In the past, raw data was mainly stored in a company's data warehouse; however, this method is no longer optimal because it doesn't take into account external information (forums, social networks or public relations) and limits the company to internal resources. Raw data is also found elsewhere, such as their own operating systems, such as CRM or ERP, and is also found in big data repositories (filled mainly with unstructured data), social networks and even open data sources. For this reason, it is very important to identify all this data and connect to it, no matter where it is. To succeed in all three phases of data analysis, you'll need a platform that extracts knowledge from raw data, and this is where data virtualization comes into play.
Data preparation and processing involves the collection, classification, processing, and filtering of the information collected to ensure that it can be used in the later stages of the analysis. An important element of this step is to ensure that all necessary information is easily accessible before proceeding with its processing. If the findings meet the objectives, the reports and results are finalized. However, if the conclusion differs from the purpose indicated in phase 1, you can return to the data analysis life cycle and return to any of the previous phases to adjust the data entered and obtain a different result.
The data analytics lifecycle describes how data is created, collected, processed, used, and analyzed to meet corporate objectives. Companies can use big data to study consumer trends by monitoring buying activity at the point of sale and online. However, the ambiguity of having a standard set of phases for the data analysis architecture is an obstacle for data experts when it comes to working with information. Start by defining the scope of your business and make sure you have enough resources (time, technology, data and staff) to achieve your goals.
The framework is straightforward and cyclical, meaning that all processes related to big data analysis must be completed sequentially. The lifecycle and tools of big data analysis quickly minimize risks by optimizing complicated decisions to deal with unforeseen events and potential threats. Using big data, companies can calculate the chances of returning products and then take the necessary steps to ensure that they suffer the minimum losses due to the return of the products. Companies use big data to track consumer trends and personalize their products and services to meet specific customer requirements.
Several data sources are identified and the amount of data that can be accumulated over a period of time is analyzed. One of the essential parts of this phase is to ensure that the data you need is actually available for processing. For this reason, it is essential to process raw data and extract the most relevant information for your company. You must identify several data sources and analyze the amount and type of data you can accumulate over a given period of time.
The essential activities of this phase include structuring the business problem in the form of an analytical challenge and formulating initial hypotheses (IH) to test the data and begin to learn them. Having a visualization of the data helps to make better decisions and also reduces the risk of losing important data, since the visualization “offers a picture of the data as a whole”. Amazon has harnessed this lifecycle of big data and analytics to provide the most personalized shopping experience, where recommendations are based on previous purchases and on items purchased by other customers, browsing habits, and other features. In this way, you separate the grain from the chaff and create a repository with the key data that affects your business.
