Interpreting data is the final step in analyzing information by making sense of the data. This process helps to draw conclusions, summarize information, and identify correlations and cause-and-effect relationships.
Modern business, in the competitive and low margin construction market, can be compared to military operations, where the success of the company and its survival depends on the speed with which the company is provided with support in the form of resources or "ammunition", behind which are the right decisions. Unlike visualisation, which is an element of reconnaissance, data analytics acts as "ammunition" and is critical to the in-formation decisions that underpin a company's quest to gain a competitive advantage in the marketplace.
Data analysis transforms the collected data into structured and meaningful information that serves as a basis for decision making.
The right solutions are delivered to the company by analysts who use statistical tools and algorithms to analyze data to identify trends, determine relationships between different types of data, and group them according to certain criteria.
Data analysis turns collected data into meaningful information
A McKinsey study indicates that companies in commerce that intensively implement data analytics and utilize big data to its fullest extent can increase their operating margins by more than 60%.
In order to make the right decisions during the analytics process, it is important to properly frame the questions that can be asked of the data where the quality of the decisions made will directly depend on the quality of the management questions that are asked of the data in the analytics process.
The art of asking insightful questions is a valuable skill to develop in the context of working with data. Most people tend to ask basic questions that require minimal effort to answer. However, true analysis begins with the ability to ask thoughtful, meaningful questions to the source of information.
Answering key questions and making informed decisions based on the results of data analysis, creates valuable insight to achieve goals and successfully manage business processes (more about prediction and forecasting tools in the chapter "Prediction, Forecasting and Machine Learning").
The need to keep analytics and dashboards up-to-date is inevitably driving management and executives to realize the benefits of automating processes to increase the speed of decision-making and reduce the human factor in decision-making processes.
The automation of analytics processes and data processing in general is inextricably linked to the topic of ETL (Extract, Trans-form, Load). Just as we need to transform data in the automation process, in the ETL process data is extracted from various sources, transformed according to the required requirements and loaded into target systems for further analysis.
