Data Science Pipeline

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What is the data science pipeline?

The data science pipeline refers to the process and tools used to gather raw data from multiple sources, analyze it, and present the results in an understandable format. Companies utilize the process to answer specific business questions and create actionable insights based on real data. All available datasets, including external and internal, are analyzed to find this information.

For example, your sales team wants to set realistic goals for next quarter. The data science pipeline enables them to gather data from customer surveys or feedback, historical purchase orders, industry trends, and more. From here, robust data analysis tools thoroughly analyze the data and identify key trends and patterns. Teams can then create specific, data-driven goals that will increase sales.



Data science pipeline vs. ETL pipeline

While both the data science and ETL pipelines refer to the process of moving data from one system to another, there are key differences between the two, including:

  • The ETL pipeline stops when data is loaded into a data warehouse or database. The data science pipeline continues past this and often triggers additional flows and processes.
  • ETL pipelines always involve data transformation whereas data science pipelines do not.
  • Data science pipelines typically run in real-time while ETL pipelines transfer data in chunks or on a regular schedule.

Why is the data science pipeline important?

Businesses create billions of pieces of data every day and every piece of data contains valuable opportunities. The data science pipeline unlocks these opportunities by gathering data from across teams, cleaning it, and presenting it in an easily digestible way. This allows you and your team to make rapid decisions that are backed by data. 

Data science pipelines enable you to avoid the time-consuming and error-prone process of manual data collection. By utilizing intelligent data science tools, you’ll have constant access to clean, reliable, and updated data that is crucial for staying ahead of the competition.

Benefits of data science pipelines

  • Increases agility to respond to shifting business needs and customer preferences. 
  • Streamlines access to company and customer insights.
  • Speeds up the decision-making process.
  • Enable users to delve into insights on a more granular level.
  • Eliminates data siloes and bottlenecks that delay action and waste resources.
  • Simplifies and speeds up the data analysis process.

How the data science pipeline works

Before moving raw data through the pipeline, it’s vital to have specific questions you want data to answer. This helps users to focus on the right data needed to uncover the appropriate insights. 

The data science pipeline is made up of several stages which include:

Obtaining data

This is where data from both internal, external, and third-party sources is collected and transformed into a usable format (XML, JSON, .csv, etc.). 

Cleaning data

This is the most time-consuming step of the process. Data may contain anomalies such as duplicate parameters, missing values, or irrelevant information that must be cleaned prior to creating a data visualization. 

Cleansing data can be divided into two categories:

  • Examining data to identify errors, missing values, or corrupt records.
  • Cleaning data, which involves filling holes, correcting errors, removing duplicates, and throwing away irrelevant records or information.

You may need to recruit a domain expert during this stage to help understand data and the impact of specific features or values.

Exploring and modeling data

After data has been thoroughly cleaned, it can then be used to find patterns and values using data visualization tools and charts. This is where machine learning tools come into play. Using algorithms such as classification accuracy, confusion matrix, logarithmic loss, and others, you can find patterns and apply specific rules to data or data models. These rules can then be tested on sample data to determine how performance, revenue, or growth would be affected.

Interpreting data

The objective of this step is to first identify insights and correlate them to your data findings. You can then communicate your findings to business leaders or fellow colleagues using charts, dashboards, or reports.

Revising data

As business requirements change or more data becomes available, it’s important to periodically revisit your model and make revisions as needed. 


How do different industries use the data science pipeline?

The data science pipeline benefits teams regardless of the industry. Some examples of how various teams utilize the process include:

  • Risk analysis: Financial institutions utilize the process to make sense of large, unstructured data to understand where potential risks from competitors, the market, or customers lie and how they can be avoided.

    Additionally, organizations have utilized Domo’s DSML tools and model insights to perform proactive planning and risk remediation
  • Research: Medical professionals rely on data science to aid with research. One study relies on machine learning algorithms to aid with research on how to improve image quality in MRIs and x-rays.

    Companies outside the medical profession have seen success using Domo’s Natural Language Processing and DSML to determine how specific actions will affect the customer experience. This enables them to address risks ahead of time and maintain a positive experience. 
  • Forecasting: The transportation industry uses data science pipelines to predict the impact on traffic that construction or other road projects will have. This also helps professionals plan efficient responses.

    Additional business teams have seen success using Domo’s DSML solutions to forecast future product demand. The platform features multivariate time series modeling at the SKU level, enabling them to properly plan across the supply chain and beyond.

What should I look for in data science tools?

The best data science tools are those that unite machine learning, data analysis, and statistics to create rich, detailed data visualizations. With the right tools in place, users (regardless of their technical skill) can identify trends and patterns and make smarter decisions that accelerate business growth and revenue.

Domo’s data visualization tools feature easy-to-use, customizable dashboards that enable users to create rich stories using real-time data. From pie charts and graphs to interactive maps and other visualizations, Domo helps you create detailed models with just a few clicks. Plus, Domo’s Analyzer tool makes it easy to get started by suggesting potential visualizations based on your data.

Powered by machine learning and artificial intelligence, Domo’s data science suite features a number of tools that simplify the process of data gathering and analysis. With a suite of over 200 connectors, data from across teams is easily brought into Domo’s intelligent platform. Here, users can customize and edit datasets and models to identify risks, forecast needs, and maximize revenue.


What will the data science pipeline look like in the future?

The data science pipeline is the key to unlocking insights locked away in increasingly large and complex datasets. With the amount of data available to businesses only expected to grow, teams must rely on a process that breaks down datasets and presents actionable insights in real-time.

As new technology emerges, the agility and speed of the data science pipeline will only improve. The process will become smarter, more agile, and accommodating, enabling teams to delve deeper into data than ever before.



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