There has been a lot of buzz about artificial intelligence (AI). After all, the AI market is making its stamp on the industry. Recent research puts it as a $5bn market by 2020, and Gartner estimates that six billion connected “things” will require AI support by 2018.
But AI is not just about snazzy robots taking over our jobs and doing our housework. AI can include connected machines, wearables, and other business tools, such as voice assistants, which are already boosting productivity not only at work but also at home.
For the past decade or so, AI has already been a staple in the background of processes, mostly handling mundane things, such as repetitive tasks, data processing or number crunching. This is why the first step to understand if your business is ready for the next step in AI is if big data analytics is a major component in your business processes. AI is more than just the hardware and software, but also the information that you feed into the system.
Embrace big data analytics
The objective of big data analytics is to find hidden patterns, correlations and present actionable solutions that businesses can use to their advantage. For example, if you want to predict the outcome of a decision, you can feed an existing data, such as the past year’s performance or a country’s annual trade report, and input your choices or limitations. The AI does the rest of the work and churns out multiple possible scenarios. Following which, you just need to select the desired scenario and the system will show the outcome.
Take for example, your company wants to predict the outcome of a decision it is about to make. You feed the AI data like past year performance, and other related data. The AI then crunches through the presented data and churns out multiple possible scenarios. All that is left for you to do, is to pick out your desired scenario, and the solution that provided the outcome.
More than 90 per cent of companies today, however, use very basic analytics, more accurately termed as descriptive analytics. There are four types of analytics that the industry has generally agreed on.
Descriptive Analytics This is the basic analytics that you get from your web server through tools, like Google Analytics. You can quickly read and understand reports based on a given period to verify if a campaign was successful or not based on simple parameters, such as the number of banner advertisement clicks and page views.
Diagnostic Analytics This means you want to delve deeper into the data you have collected in order to understand why some things happened. You can use business intelligence tools to get some insights, but it can be a tedious task that gives you limited actionable insights. It is ideal, however, if you require an extensive understanding of a limited piece of the problem.
Predictive Analytics If you have been collecting contextual data and correlating it with other user behaviour datasets, as well as expanding user data beyond what you can get from your web servers, you enter a new level where you can get real insights. Over time, you can predict what will happen if you continue to actively collect data.
Prescriptive Analytics This is the point where you can accurately analyse your data to predict what is going to happen. The system will also churn out solutions to your problems. By this time, you are very close to being able to understand what you should do in order to potentially maximise good outcomes and also bad outcomes. This is on the edge of innovation today and the final stage of analytics.
By understanding which stage of analytics your business is in, it will provide a relatively accurate indicator of whether your company are ready for the next level in AI.
Written for Supply Chain Asia publications at http://supplychainasia.org/business-ready-ai/.