Any business or organization generates a large amount of data. This data in itself is of no use if its meaning cannot be ascertained. Organization heads have different types of questions about their operations, investments, employees, and customers. Which projects are working or not working? Which projects require more funding? Are employees satisfied with the new rules and regulations? How many sales can be expected in the coming years? There are all such questions that must be answered using the available data generated from previous operations, sales, and interactions. Advanced data analysis is needed to prepare such reports and knows the trends and other details.
Predictive Modeling
Under this modeling, managers try to know how one variable affects another variable. In the driver analysis method, the roles of different drivers are determined. For example, a survey about price, customer service, and the company can be compared with another survey that was limited to ascertaining customer satisfaction. It helps to know the overall satisfaction rate. Targeting is another method under this modeling. It identifies a group’s specific characteristics valuable to the company. It can help us know the demographics of the most valuable customers. The segmentation technique is used to identify a particular group from the survey responders. All these techniques are valuable in analyzing big data.
Human Capital Effects on Critical Business Goals
This data analysis gives insights about the employees. It is used to improve their satisfaction rate. Organizations and their HR department must conduct this analysis regularly or they can fail to rectify the dissatisfaction issues among employees. It can lead to high attrition and loss of valuable staff. Simple training of shopfloor employees can improve their productivity. It makes a positive impact on their confidence level. The store can expect better sales results after this training. The data analysis helps know which employees need this type of training. Similar techniques are used in the recruitment process to reduce recruitment costs and hire the best candidates.
The Purpose of Using Advanced Data Analysis, it is used to examine data with the help of sophisticated tools and techniques. It provides deep insight into the upcoming trends. More accurate predictions and recommendations can be made. Its techniques include data mining, pattern matching, visualization, machine learning, sentiment analysis, cluster analysis, network analysis, forecasting, simulation, graphs analysis, and neural networks, among others.
Just mining data is not sufficient. An organization should be able to perform advanced analysis of its big data to obtain the required information from it. It helps you know “what-if” calculations and models. Data mining, big data, and predictive data analytics are some of its major areas.
