Understanding the Fundamentals of Analytics Software
Analytics provides data and insights into the performance of an organization. The NFL uses it to identify key players and make predictions for who will win games, while private corporations use data analytics to forecast sales and improve their marketing efforts.
Basic analytics can be accessed from a variety of 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 analyses in real time, 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.
Basic analytics tools can help you understand what happened over a set period of time. A retail company might look at data insights from the 2019 holiday season when considering how to approach the upcoming festive preparation and shopping days. However, advanced analytics are predictive and have a wide-range of applications because these systems use data from the past and trends from the present to forecast what is going to 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 orders from customers. 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.
One of the main reasons advanced analytics tools are able to predict the future is with artificial intelligence (AI) and machine learning. As the name sounds, machine learning teaches bots to “read,” or understand materials that they come into contact with, in order to form patterns. For example, an AI tool might identify a picture of a giraffe because of its long neck, thin legs, and spots.
Machine learning tools use big data to gather information and draw insights from what they read. When the data is of a high quality and there is a lot of it, the AI tools can make predictions with higher levels of confidence. These systems also work faster than any human business analyst or data professional. Today’s analytics tools use machine learning to look ahead, rather than simply organizing data to report the past.
One of the great things about advanced analytics is that most tools aren’t restricted to enterprise-level teams who have million-dollar software budgets. AI systems, like forms of technology, follow economies of scale. When these analytics first debuted, they were expensive and hard to find because they were brand new– not unlike the first computers. Today, small business owners and startup founders alike can invest in the analytics tools that they need, without blowing through their technology budget.
Consider shopping around to learn about a few advanced analytics companies that tailor data and prescriptive analytics tools for your industry. 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. Too often, business owners take the data at their hands and ignore it, instead choosing to move forward with their “gut instincts” or operations plans which they already had in place. If you invest in these tools, make sure that you are ready to respond to the data and trust the information. This will ensure that you get your money’s worth out of them and really drive your business forward.