Data Analytics Fundamentals for Business Leaders

First things first, let's discuss on what "data analytics" really is and how business leaders can learn about working with data. According to Technopedia, "data analytics" refers to qualitative and quantitative methods and processes used to enhance productivity and increase profit. In the analytics, data is extracted and categorized to identify and analyze behavioral patterns using various techniques customized to the using organization. As a business leader, you're responsible for making the best decisions for the company, which can now be supported with a tremendous amount of data. This being said, you'd need to be at least literate on where you can obtain meaningful data and which types of data to collect and use. Second, understand the problem to solve. What problem do you try to solve? Are you looking for ROI of certain activities? Recognize the data you'd need to solve a particular problem. Third, understand the data-generation process. Before business leaders can make a valid decision, they need to discern the good from the bad data. For this, they'd need to understand the data collection process and how to evaluate the validity and "cleanliness" of the data. This would largely depend on the tools you use and the setup, as well as whether the data is structured or unstructured. Fourth, use the business domain knowledge. Divide each action into trackable variables and steps. For this purpose, you'd need to understand each data point based on the business activities. For instance, there are connections and correlations in each operation to take note and use in the analytics. Fifth, understand the types of data and their use cases. There are various types of data based on various categories and purposes. Google Analytics, for instance, provides information on touch points, user demographics, devices used, and other dimensions that can be quite comprehensive and extremely valuable to any business. Moreover, the best of it is it's free to use. Each analytics can be used for descriptive, prescriptive, or even automated purposes. There are descriptive, prescriptive, behavioral data, historical data, and diagnostic data. Your job is making sure that you understand each type of data works and how you can use them to solve a problem. For approving or denying a credit application, for instance, you can use automated data. For predicting how much stock you'd need to get ready for the holiday, most likely you'd need predictive analytics. And descriptive analytics is useful for understanding the personas of your customers. At last, we're already in the Fourth Industrial Revolution, where using automation, AI, and data analytics is the new normal. Any business that doesn't use data analytics may face a serious challenge in the future. Thus, it's the leader's job to ensure a smooth transition in this new AI and Big Data era.