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A Comprehensive Guide to Implementing an eCommerce Data Pipeline

If you want to start an eCommerce business, you need to think about various aspects such as your products, marketing, and branding.

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A Comprehensive Guide to Implementing an eCommerce Data Pipeline

Running an online business in the eCommerce industry means that you are running a business that creates a lot of data along the way. This data helps you stay competitive and make decisions based on facts instead of guessing. And we all know that guessing is never good in the world of business.

If you want to start an eCommerce business, you need to think about various aspects such as your products, marketing, and branding. But for all of that, you need to rely on data that you will collect as a vendor every day.

This data deals with website traffic, sales records, product details, inventory details, marketing efforts, advertising numbers, customer insights, and so on. Almost all operations related to your business generate various amounts of data.

But what should you do when you get overwhelmed with the amount of data that is being generated?

Well, you need to transform all data coming from your data sources into actionable insights that could mean a lot to your business. And you can do that with a data pipeline. Take a look below and learn more about eCommerce data pipelines and how they can benefit your business.

1. What is a data pipeline?

A data pipeline is essentially a set of tools and activities used to move data from one process with its method of data storage and processing to another, where it can be stored and handled differently.

In the eCommerce realm, a data pipeline should be seen as an automated process of various actions used to extract and handle data from different sources into a format used for further analysis.

Take a look at various places where your business data can be gathered:

A data pipeline allows a user to extract and move data from all disparate apps and platforms into one central place. It transforms it into a usable format for reporting across sources.

Therefore, all businesses that rely on multichannel insights need to recognize how a data pipeline can help them improve their processes. Remember, before one can extract valuable insights from the gathered data, they first need to have a way to collect and organize it.

2. ETL pipeline vs data pipeline

ETL pipeline vs data pipeline

A lot of businesses that rely on this kind of technology also talk about ETL pipelines. Moreover, many have ditched the traditional pipelines and got ETL ones.

So, what is an ETL pipeline? And how is it separate from a traditional data pipeline?

An ETL data pipeline can be described as a set of processes that involve the removal of data from a source, its change, and then loading into the target ETL database or data warehouse for data analysis or any other need.

The target destination can be a data warehouse, data mart, or database. It is essential to note that having a pipeline like this requires users to know how to use ETL software tools. Some benefits of these tools include facilitating the performance, providing operational resilience, and providing a visual flow.

ETL stands for extraction, transformation, and loading. You can tell by its name that the ETL process is used in data integration, data warehousing, and data transformation (from disparate sources).

The primary purpose behind an ETL pipeline is to collect the correct data, prepare it for reporting, and save it for quick and easy access and analysis. Along with the right ETL software tools, such a pipeline helps businesses free up their time and focus on more critical business tasks.

On the other hand, a traditional data pipeline refers to the sets of steps involved in moving data from the source structure to the target system. This kind of technology consists of copying data, moving it from an onsite location into the cloud, and ordering it or combining it with various other data sources.

A data pipeline is a broader term that includes ETL pipeline as a subset and has a set of proceeding tools that transfer data from one process to another. Depending on the tools, the data may or may not be transformed.

3. Are there any other kinds of data pipelines?

Keep in mind that there are quite a few data pipelines that you could make use of. Let’s go through the most prominent ones that have already worked for many other businesses.

  • Open-source vs proprietary. If you want a cheap solution that is already available to the general public, seeking open-source tools is the right way to go. However, you should ensure that you have the right experts at the office and the needed resources to expand and modify the functionalities of these tools according to your business needs.
  • On-premise vs cloud-native. Businesses still use on-premise solutions that require warehousing space. On the contrary, a cloud-native solution is a pipeline using cloud-based tools, which are cheaper and involve fewer resources.
  • Batch vs real-time. Most companies usually go for a batch processing data pipeline to integrate data at specific time intervals (weekly or daily, for instance).

This is different from a real-time pipeline solution where a user can process data in real-time. This kind of pipeline is suitable for businesses that quickly process real-time financial or economic data, location data, or communication data.

4. How to determine what data pipeline solution you need?

How to determine what data pipeline solution you need

You might want to consider a few different factors if you want to determine your business needs’ exact type of data pipeline. Think through the whole business intelligence and analytics process at your company and ask yourself these questions:

  • How often do we need data to be updated and refreshed?
  • What kind of internal resources do we have to maintain a data pipeline?
  • What is the end goal for our data?
  • What types of data do we have access to?
  • How should it be extracted, arranged, and maintained?

Keep in mind that you really can build a data pipeline all on your own. But connecting your various data sources and building a sustainable and scalable workflow from zero can be quite a feat.

If you want to do this and consider this option, think about what the process would look like and what it would take. A data pipeline consists of many individual components, so you will have quite a bit of thinking to do:

  • What insights are you interested in?
  • What data sources do you currently have access to/are using?
  • Are you ready to gather additional solutions to help you with data storage reporting?

If all of this seems overwhelming, do not worry. Most businesses out there don’t have the expertise (or resources) to devise a data pipeline independently. However, if you connect the right expert to your data storage, you might achieve this goal. It will not happen quickly, but it will be worth it.

5. Final thoughts

Take another look at the most critical parts of this guide and evaluate your business needs. Only then can you be able to come up with the right solution for your business.

Again, if you can’t make up your mind and struggle between ETL and traditional data pipelines, know that ETL data pipelines are used for extraction, transformation, and loading, while data pipeline tools may or may not include change!

Keep this in mind since it can make a difference even though it is just one functionality.

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Big Data

Benefits of Data analytics for Your Business

One of the most significant benefits of Big Data analytics is that businesses can now deliver personalized experiences at scale.

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Run a Better Business With These Key Tips

Data Analytics provides the path for creative solutions, hyper-targeted promotional techniques, and personalized promotional strategies by allowing you to define your consumer clearly. The potential advantages of participating in Big Data analytics are well understood: these technologies offer cost reductions, increased productivity, and an enhanced decision-making process.

According to research, 90 percent of collaborating enterprise clients stated that data and analytics are critical to their company’s digital development objectives. Students are opting for data analytics courses to join this dynamic industry and be a part of the future driven by advanced technologies and innovations. There are distinct benefits to adopting data analytics into your organization since it generates effective results, like the ones listed below:

1. Better Decision-Making:

One of the most significant advantages of Big Data analytics greatly enhances decision-making procedures. Businesses are progressively turning to data before making decisions instead of depending solely on intuition. Businesses are better positioned to produce accurate forecasts when big data combines AI, machine learning, and data mining.

In essence, you’re streamlining what was formerly a years-long collection of knowledge and relying on technology to achieve results swiftly and with less experimentation. Another important use is categorization utilizing AI-analytics methods.

2. Personalization:

The performance indicator that most advertising agencies aspire for is to provide a personalized experience. As a result, one of the most significant benefits of Big Data analytics is that businesses can now deliver personalized experiences at scale.

Big Data fuels prediction engines and pricing optimization, and it gives businesses a complete picture of their customers, allowing them to customize to each person.

SEO Tactics Small Businesses

3. Predictive Modeling:

Organizations may use predictive modeling to determine what’s causing problems and anticipate future results. Predictive analytics technologies are used in financial services to assess fraud threats and establish trustworthiness.

These technologies are also being used to assist climate warming and sustainability policies, nuclear power, oil exploration, and other topics.

4. Retention and Loyalty:

According to Market experts, the major accelerators for improving sales and customer fulfillment are social networking site promotion, email marketing, and behavioral studies. Social media management, website analytics, behavioral data, and competitive analysis are all used to assist organizations in figuring out what their customers want.

5. Forecasting:

Marketers can forecast customer experience and outcomes more precisely using Big Data analytics mixed with statistical approaches and historical information. They can then use the information to develop long-term plans.

6. Optimized Messaging and Solutions:

Data analytics can determine what clients want and how they respond to communications and then integrate those findings into promotional, production, and sales tactics.

7. Accurate Measure of Campaign ROI:

For marketers, attribution has always been a difficult task. Advanced analytics technologies can assess the effectiveness of all campaigns, messages, and techniques that contribute significantly to a customer’s conversion.

These findings provide a platform for future operations for businesses. Companies can also use the information to make better financial planning decisions.

8. More Accessible Analytics:

By democratizing the process of creating plans and implementing the sense of the findings, AI is increasingly assisting in getting data analytics into the hands of more people. Organizations may obtain comprehensive insight into their business using self-service, intelligent solutions across all divisions.

So, if you are planning to pursue a career in data analytics, you must start by signing up for analytic data courses now!

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