Across social media, business touchpoints, and historical data, companies are now overwhelmed with choices when it comes to collecting and using data. The world now runs on data-driven decision-making, with the rapid access and rich possibilities of data analysis allowing companies to make better choices in less time.
Yet, data doesn’t just appear in its final form as neat insights ready to be used. Instead, data goes through an extended process, moving through the data pipeline. Across this pipeline, data is extracted, transformed into the correct structure, and then loaded into analytical tools. Although the ETL/ELT process is typically split into three steps, there is much more behind the scenes.
In this article, we’ll follow data through the pipeline, exploring each stage and what happens to data in it. By the end, you’ll have a comprehensive understanding of exactly what occurs behind the scenes whenever you use an insight.
Let’s dive right in.
The data pipeline is a term used to describe the end-to-end process of collecting data and drawing meaningful insights from it. Of course, there is more to this than simply conducting analysis on raw data. Beyond just collection and insight, there are storage stages, ingestion stages, processing, and more.
Whenever a user reads insights from data, they are really coming into contact with data that’s been through seven distinct stages:
- Data Collection
- Data Ingestion
- Data Storage
- Data Processing
- Data Analysis
- Data Visualization
- Insight Generation
Let’s break down each stage of the data pipeline, tracing its entire scope and exploring exactly what happens across each step.
The first stage in the data pipeline is all about finding data to perform analysis on. Businesses are now spoiled for choice when it comes to finding sources for their data, being able to find it anywhere from their own business logs, social media, databases, APIs, sensors, or something else entirely.
While a business could manually collect data, most of the time this stage will be an automatic process. With the huge array of tools we currently have at our disposal, data collection isn’t limited to structured data. Data collection could engage with any data format, including semi-structured and unstructured data.
Once a business has collected the data they’re going to use, it enters the pipeline and continues to the ingestion phase.
In the data ingestion phase, data is imported into whatever storage solution a business uses. For structured data, the storage solution could be a data warehouse. Alternatively, if a business was collecting large amounts of unstructured data, then a data lake or a NoSQL database would also suffice.
Depending on where the data is going to be stored, it might also go through some additional steps in this phase. For example, if the data is structured, it might go through cleansing and normalization to increase its quality. The final product of this stage is data that is ready to be stored.
Data storage takes ingested data and stores it. Most businesses, especially with the rising accessibility of cloud data services, can scale their storage to match any amount of data they would want to store.
There are numerous ways that a business could go about storing its data:
- Relational Databases
- NoSQL databases
- Cloud-based storage solutions
Although not an exhaustive list, the above are a glimpse of the different ways to store data. The correct storage system for a business will depend on the data itself, taking into account its volume, structure, and more.
In the data processing stage, data goes through a process of extraction and transformation. This stage focuses on adding, filtering, sorting, joining, or completely transforming datasets so that they’re more useful going forward. The process of transformation is vital as it allows data to fit into a format that data analysis tools can more easily work with.
For example, if a business was working with BigQuery, it would use commands to extract specific values from a data set. They could use json_extract Bigquery to extract elements, properties, and values from data, which would then give them the exact information they were looking for.
The processing stage is vital as it gets data ready for the targeted analysis phase, which is where the valuable insights that businesses are looking for begin to materialize.
At this stage, data is in a format that can be used in analysis, allowing data analysts to begin to find connections, notice trends, underline correlations, and draw insights. There are many distinct fields of data analysis that a business could use here. Especially with the advancements in ML and AI technology, data analysis is becoming more precise and expansive every single day.
The main goal of this stage is to take a large quantity of data and distill it into a few selective and powerful meanings. Data analysis allows us to get a deeper understanding of data and why it behaves the way it does.
The second last stage of the data pipeline is data visualization. In this stage, the results from data analytics are transformed into a visual medium. Especially in businesses, this is the stage where analysts create graphs, charts, dashboards, and other visual depictions that they can share with the rest of their business.
Data visualization is an important stage because it allows people that don’t have any training in data to rapidly understand what certain datasets are saying. This stage unlocks the power of data by increasing accessibility.
The final stage of the data pipeline is insight generation, the stage in which businesses draw meaning from the visual depictions of data. While a database of sales records and months may not immediately show much, a graph that shows sales spikes in certain months instantly gives a piece of information they can work with.
Insights that businesses take from data can be put to use in improving processes, optimizing operations, identifying opportunities, revealing challenges, and more. This stage turns data into action, pointing businesses to smart decisions they can make to improve in the future.
The data pipeline is a well-oiled machine, with data engineers building complex systems that move, process, and draw meaning from data in record-breaking times. As the data industry has matured, there is now a countless selection of tools that help facilitate data’s journey across the pipeline.
Whenever a business, user, or employee uses an insight gained from data, they’re really interacting with the fruits of this refined process. The more efficient the data pipeline becomes, the more rapidly businesses can optimize the process of drawing insights from data and streamline progress toward their company goals.