Data analytics is now firmly embedded in businesses all around the world. From scaling business operations with effective predictions to streamlining internal processes, companies can make use of data to create better working environments and drive progress. However, data isn’t useful until it’s gone through an extensive process known as the data pipeline.
The data pipeline describes the extensive process that data takes from ingestion to analytics and visualization. Raw data arrives in the pipeline, moves through various stages, and is then in the correct format for productive analysis. In order to better understand how to utilize data, business leaders should understand how to manage and oversee the data pipeline.
Considering that around 80-90% of all collected data is unstructured, a firm knowledge of the process this data undergoes in its pipeline is vital. In this article, we’ll dive into each of the core stages of the data pipeline, detailing what happens to data and why it is a necessary part of the lifecycle of data.
Data ingestion is the first stage of the data pipeline, covering the processes of collecting raw data from several sources. There are several potential types of ingestion that businesses can use, like batch ingestion. Typically, the data ingestion stage will involve extracting data from original sources and loading them into the first stage of the pipeline.
While often overlooked, this stage is arguably one of the most important in the pipeline. Without a continual flow of new information, businesses are unable to access the information they need to drive progress. As data analysis and extraction techniques have improved, companies can now deal with higher variances in source data.
Initially, only highly structured data was appropriate for analysis. Now, mainly in part due to the extensive data pipeline, unstructured and semi-structured data are also able to enter into this ingestion stage. Expanding the top of the funnel allows businesses to gain a more diverse insight, further driving success in all data-related fields.
The second stage of the data pipeline is commonly known as the transformation stage. However, some businesses will use ETL while some will use ELT, both of which describe a type of data pipeline.
ETL, which stands for Extract, Transform, Load, is a data pipeline in which source data undergoes transformation before being moved into the storage phase. Extract refers to pulling data from its original source; transform explains the process of formatting or standardizing data into the desired structure; load refers to the movement of data into a final destination, like a data warehouse.
ELT follows the same processes, but the middle and final stages are switched around. In this form of data pipeline, data is loaded into its final destination before undergoing the transformation.
Regardless of which pipeline your company typically follows, this core stage will transform your raw data into a more manageable form. For most companies, this involves standardization, changing unstructured data into structured information that businesses can more easily process into data analytics tools.
Some data pipelines will use either batch processing or stream processing at this stage. If a business wants to combine the transformation and storage phases, then it will use one of these aforementioned formats. Batch processing is better for huge datasets that do not require instant results, as they can take anywhere from a few minutes to a few days to process and ready data.
Alternatively, stream processing is more commonly used with real-time analytics tools. Stream processing continuously processes and passes data onward, facilitating a stream of new information.
Alongside these potential processing units, most companies will store their data at this stage. Businesses can turn to various suitable repositories, such as databases, cloud data warehouses, data lakes, and delta lakes. The latter two of these are often confused due to their similar names. When comparing data lake vs delta lake, the former is used to store data in a raw form while the second adds a layer of ACID compatibility.
Depending on the unique needs of a business, the data storage architecture they use will change, often encompassing several infrastructures that work together to provide a comprehensive solution.
Data analysis takes the transformed data, cleans it, and then examines it with specific analytical methodologies to provide insight. In the world of business, this insight could be related to anything from internal metrics of productivity to customer actions that can be turned into effective future strategies.
However, before analysts and companies can use this information to create positive change or restrategize, it must first enter a reporting phase. While data analysts can understand the raw insight produced from the analysis stage, many with decision-making power in the world of businesses lack the technological skills to do so.
The reporting phase turns insight into easy-to-follow graphs, presentations, and reports. By feeding these reports that clearly detail what actionable insights businesses can take to C-suite executives and others in power, companies can then create effective plans for the future. Due to how diverse the data collection stage now is, the insights that this final stage can produce are essentially endless.
Especially in light of live reporting and the continuous stream of data that companies can now access, reporting is more effective than ever before. Online dashboards that record specific metrics and trace their changes over time are now a core part of the data infrastructure that we interact with on a daily basis.
Analysis and reporting provide insights into complex data in an accessible format, boosting engagement within both businesses and customer groups. As the extent to which data is integrated into popular culture continues to grow, we’ll only see data visualization become more important.
Although not many business leaders understand the data pipeline and the actual processes that go on when they use analytics tools, this data architecture is a core part of almost every modernized business. Over the past few years, as the role of data and data-driven decision-making have become more pronounced, a clear understanding of this pipeline is more vital than ever before.
Every time a business leader or data analyst draws insight from their data, they are engaging with the final product of this intensive and extensive pipeline. While hidden from view, data pipelines are a central part of every business that engages with data and draws on effective, fact-driven practices.
Those that can make use of their data pipelines to a greater extent can harness the power of information, driving progress in their businesses.