand coupled with rampant disruptions in the supply chain have caused widespread dissatisfaction from retailers and consumers alike. AI analytics tools can utilize historical data to identify trend patterns and alert business leaders before demand is expected to spike. Even better, some AI analytics systems can automatically alter stock orders when it detects an upcoming change in demand.
Unifying Data Systems
Large businesses often suffer from data systems that were created piecemeal and thus did not integrate well. AI tools can help organizations unify their data and analytics processes, providing better visibility for high-level executives and ensuring that regional departments have full access to the data they need to operate effectively.
AI is the future of the data analytics industry, and the sooner business leaders understand how to leverage the power of AI in their analytics processes, the better.