Your business needs to be data-driven in today's economic environment. However, a data-driven approach involves much more than collecting and organizing data. You must also analyze this data, uncover meaningful insights, and make relevant conclusions. Intelligent analytics is the primary process through which your business can derive value from collected data.
The field of intelligent analytics has grown significantly in recent years. This is because businesses are racing to find the best analytical approach they can use to derive value from their data. To remain competitive, you need to implement advanced analytical techniques that can be used to sift through large amounts of data in real-time.
Intelligent analytics allows you to eliminate human bias/error, analyze data faster, and make real-time decisions based on the valuable insights you uncover.
Defining Intelligent Analytics
In the simplest terms, intelligent analytics refers to the process of examining raw data to draw conclusions and make strategic decisions. There are many different dimensions to data analytics. This process encompasses data management, mining, predictive analytics, and other related techniques.
The ultimate goal is to examine both historical and real-time data in a manner that uncovers performance insights. Because the amount of data that needs analysis is growing daily, advanced technologies are necessary to collect, arrange, and analyze incoming data. This approach enables businesses to detect valuable connections and trends- and to make decisions that boost overall performance.
Types of Intelligent Analytics
When analyzing data, several approaches can be used. Intelligent analytics will come in handy if you're looking to establish behavioral patterns or to define and categorize a specific problem. Here are 3 of the most common types of intelligent analytics.
Descriptive Analytics: Before beginning to derive value from data, you first need to collect, describe, and categorize this data. Descriptive analytics refers to the process of defining what happened from the data you collected. Examples include finding out the number of sales during a specific month, how many new customers the business acquired during a given year, or the quantity of revenue realized from a new ad campaign. Descriptive analytics lies at the top layer of the data analysis pyramid.
- Predictive analytics: Once you discover "what happened" and "when it happened," the next approach is to discover "why it happened?". Predictive analytics uses findings from descriptive analytics to estimate what's likely to occur in the future. Think of predictive analytics as a forecasting approach, where you would use trends and connections within your data to estimate what's likely to happen down the road. For example, predictive analytics estimates future sales, assesses risk impact, and predicts marketing campaigns' ROI.
- Prescriptive analytics: Prescriptive analytics is the deepest level of the intelligent analysis approach. Prescriptive analytics involves deciding the exact steps to take in response to the insights you obtained from the collected data. Think of it as an action plan you may develop to resolve a problem or take advantage of an upcoming opportunity. For example, you may decide to increase the price of a specific product based on historical sales data and projected future sales. Therefore, prescriptive analytics requires sophisticated data analysis techniques- including the use of algorithms, data mining, and machine learning.
With the three intelligent analytics approaches described above, your business will be on the path toward fully implementing a data-driven approach. Not only will you be able to examine historical data (descriptive analytics), but you'll also be able to accurately forecast future events (predictive analytics) and make actionable decisions based on this information (prescriptive analytics).
Intelligent analytics offers distinct advantages over traditional means of statistical analysis, especially when working with complex multi-process environments. Because of its algorithmic analysis methods, intelligent analytics removes the bias that an individual analyst can impose.
Intelligent analytics allows strategic leaders to proactively anticipate a situation before it becomes a problem or missed opportunity. These leaders can take this information and make informed decisions, build effective strategies, and drive continuous improvement of each situation.