Advanced analytics can give companies of all sizes the tools to make better decisions. Big data and statistical modeling can give teams an idea of the levels of risk involved in certain actions, while a variety of algorithms can make decisions for what is best based on data.
That being said, advanced analytics tools alone aren’t enough to help companies grow and take advantage of overlooked opportunities. There are additional steps your business needs to take for this data to get used effectively.
Follow these steps to maximize your advanced analytics solutions and get a competitive advantage within your industry.
One of the main benefits of advanced analytics for your business is predictive modeling or forecasting future events rather than using historical data to report what happened in the past. However, these predictive models need context if they are going to be believed or acted upon. This is where the human element of business intelligence comes in.
Please work with your data analysis team to help them provide context to the reports they submit. These individuals aren’t just data scientists; they are educators. Their job is to review why the model predicts a certain trend and whether it should be deemed accurate.
For example, a normally reliable artificial intelligence system might predict an increase in sales for a retailer in January after the major shopping months of November and December. This system doesn’t realize that the holiday season will end, and the data analyst will need to adjust the computer’s predictions. Remember, machines aren’t always right, and a human can catch problems that advanced analytics tools cannot.
As you start to use data analytics within your organization, you will discover that there are useful metrics and useless ones. Useful metrics can alert your business to important problems and help you take steps to prevent major issues. For example, if your website spikes the bounce rate, there might be an issue with your pages.
Other metrics aren’t as valuable. There are vanity metrics that really only tell you how great you’re doing, along with irrelevant metrics you don’t need to see. (A Michigan-based brick and mortar business doesn’t necessarily need to know that 98% of its traffic comes from the United States.)
Determine which analytics provide value to your business and focus on those. This way, you can funnel your limited business intelligence and big data resources to tracking and improving relevant KPIs rather than numbers that won’t make an actual difference.
The majority of people (around 65%) are visual learners, which means they will get more out of a chart, graph, or image than out of a data set or oral report. Math can be intimidating for some people, including your statistical analysis and data assets.
As you invest in advanced analytics, train your team on effective ways to communicate information to other organization employees. For example, if you need to win over your VP on a budget increase, adding context to your data can help them understand why the money is so important.
Just because your team has a deep understanding of advanced analytics doesn’t mean other departments do. The clear reports and visuals you make can ensure everyone stays in alignment.
At first, your advanced analytics systems might seem like optimal solutions for data visualization and clarity. However, one tool alone can’t provide all the business intelligence. Make sure your machine learning also comes with some human learn – namely critical thinking and decision-making. These soft skills can help turn your hard data into actions to move your business forward.