Data Science Pipeline and Its Workflow

The rate at which data is generated on our planet per hour has increased exponentially over the past few years. This surge in volume, velocity, and variety of data being produced has opened the doors to opportunities that were non-existent before. Leaders, Managers, and Stakeholders from different industries are more excited than ever before to unleash the hidden profits and efficiency gains that they can gain with their data. Data Science pipelines are the channel that bridges together the gap between raw data and intelligent insights. In this article, we’re going to dive deeper into Data Science pipelines and how different industries are using them to gain a competitive edge.

What Is Data Science Pipeline?

We can think of a data science pipeline as a unified system consisting of customized tools and processes which enable the organizations to get the maximum value out of their data. Depending on the factors like scale, nature of the problem at hand, domain, etc, the data science pipeline can be as simple as a simple ETL process, or in other cases, it could be very complex consisting of different stages with multiple processes working together to achieve the final objective. To get a deep understanding of the Data Science Pipeline, you could refer the Data Science Bootcamp Curriculum.

Why Is the Data Science Pipeline Important?

The data science pipeline of any organization is a fair reflection of how data driven the organization is and what’s the influence of derived insights on business-critical decisions.

Here are some of the points explaining the importance of a data science pipeline for an organization:

1. Data science pipeline enables business decisions driven by data. 
2. Data science pipeline helps to identify shortcomings of current processes. 
3. Data science pipeline makes an organization future ready. 
4. Data science pipeline allows for innovation and creativity by unlocking the previously inaccessible insights. 
5. Without a data science pipeline, the valuable data of the organization would be worthless. 

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How the Data Science Pipeline Works

Once deployed, the data science pipeline works by triggering a chain of steps called Stages of the pipeline. Each stage is usually responsible for achieving a specialized task e.g. extraction, loading, cleaning, transformation, modeling, evaluation, deployment, etc. The data science pipeline works by ensuring all the steps are triggered in the pre-configured fashion and it’s also responsible for emitting events which helps the external observers to monitor the different stages for debugging or other purposes. The Knowledgehut Data Science bootcamp curriculum explains how a pipeline should work. 

Data Science Pipeline Stages

Following are the various stages of the Data Science Pipeline : 

1. Data Acquisition: Data science pipeline starts with Data. In most companies, there are Data engineers that create tables for data collection and in some cases, you may use API to call data and make it available at the start of the pipeline. 
2. Data Splitting: The second stage is very important, which is data splitting. Here we break the dataset into train and test and validate data. Training data is used to train the model and we do this process in the initial stage only so that we can avoid data leakage. In some cases, we break the data after preprocessing. 
3. Data Preprocessing: In Data science we say it often, “Garbage in, Garbage out”, hence the quality of data matters if we want quality in outcome. This step is usually about cleaning the data and normalizing it. Generally, it takes care of getting rid of characters that are irrelevant. The purpose of normalization is to update the numerical values in data and bring them to a common scale without harming the actual difference in values. This is applicable wherever there is a huge range in any variable values. 
4. Feature Engineering: This step consists of multiple tasks which are missing value treatment, outlier treatment, Combinations (using current features to make new features), aggregations, transformations, and encoding for categorical data type. 
5. Labeling: This step is applicable in supervised cases which means you don’t have labels, but they are required and you feel that model will be better if we feed labels to it. There are two ways to approach this one is manual method and other is rule based. 
6. Exploratory Data Analysis: Some people do this part early but for simplicity it’s suggested to EDA after we have relevant features and labels in hand. EDA can guide you during feature engineering. 
7. Model Training: Model training refers to experimenting with different ML models for the task at hand and choosing the best model based on the problem at hand.
8. Performance monitoring: After training a model, it is important to spend time in model performance monitoring. We can make printouts about relevant model metrics, reports, charts and visuals that provide clarity about model performance.
9. Interpretation: It’s critical for businesses and to get knowledge of what is happening. There are many ways to approach the same. Global and local interpretations are the examples. 
10. Iteration: This step talks about modifying the model to get better performance. It takes the feedback loop into consideration. 
11. Deployment: Next step is Deployment which means to put the model under production. It depends upon systems, on cloud and also on how a company desires to use the built model. 
12. Monitoring: Post deploying the models, one has to keep monitoring the performance of it against unseen data. Oftentimes, the model needs to be re-trained due to a certain data drift i.e., the distribution of the unseen data has changed compared to what was used during training and validation phase. 

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Benefits of Data Science Pipelines

Following are the benefits of the Data Science Pipelines:

1. Organizations can leverage the insights gained with the help of the pipeline and hence make critical decisions faster.
2. Data Science pipelines allow organizations to understand the behavioral patterns of their target audience and then recommend personalized products and services.
3. Allows for efficiency in the processes by identifying the anti-patterns bottlenecks.

Characteristics of a Data Science Pipeline

1. Customizable and Extensible: The constituent components of a data science pipeline should be loosely coupled allowing for easy extensibility and ease of customization when it comes to use by different teams or departments.
2. Highly available and resistant to data corruption: Depending upon the rate / amount of ingestion, the data science pipeline should be elastic enough to handle surge in amount of data without causing any kind of corruption of data. 
3. Redundancy and recovery from disaster: An ideal data science pipeline should have controls in place to recover from a disaster and in the event of a disaster, continuity of business should not be impacted, or the impact should be minimized.

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How Do Different Industries Use the Data Science Pipeline?

How different industries use data science pipelines vary by use case. Below are some examples:

1. Health care industry: Physicians and Researchers rely on the data science pipelines to build models used to predict diseases with accuracy and hence allow for mitigating the risks at later stages.
2. Solar Power industry: In the solar manufacturing pipeline ( or any manufacturing pipeline in general ) , data science pipeline plays an important role from the collection of data from sensors to performing different analyses on data and deriving insights like efficiency of the cells and the root causes which might be degrading the efficiency.
3. FMCG industry: Data Science pipelines play an important role in the FMCG industry to make dynamic decisions in the context of distribution based on market demand and for forecasting the future demand etc.
4. Ecommerce: Ecommerce is one of the major use cases of Data Science pipelines where it plays an important role in different processes ranging from recommending the right products to customers to finding the best routes for maximizing deliveries and optimizing costs.
5. Aviation Industry: Dynamic pricing is one of the best use cases in the aviation industry which is driven by data science pipelines only based on the market demand and other events.

What Will the Data Science Pipeline Look Like in the Future?

There’s already a lot of innovation going on around making the data science pipelines more and more autonomous and driven by continuous manual or automated reinforcement in the form of the feedback based on the past results emitted by the pipeline. It’s very likely that the next generations of data science pipelines would be more and more autonomous with less and less requirement of manual intervention past the initial start.

Conclusion

In the digital era where tons of bytes of data are generated every hour by humans and machines ( IOT ), it’s no more a privilege to have data science pipelines setup in order to gain useful insights rather it has become a necessity and the organizations which are early adopters of the data driven approach are already starting to reap the benefits of their data whereas the other organizations are also either in-progress or heading fast towards building their own pipelines. 

Frequently Asked Questions (FAQs)

1. What are the steps in a data pipeline?

Ans: A Data Science pipeline generally follows the below steps – 

1. Project Ideation 
2. ETL pipeline setup 
3. Exploratory Data Analysis 
4. Model Experimentation 
5. Business metric validation 
6. Stakeholder presentation 
7. Deployment & Monitoring 
8. User Acceptance 

2. What is the ETL pipeline in data science?

Ans: ETL refers to “Extract -> Transform -> Load” pipeline, as the name suggests ETL pipeline refers to a set of tools which help with extraction, transformation and loading of data. A typical example of an ETL pipeline is the ELK stack.

3. What is the first step in the data science pipeline?

Ans: Data Sourcing or Data Acquisition is the first step in any data science pipeline. Here data is made available for further stages. 

4. What are data pipeline examples?

Ans:

1. Recommendation pipelines 
2. Stream data analytics 
3. Cloud migration data pipelines 
4. Forecasting data pipelines 

5. What is the purpose of the data pipeline?

Ans: The purpose of the data pipeline is to contribute to one of the several phases of the journey from raw data to useful business insights.

6. What is the difference between data pipeline and ETL?

Ans: ETL is a subset of the data pipeline. It is a series of processes extracting data from source, then transforming it and finally loading it to the destination. Whereas data pipeline describes a set of processes which move data from one system to another, sometimes transforming the data sometimes not. It’s a series of steps where data is moving.