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DATA ANALYTICS LIFECYCLE PHASES
Data is extremely important in today’s digital-first world, as it has always been. Throughout its life cycle, it goes through a number of stages, including creation, testing, processing, consumption, and repurposing. The Data Analytics Lifecycle is a diagram that depicts these steps for professionals that are involved in data analytics projects. The phases of the Data Analytics Lifecycle are organized in a circular framework, which is referred to as the Data Analytics Lifecycle. Each stage has its own significance as well as its own peculiarities. The phases that are fundamental to each data analytics process. Hence, they are more likely to be present in most data analytics projects’ lifecycle. The Data Analytics lifecycle primarily consists of 6 phases. Phase 1: Discovery – This phase is all about defining the data’s purpose and how to achieve it by the end of the data analytics lifecycle. The stage consists of identifying critical objectives a business is trying to discover by mapping out the data. During this process, the team learns about the business domain and checks whether the business unit or organization has worked on similar projects to refer to any learnings. In this phase, the team also evaluates technology, people, data, and time. For example, while dealing with a small dataset, the team can use Excel. However, heftier tasks demand more rigid tools for data preparation and exploration. In such scenarios, the team will need to use Python, R, Tableau Desktop or Tableau Prep, and other data cleaning tools. This phase’s critical activities include framing the business problem, formulating
initial hypotheses to test, and beginning data learning. The key aspects to be considered in this phase are : - The data science team learn and investigate the problem. Develop context and understanding. Come to know about data sources needed and available for the project. The team formulates initial hypothesis that can be later tested with data. Phase 2: Data Preparation – In this phase, the experts’ focus shifts from business requirements to information requirements. One of the essential aspects of this phase is ensuring data availability for processing. During this phase’s initial stage, the team gathers valuable information and proceeds with the business ecosystem’s lifecycle. Various data collection methods are used for this purpose, such as a) Data Entry – Collecting recent data using manual data entry techniques or digital systems within the organization b) Data Acquisition – Gathering data from external sources c) Signal Reception – Capturing data from digital devices, including the Internet of Things and control systems. The stage encompasses the collection, processing, and cleansing of the accumulated data. The key aspects to be considered in this phase are : - Steps to explore, preprocess, and condition data prior to modeling and analysis. It requires the presence of an analytic sandbox, the team execute, load, and transform, to get data into the sandbox. Data preparation tasks are likely to be performed multiple times and not in predefined order. Several tools commonly used for this phase are – Hadoop, Alpine Miner, Open Refine, etc. Phase 3: Model Planning – This phase needs the availability of an analytic sandbox for the team to work with data and perform analytics throughout the project duration. The team can load data in several ways. The team identifies variables for categorizing data, identifies and amends data errors. Data errors can be anything, including missing data, illogical values, duplicates, and spelling errors. For example, the team imputes the average data score for categories for missing values. It enables more efficient data processing without skewing the data. After cleaning the data, the team determines the techniques, methods, and workflow for building a model in the next phase. The team explores the data, identifies relations between data points to select the key variables, and eventually devises a suitable model. The key aspects to be considered in this phase are : - Team explores data to learn about relationships between variables and subsequently, selects key variables and the most suitable models. In this phase, data science team develop data sets for training, testing, and production purposes.
DATA ANALYTICS LIFECYCLE EXAMPLE
Consider an example of a retail store chain that wants to optimize its products’ prices for boosting its revenue. The store chain has thousands of products over hundreds of outlets, making it a highly complex scenario. Once you identify the store chain’s objective, you find the data you need, prepare it, and go through the Data Analytics lifecycle process. You observe different types of customers, such as ordinary customers and customers like contractors who buy in bulk. According to you, treating various types of customers differently can give you the solution. However, you don’t have enough information about it and need to discuss this with the client team. In this case, you need to get the definition, find data, and conduct the hypothesis testing to check whether various customer types impact the model results and get the right output. Once you are convinced with the model results, you can deploy the model, integrate it into the business, and you are all set to deploy the prices you think are the most optimal across the outlets of the store. The Data Analytics lifecycle’s circular process consists of 6 primary stages that dictate how information is created, collected, processed, used, and analyzed. Mapping out business objectives and striving towards achieving them will guide you through the rest of the stages.
