The data analytics lifecycle is a series of steps to transform raw data into valuable and easily consumable data products. These can range from well-managed datasets to dashboards, reports, APIs, or even web applications. In other words, it describes how data is created, collected, processed, used, and analyzed to achieve a specific product or business goal.
The increasing complexity in organizational dynamics directly impacts how data is handled. Numerous people must use the same data but with different goals. While a top executive might need to know just a few top-level key performance indicators to track business performance, a middle manager might need a more granular report to support daily decisions.
This highlights the need for a governed and standardized approach to creating and maintaining data products based on the same data foundation. Given the many decisions an organization must make regarding its data governance, technologies, and management processes, following a structured approach is fundamental to documenting and continuously updating an organization's data strategy.
The data analytics lifecycle is, therefore, an essential framework for understanding and mapping the phases and processes involved in creating and maintaining an analytics solution . It is an essential concept in data science and analytics and provides a structured approach to managing the various tasks and activities required to create an effective analytics solution.
The data analytics lifecycle typically includes the following stages:
- Problem definition :
The first phase of the analytics cycle is about understanding the problem that needs to be solved. This includes identifying the business objectives, the available data, and the resources needed to solve the problem.
- Data modeling :
After the business requirements are identified, and an assessment of data sources is completed, you can begin modeling your data according to the modeling technique that best meets your needs. You can choose a diamond strategy, a star schema, a Data Vault, or even a fully denormalized technique.
- Data ingestion and transformation :
The next phase is to ingest and prepare the data that's coming from the source systems to match the models created. Depending on the overall information architecture, you can opt for a schema-on-write strategy, where you put more effort into transforming the raw data directly into your models, or a schema-on read strategy, where you ingest and store the data with minimal transformations and move heavy transformations to the downstream layers of your data platform.
- Data storage and structuring :
Once the data pipelines are designed and potentially implemented, you need to decide on the file formats to use - simple Apache Parquet or more advanced formats like Delta Lake or Apache Iceberg - as well as the partitioning strategies and storage components to use - a cloud-based object store like Amazon Simple Storage Service (S3) or a more data warehouse–like platform like Redshift, Big‐Query, or Snowflake.
- Data visualization and analysis:
Once the data is available, the next step is to explore it, visualize it, or create dashboards that directly support decision making or enable business process monitoring. This phase is very business oriented and should be created in close coordination with business stakeholders.
*- Data quality monitoring, testing, and documentation : *
Although illustrated as the final phase of the analytics lifecycle, data quality should be an end-to-end concern and ensured by design across the whole flow. It involves implementing all quality controls to ensure that stakeholders can trust your exposed data models, documenting all transformations and semantic meanings, and ensuring proper testing along the pipelines as the data continues to flow.
The analytics lifecycle is a key concept that enables organizations to approach data engineering, science, and analytics processes in a structured and consistent manner. By following a structured process, organizations can ensure they are solving the right problem, using the right data, and building data products that are accurate and reliable, ultimately leading to better decision making and better business results.
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