The data analytics lifecycle is the process by which data analysts, data scientists, and other members of the data team undergo in order to turn raw data into actionable insights. Every company has its own unique processes and requirements. No two businesses are exactly alike. But there are universal best practices that can help any data analytics team work more efficiently and produce better results. If you’re new to the world of data analytics or you’d like a refresher on the various principles involved, you’ve come to the right place! In this blog post, we will cover everything you need to know about the data analytics lifecycle as well as what steps are involved in this process for any business looking to optimize their analytical capabilities.
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What is the Data Analytics Lifecycle?
The data analytics lifecycle is the process by which data analysts, data scientists, and other members of the data team undergo in order to turn raw data into actionable insights. Every company has its own unique processes and requirements. No two businesses are exactly alike. But there are universal best practices that can help any data analytics team work more efficiently and produce better results. In short, the data analytics lifecycle is the series of steps and processes that your organization will go through in order to turn raw data into meaningful insights that can help improve your business in some way. The lifecycle will vary greatly depending on the type of data you’re dealing with as well as your industry. In most cases, the process starts with data discovery and moves through data cleansing, data integration, data warehousing, and then ends with data analysis and visualization.
Data Discovery
At the beginning of any analytics project, it’s important to understand what exactly you’re dealing with. In other words, the goal of the data discovery phase is to obtain a complete view of the data you’ll be using for the remainder of the analytics lifecycle. The first thing that data analysts will do during the discovery phase is to obtain a copy of the raw data. They’ll also begin to identify key trends and patterns in the data to help them determine what kind of insights they’ll be able to produce as the project progresses. In many cases, the raw data will be coming from various sources. This means that it will need to be reconciled and standardized before it can be analyzed. In other words, data analysts will need to clean the data before they can proceed with their other projects.
Data Cleansing
Once data analysts obtain copies of their raw data, they’ll begin the process of cleansing it. This is the most important step in the data lifecycle. Why? Because it can make or break your entire analytics project. If you don’t cleanse your data correctly, it will be difficult to turn it into actionable insights. It may even be impossible. During the cleansing process, data analysts will perform a variety of tests to determine whether some or all of the data is unusable. They might check for things like missing values, incorrect units, missing or incorrect IDs, or incorrect timestamps. Perhaps the data is missing crucial pieces of information that would be needed to turn it into useful insights. Or maybe it’s simply not accurate, which would make it difficult or impossible to trust.
Data Integration
Once the data has been cleansed, data analysts will need to integrate it. In other words, they’ll need to bring all of the cleansed data together into one central location where it can be accessed easily by other team members. Data analysts will use a wide variety of tools and platforms to accomplish this. Perhaps they’ll use a data warehouse, ETL tool, or some other data integration solution. The goal of the data integration phase is to create a centralized data store that can be accessed by every member of the team. This will allow team members to bring their data together to create comprehensive reports and visualizations. It will also make it easier to turn the data into actionable insights.
Data Warehousing
During the data warehousing phase, data analysts will create a centralized data warehouse where all of the data is stored and made easily accessible to the rest of the team. The data warehouse will store data from every source relevant to the project, including raw data, cleansed data, and data that has been standardized and integrated into the centralized data store. The data warehouse is a crucial element of any analytics lifecycle. It allows every team member to easily access the data they need to do their jobs. They can simply log into the data warehouse and pull whatever data they require, whenever they require it. Data warehouses also provide a centralized location for all of the data used in the analytics lifecycle. This makes it much easier for data analysts to track and maintain their data as the project progresses.
Data Analysis
The data analysis phase is where data analysts actually turn their raw data into actionable insights. There are many different types of analytics that your team members can perform during this phase of the lifecycle. Some of the most common include descriptive, predictive, and prescriptive analytics. Descriptive analytics simply means describing what has already happened. This data is obtained from historical record-keeping. It doesn’t include any forecasts or predictions. Predictive analytics uses past data to predict what will happen in the future. It is based on the assumption that what happened in the past will most likely happen in the future. Prescriptive analytics involves making recommendations based on data analysis. It can be used to make predictions about future events as well as offer suggestions on ways to improve a current process or situation.
Conclusion
The data analytics lifecycle is the process by which data analysts, data scientists, and other members of the data team undergo in order to turn raw data into actionable insights. This lifecycle will vary greatly depending on the type of data you’re dealing with as well as your industry. In most cases, the process starts with data discovery and moves through data cleansing, data integration, data warehousing, and then ends with data analysis and visualization.
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