Enterprises use data analytics based on the type of data they require to make decisions based on their objective. The adoption of an analytics strategy includes the perfect combination of qualitative and quantitative methods based on the requirements of the enterprise.
According to Joel Shapiro, a clinical associate professor of data analytics at Kellogg, each analytical method has something unique to offer. Quantitative analysis is useful to identify the broader trends. An enterprise should choose the tools and approaches based on the adoption of an analytical strategy.
When a business problem occurs, the company searches for new approaches to solve the business problem. This involves the collection of data followed by an analysis that poses a bigger challenge. An enterprise dilemma happens when the data contains several data points. It creates confusion to understand which data is important and which one should be discarded. Data analysts can face issues when they formulate the wrong problems or chase the wrong opportunities. In turn, data analysts will have a tough time focusing and build their data strategies to break these vast amounts of data silos.
Catapulting data relationships is another thing to be considered when it comes to adoption analysis strategy. Every enterprise tries to make maximum use of data analytics for better results. As big data rapidly changes over time it is a tedious task to find an interesting trend or relationship in the data.
According to Shapiro, based on past relationships all the predictions are made. The environment is shifting constantly such that customers’ preferences can change. Therefore, the business has to consider all the reasons that might not be true in the coming years.
Big data’s multiple data points provide data insights that can be used to solve business problems. Enterprises need to focus on what their business problem is and utilize the data to scale up the data models for a strategic uplift especially when it comes to quantitative analysis. Quantitative analysis is used to figure out to know what happened while qualitative analysis is used to figure out why it happened. At times, it requires a hypothesis test about people’s motivation, to know if they scale into cost-effective solutions. Quantitative and qualitative analysis are linked to the process of using data.
A massive data analytics problem is faced by modern enterprises and it depends on how they turn a crisis to an opportunity eventually determine who rules the data competition.