Analytics provides data and insights into the performance of an organization. The NFL uses it to identify key players and predict who will win games, while private corporations use data analytics to forecast sales and improve their marketing efforts.
Basic analytics can be accessed from various databases, including Google Analytics and Facebook Insights, amongst many others. However, companies that want to dig deeper into their operations and have the right business intelligence to clearly understand what is going on with their real-time analyses can benefit from advanced analytics software systems. These systems have a range of use cases, from marketing analytics to energy analytics. Keep reading to learn more about these tools.
Advanced analytics uses predictive models.
A retail company might look at data insights from the 2019 holiday season when approaching the upcoming festive preparation and shopping days. Basic analytics tools can help you understand what happened over a set period. Basic analytics tools can help you know what happened over a set period. However, advanced analytics is predictive and has a wide range of applications because these systems use data from the past and trends from the present to forecast what will happen in the future.
Once again, using the 2020 holiday shopping season as an example, a company might use predictive analytics and trends from this year to forecast website traffic levels over the next few months. As the pandemic has driven more people to adopt online shopping (and on a larger scale), the system might increase the estimated spending amounts and the number of customer orders. These analytics tools might also account for future trends in popular shopping dates away from Black Friday. These predictions are more valuable for business owners, who can take action based on the estimated insights.
AI and machine learning increase analytical accuracy.
One of the main reasons advanced analytics tools can predict the future is artificial intelligence (AI) and machine learning. As the name sounds, machine learning teaches bots to “read” or understand materials they come into contact with to form patterns. For example, an AI tool might identify a giraffe picture because of its long neck, thin legs, and spots.
Today’s analytics tools use machine learning to look ahead rather than simply organizing data to report the past. Today’s analytics tools use machine learning to look ahead rather than merely collecting data to inform the past. Machine learning tools use big data to gather information and draw insights from what they read. When the data is of high quality, and there is a lot of it, AI tools can make predictions with higher confidence levels. These systems also work faster than any human business analyst or data professional.
Analytics tools are increasingly affordable.
AI systems, like forms of technology, follow economies of scale. One of the great things about advanced analytics is that most tools aren’t restricted to enterprise-level teams with million-dollar software budgets. When these analytics first debuted, they were expensive and hard to find because they were brand new, unlike the first computers. Today, small business owners and startup founders can invest in the necessary analytics tools without blowing through their technology budget.
Consider learning about a few advanced analytics companies that tailor your industry’s data and prescriptive analytics tools. You may be able to find an ideal solution for your team. There is one consistent point between simple analytics tools and advanced prescriptive analytics: the data is only as useful as what you do with it. Business owners often take the data at their hands and ignore it, instead choosing to move forward with their “gut instincts” or operations plans they already had in place. Investing in these tools ensures you are ready to respond to the data and trust the information. This will ensure you get your money’s worth out of them and drive your business forward.