Understanding Data Processing: Challenges of Scaling (2024)

Data Processing Guide

Data processing refers to the collection and interpretation of raw data to gain deeper understanding and insight. Data processing isn’t a standalone task, but a multi-step process that spans data collection, validation, transformation, aggregation, and storage.

People rely on data processing to make sense of otherwise complex data sets that would be cumbersome and time-consuming to sift through and manually analyze. With data processing, teams and organizations are able to rapidly sort through data and translate it into charts, graphs, and dashboards to identify trends, patterns, and important takeaways.

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A single platform to collect, unify, and connect your customer data

Common data processing challenges

Data opens doors to incredible insights, cost-savings opportunities, and pathways for growth. But even the best data processing efforts fall short when these obstacles aren't addressed head-on.

Volume: Handling massive amounts of data

The term “big data” refers to the ever-growing and complex bodies of information that the average organization has to deal with. The sheer volume of data creates bottlenecks as leaders try to manage what to process and how to best use it. (Roughly2.5 quintillion bytes worth of dataare being generated each day.)

As data volume grows, organizations need more storage space, memory, and processing power. This is where having a scalable data infrastructure comes into play, which is able to handle an increase in events or even a sudden influx (e.g.,cloud-based data warehousesthat easily scale up or down depending on volume).

Variety: Managing data from different sources and formats

Simply collecting data isn’t enough to derive value from it. That data is likely coming into your organization from a variety of different sources, and in a variety of different formats. Data processing helps classify and categorize all this incoming data so that it can be made sense of and give end-users the complete context.

For instance, data coming in from a social media management tool won’t automatically be compatible with data from sales records – it would need to be transformed and consistently formatted so that an analytics tool or reporting dashboard can recognize it.

This is wheredata mappingcan play a key role, which provides instructions on how to move data from one database to another (e.g., any transformations that need to take place to ensure proper formatting, which specific fields this data will populate in its target destination).

Veracity: Ensuring the quality and accuracy of data

There’s a common saying that:garbage in is garbage out. While data has the ability to drive insights and hone strategies, the major caveat here is that:the data needs to be accurate.

Ensuring quality data at scalerequires proper planning and the right technology. We recommend aligning your team around a universal tracking plan and validating databeforeit makes its way to production as two key steps in ensuring data accuracy.

With SegmentProtocols, you can automatically enforce your tracking plan so that bad data is blocked at the Source (not discovered days, if not weeks later, when it’s already skewed reporting and performance).

Compliance: Adhering to data privacy laws and regulations

There are numerous laws and industry regulations in regards to handling and processing data, with theGeneral Data Protection Regulation (GDPR)and theCalifornia Consumer Privacy Act of 2018 (CCPA)being two of the most notable.

Every person has rights when it comes to their personal data and how it’s being processed, stored, and used. Meaning: businesses must practice good stewardship from both a legal and ethical standpoint.

For example, a business outside the EU may still be subject to the GDPR if their customers are EU residents. Recently, the European Commission adopted theAdequacy Decision for the EU-US Data Privacy Framework(DPF), which stipulates that personal data transferred from the EU to the US must be adequately protected (in comparison to EU protections).

Not only is Twilio Segment DPF certified, but it also offers regional infrastructure in the EU to ensure businesses can remain fully compliant withdata residency laws.

Scaling data processing through automation

Automated data processinguses software, apps, or other technologies to handle data processing at scale through the use ofmachine learning algorithms and AI, statistical modeling, and more.

By taking out the need to manually complete data processing, businesses are able to reap the benefits of insights and analysis at a faster rate. Let’s go over a few more benefits.

Efficiency

It’s possible to use traditional data entry methods to account for all your information, but that doesn’t mean it’s the most efficient way. In fact, manual data processing pales in comparison to automated software and tools that allow you to perform these actions swiftly and at scale.

A great example of this is with Retool, a B2B platform that helps businesses build internal apps.Retool was rapidly growing, and needed a scalable data infrastructurein place to help them wrangle their data. By using Segment, they were able to save thousands of engineering hours it would have otherwise taken to build an infrastructure in-house that could adequately handle the collection, unification, democratization, and activation of their data.

Quality

Whether it's a simple miskey that affects one data record or an incorrect file name that mis-categorizes an entire data type, human error can pose a risk to data quality. To prevent this, and protect the integrity of your data, businesses need to quickly identify and correct bad data before it wreaks havoc on analysis, reporting, and activation.

This is why we recommend that every team is aligned around a universaltracking planto help standardize what data is being tracked, its naming conventions, and where it’s being stored.

Security

The best data processing tools and software are built with privacy and security in mind.

Take the matter of personally identifiable information (PII). Businesses processing that type of data have an obligation to protect it from outside risks like theft, ransomware, or data breaches.Luckily, businesses can automatically classify data according to risk level with the right tools to help strengthen security.

Data processing perfection with Segment’s Customer Data Platform

Segment’s CDP empowers businesses to collect, process, aggregate, and activate their data at scale and in real time. Here’s a look inside Segment’s data processing capabilities.

Validation and Transformation

Twilio Segment allows businesses to apply transformations to dataasit’s being processed, along with blocking any data entries that don’t adhere to a predefined tracking plan.

We also built our own customJSON parserthat does zero-memory allocations to make sure your data keeps flowing.

Deduplication

Segment deduplicates databased on the event’smessageId(rather than the contents of the event payload). Segment stores eventmessageIdson a 24-hour basis, and deduplicates data based on that time period. However, if a repeated event is more than 24 hours apart, Segment deduplicates data in the Warehouse or at the time of ingestion for a Data Lake.

Learn more about how we partitioned Kafka based on the ID of each message to ensure events are delivered only once.

Privacy Controls

Twilio Segment’sPrivacy Portaloffers features like the ability to handle user deletion requests at scale, automatically classify data according to risk level, and mask data entries to uphold theprinciple of least privilege.

GDPR Compliance

Along with beingDPF certified, Segment offersregional data processingin the EU to help companies stay compliant with the GDPR.

Every time a user deletion or suppression request is logged, Segment also stores the receipt in a database – creating anaudit trail. This provides proof and peace of mind that a user’s data has actually been deleted.