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Building a (Big) Data Pipeline the Right Way

Collecting and analyzing data has been around for some time now. Yet all too often, the former takes hold of companies with such force that one doesn’t care about the use of the data. There’s a reason we had to come up with a name for this phenomenon – “dark data”.

Unfortunately, data is often collected for no good reason. It’s understandable – a lot of internal data is collected by default. Today’s business climate requires the use of many tools (eg CRM, accounting journals, invoicing) that automatically create reports and store data.

The collection process is even broader for digital businesses and often includes server logs, consumer behavior, and other tangential information.

Correctly build a (big) data pipeline

Unless you are in the data as a service (DaaS) business, there is no benefit to simply collecting data. With all the hype surrounding data-driven decision making, I think a lot of people have lost sight of the forest for the trees. The collection of all forms of data becomes an end in itself.

In fact, such an approach costs the company money. There is no free lunch – someone has to set up the collection method, manage the process, and keep tabs on the results. These are wasted resources and finances. Instead of aiming for the amount of data, we should be looking for ways to improve the collection process.

Humble beginnings

Almost all businesses begin their data acquisition journey by collecting marketing, sales, and account data. Some practices such as Pay-Per-Click (PPC) have proven to be incredibly easy to measure and analyze through the lens of statistics, making data collection a necessity. On the other hand, relevant data is often produced as a by-product of regular day-to-day sales and account management activities.

Businesses have already realized that sharing data between marketing, sales and account management departments can lead to great things. However, the data pipeline is often clogged and relevant information can only be accessed in the abstract.

Often, the way departments share information lacks immediacy. There is no direct access to the data; instead, it’s shared in meetings or in-person discussions. This is just not the best way to go. On the other hand, consistent access to new data can provide departments with important information.

Interministerial data

Not surprisingly, cross-departmental data can improve efficiency in several ways. For example, Ideal Customer Profile (KPI) lead data across departments will point to better sales and marketing practices (for example, a more defined content strategy).

Here’s the burning problem for every business that collects a large amount of data: It’s scattered. Potentially useful information is left all over spreadsheets, CRMs, and other management systems. Therefore, the first step should be not to get more data but to optimize current processes and prepare them for use.

Combining data sources

Fortunately, with the advent of Big Data, companies have been thinking in detail about information management processes. As a result, data management practices have made great strides in recent years, greatly simplifying optimization processes.

Data warehouses

A commonly used principle of data management is to create a warehouse for data collected from many sources. But, of course, the process isn’t as simple as integrating a few different databases. Unfortunately, data is often stored in incompatible formats, which makes standardization necessary.

Usually, integrating data into a warehouse follows a 3-step process: Extract, Transform, Load (ETL). There are different approaches; However, ETL is probably the most popular option. Extraction, in this case, means taking data that has already been acquired from internal or external collection processes.

Data transformation is the most complex process of the three. This involves aggregating data from various formats into a common format, identifying missing or repetitive fields. In most businesses, doing all of this manually is out of the question; therefore, traditional programming methods (eg, SQL) are used.

Loading – Moving to the warehouse

The upload essentially involves moving the prepared data to the warehouse in question. Although this is a basic process of moving data from one source to another, it is important to note that warehouses do not store information in real time. Therefore, separating the operational databases from the warehouses allows the former to separate as a backup and avoid unnecessary corruption.

Data warehouses generally have a few critical features:

  • Integrated. Data warehouses are an accumulation of information from heterogeneous sources in one place.
  • Time variant. The data is historical and identified from a given period.
  • Nonvolatile. Previous data is not deleted when new information is added.
  • Subject oriented. Data is a collection of information based on subjects (personnel, support, sales, revenue, etc.) rather than being directly related to ongoing operations.

External data to maximize potential

Building a data warehouse isn’t the only way to get more from the same amount of information. They contribute to interministerial efficiency. Data enrichment processes could contribute to intra-departmental efficiency.

Enrichment of data from external sources

Data enrichment is the process of combining information from external sources with internal sources. Sometimes, enterprise-level companies may be able to enrich data from purely internal sources if they have enough different departments.

While warehouses will work almost the same for almost any business that deals with large volumes of data, each enrichment process will be different. Indeed, the enrichment processes depend directly on the objectives of the company. Otherwise, we would go back to square one, where data is collected without a suitable end purpose.

Enrichment of inbound leads

A simple approach that could be beneficial for many businesses would be the enrichment of inbound leads. Regardless of the industry, responding quickly to requests for additional information has increased sales efficiency. Enriching prospects with professional data (for example, information about public companies) would automatically categorize prospects and respond more quickly to those who come closer to the Ideal Customer Profile (KPI).

Of course, data enrichment should not be limited to business services. All kinds of processes can be powered by external data – from marketing campaigns to legal compliance. However, as always, the details should be kept in mind. All data should serve a business purpose.


Before venturing into complex data sources, cleaning up internal processes will bring better results. With dark data accounting for over 90% of all data collected by businesses, it’s best to look inside and optimize current processes first. Including more sources will exile some potentially useful information due to inefficient data management practices.

After creating robust data management systems, we can move on to complex data collection. We can then be sure that we are not missing anything important and that we can match more data points to gain valuable information.

Image Credit: rfstudio; pexels; Thank you!

a53923475c6105a218781f678c17f6e5?s=125&d=mm&r=g Building a (Big) Data Pipeline the Right Way

Julius Cerniauskas

CEO at Oxylabs

Julius Cerniauskas is Lithuanian technology industry leader and CEO of Oxylabs, covering topics on web scraping, big data, machine learning and technology trends.



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