Using the data science pipeline to simplify complex analytics

The challenges of complex data are endless. Attempting to gain a holistic view of your organization based on messy, disorganized data is virtually impossible. And with data coming in from multiple sources it becomes even more difficult to keep it organized.

However, unlocking the insights hidden inside complex data could lead to your next big opportunity and be the next step in your digital transformation journey. But gathering and analyzing this data is no small task. With trillions of amounts of data created every day, businesses are turning to robust data analysis platforms and tools to sort through large amounts of chaotic data.

One of these tools is the data science pipeline. It’s through this process that data is gathered from across your organization and transformed into well-structured datasets for further analysis.

What is the data science pipeline?

The data science pipeline refers to the process of ingesting large amounts of raw data from multiple sources into a single platform for further analysis and reporting. Additionally, it’s used to automate the process of gathering data from disparate sources which breaks down siloes and provides a comprehensive view of your entire business.

With businesses having more access to large volumes of data, the process has evolved to support big data. Today’s pipelines are able to accommodate big data’s three traits (velocity, volume, and variety) and are ideal for streamlining data processing. Additionally, today’s processes are becoming more scalable, allowing for rapid changes in data volume or velocity.

Why is the data science pipeline important?

Even before the outbreak of COVID-19, more businesses were moving away from a multichannel experience towards an omnichannel approach. In fact, an article from Harvard Business Review found that 73% of consumers use an omnichannel approach during their buying journey.

With an ever-increasing amount of customer touchpoints and business tools, it’s easy for data to become fragmented or siloed, making it difficult to access customer and corporate data. Even if you manage to gather data from across platforms and input it into an Excel spreadsheet, it likely contains data redundancies, duplications, and errors. The time, effort, and resources required to pull this data together are astronomical. And there’s incorporating live data into your analysis, which complicates the process even further.

A data pipeline automates this entire process, gathering data from all your business platforms into one location for rapid data analysis. The process also helps to eliminate data redundancies, ensuring all gathered data is up-to-date and error-free. 

Finally, with your data in a centralized location, you’ll have a comprehensive view of your entire organization which is crucial for making informed decisions that are backed by data.

How does the data science pipeline work?

Generally, the data science pipeline follows a series of steps when ingesting data:

Obtaining data: First, the solution gathers data from your entire technology stack and puts it into an easy-to-read format such as JSON or XML.

Cleaning: To ensure the data you’re working with is accurate and error-free, the solution will ‘clean’ the data. This involves scanning the data and deleting any duplicates or irrelevant information.

Modeling: Machine learning tools are utilized during this step to create rich data visualizations that identify crucial trends, patterns, or insights.

Interpreting: After you’ve identified insights and correlated them to the appropriate data, you can report your findings using charts, graphs, reports, or dashboards.

It’s important to regularly update your findings as new data becomes available to ensure accuracy.

Using the data science pipeline

Businesses from across industries have utilized the data science pipeline to make sense of complex data and analytics. 

For example, Spotify, a music streaming service with over 172 million subscribers worldwide, has access to an enormous amount of big data. To make sense of this data, they created a pipeline to help developers more accurately understand user preferences and trends. This enabled the company to provide more personalized music recommendations to listeners. It’s this personalization that makes them one of the most popular music streaming services on the market.

This is only a small glimpse into how the data science pipeline can be utilized. Because the tool is so versatile, it makes it ideal for a number of business cases, including:

Customer experience

A robust data pipeline like Domo can help to identify patterns in customer sentiment by utilizing Natural Language Processing and DSML modeling. This can help to identify potential hazards to the customer experience, allowing you to quickly mediate them. 

Additionally, a data pipeline can help to improve how predictive models perform over time, providing you with more accurate predictions.

Marketing attribution

Businesses looking to hone their marketing tactics may rely on marketing attribution to determine which channels are increasing conversion or sales. However, with more businesses moving to an omnichannel approach, the number of channels and overall data can quickly become overwhelming.

Tools like Domo rely on the data pipeline and proactive DSML modeling to affirm which channels are most likely to perform. With these insights, business teams can develop targeted, multichannel marketing strategies that increase ROI. 

Lead qualification

Lead qualification is a critical part of the sales process. It enables you to determine which of your leads are likely to move down the sales funnel and helps you appropriately allocate resources. However, with leads easily numbering into the thousands, it takes significant time to qualify leads—time that could be spent on more pressing tasks. 

The data pipeline takes much of the guesswork out of qualifying leads, disqualifying cold leads and presenting the most promising ones. Additionally, tools like Domo help you identify leads that are most likely to convert based on their persona. The solution then automatically applies a score based on how well they fit with your company’s successful conversion patterns. This allows you to prioritize these leads and determine where to focus efforts.

The future of data pipeline in data analytics

Tomorrow’s datasets are set to become increasingly more complex. New technology, rapidly evolving customer demands, and increasing customer touchpoints will only increase the amount of big data accessible to businesses. 

The versatility and agility of the data pipeline make it ideal for the future landscape. And it’s becoming smarter. Many of today’s pipelines allow for real-time data analysis—an imperative success factor in today’s always-on business environment. With real-time analysis and insights, businesses can make data-informed decisions more quickly, no matter how large the dataset.