Over the past few years, machine learning and artificial intelligence have come to dominate the rapid evolution of technology. With pioneers like Microsoft, Google and Amazon now integrating AI with many of their industry-leading technologies, it’s only reasonable to expect this trend to have a growing impact on the way organizations all over the world function in the future.
Most businesses already rely on automation to a degree. For example, many service centers rely on chatbots for helping customers resolve more common issues, while marketing departments rely more on machine learning to make sense out of increasingly large data sets. To stay relevant and maintain a competitive edge in the years to come, your business too must prepare for the machine-learning revolution.
Understanding Your Business Processes
While we might still be some years away from things like fully autonomous customer service centers, it’s important to prepare your business for emerging technologies. That means starting with a thorough analysis of your existing business processes to determine which ones can benefit from automation and machine learning.
You can start by looking for any repetitive processes and decisions made consistently and frequently enough to benefit from automation. For example, a bank might rely on automation to reject or accept loan applicants based on their credit scores, or an online store might use artificial intelligence to provide personalized marketing based on past shopping and browsing habits.
The main driver behind rapidly increasing investment in machine learning is big data, which refers to the increasingly large data sets that modern enterprises have at their disposal. Since every digital activity generates data, it’s more important than ever to integrate machine learning into your processes to provide actionable insights by way of intelligent analytics.
It’s important to remember that AI is still a long way off from automating complicated business processes that are inherently reliant on the human element. For this reason, you should focus primarily on simple processes that are already clearly defined and understood. Machine learning can draw upon the data gathered by these processes to automate many tasks, such as predicting production needs and forecasting sales.
Preparing Your Data for Machine Learning
Just as a human can’t expect to learn without something or someone to learn from, a machine can’t learn without data to garner insights from. That’s why it’s important to be able to integrate machine learning into your existing systems and, if that’s not yet possible, to consider replacing your current technology with a more future-proof infrastructure.
Since machine learning works by analyzing existing data and providing insights based on these analyses, it needs a consolidated database to draw from. This means you should prepare your core systems, such as customer relationship management, for integration with machine learning.
If you’re currently one business with many systems, then it’s time to integrate and consolidate to ensure that all key business processes work well together. If, by contrast, your existing system is fragmented, successfully implementing machine learning will be practically impossible.
Ultimately, the more disciplined your approach toward handling data, the more you’ll be able to benefit from machine learning. After selecting and consolidating your data and its sources (such as email, social media and user account details), you’ll need to configure it so that it’s compatible with machine-learning algorithms. This step also requires the removal of incorrect or duplicate data, which will increase performance and improve the accuracy of analytics and search results later on.
Preparing data for machine learning can be a monumental undertaking. To ensure that your business is ready to take advantage of new and emerging technologies, it’s essential to have a strategy. That’s where SpectrumWise comes in. If you’re ready to make a smooth transition to technology that will keep up with your business, give us a call today.