As your organization begins to collect increasingly large volumes of behavioral data, the challenge of capturing and managing the data flowing into your systems can be overwhelming. After all, data collection has a tremendous impact on the effectiveness of your data use cases and decision making.
To truly be a data-informed business, it’s important to pay attention to how data is collected and used throughout your organization. However, because data collection is only a tiny part of the overall data function, it’s often overlooked by organizations that prefer to spend their money on business intelligence (BI) tools or focus their attention on data modeling instead.
This leads companies to often rely on Extract Transform Load (ETL) tools or Customer Data Platforms (CDPs) to deliver data into their warehouse. While these tools are powerful for other purposes, they are not specifically designed for providing high-quality behavioral data into the data warehouse. Often, using these tools can lead to a messy, inefficient data asset that can hinder the organization.
Data collection is a crucial part of any data strategy and plays a key role in delivering high-quality data that is understandable and easy to work with for data consumers.
In this guide, you’ll learn:
- Why having a data collection plan matters
- 5 best practices to upgrade your data collection
- How can you collect data with privacy in mind?
- How Snowplow makes it easy to capture high-quality data
Why having a data collection plan matters
For any organization, data is a hugely valuable business asset. However, according to a report from Seagate Technology, 68% of the data that is available to organizations goes unused. That’s an awful lot of data that is going to waste.
Data goes to waste for a variety of reasons, from the information not being trusted to the data simply not being relevant to a particular use case. To drive the most value from your data, your organization should focus on collecting high-quality data that is as accurate and useful to your stakeholders as possible.
Working with data is not an easy task for any organization, and companies rely on data management best practices to drive the value of data for their organization. So, what is stopping companies from utilizing all of this data to their advantage?
Better data quality starts with better data collection
When you take ownership of your data collection process, stakeholders can trust your data and gather valuable insight from the data collected. This is why we’re big advocates of owning your data (which is what an open-source tool like Snowplow allows you to do).
While we won’t cover in detail the recent changes to privacy updates, ad blockers, and third-party cookies, we will tell you that navigating the task of capturing data from web is a challenge itself. Data is flowing in at faster rates and from a higher volume of sources, and companies are constantly making changes to their data stack to accommodate their needs.
In our opinion, data quality and data collection go hand-in-hand. If you have an effective data collection strategy that focuses on collecting data that is complete and accurate, your data will have greater meaning and will be more actionable for your stakeholders. With companies relying on data for a variety of use cases, it is more important than ever to have data that is reliable for analysis, reporting, and key decision-making. Often, when data is not accurate or complete, it comes down to how the data was collected in the first place.
When it comes to data collection, we recommend taking full ownership of your data. Owning your data allows you to:
- Control what data is collected.
- Where you store your data (important when dealing with privacy regulations such as GDPR and CCPA).
- Which use cases the data gets applied to.
- And, how the data is processed.
Taking full ownership of your data puts you in a strong position to ensure that your data is accurate and complete since you are no longer relying on third-party tools that use aggregated data samples and pre-determined forms of data for specific use cases.
With stricter regulations regarding third-party cookies from companies like Google and Apple, companies can no longer rely on them to collect everything to develop a single customer view. Staying ahead of the curve, companies are using first-party , server-side tracking to capture data from their own properties, which in turn overcomes the barriers faced by packaged analytics tools.
By collecting first-party data, companies are collecting data that is relevant to their business, eliminating an abundance of data that served no use for them in the first place. In this case, collecting more data is not always the best solution, as storing the raw unstructured data in your data lake can become a mess and create more problems for your organization.
5 best practices to upgrade your data collection
Organizations are aware of the value data brings to their business, but companies often overlook the process of collecting that information. It is common for an organization not to have a plan in place when it comes to collecting data.
If you are in the same boat, don’t panic. The following best practices can be helpful if you are looking to get started or improve your organization’s data collection strategy.
1. Make data easy to understand and work with
If users do not understand the information they have access to, they are less likely to work with it, which makes it difficult to become a data-informed organization. We recommend building a shared language for your data that makes it possible for all stakeholders involved to understand the data and put it to use.
When this happens, users can easily understand what data they are working with, any tests that were added to the data sets, and what use cases it can be applied to.
2. Constantly evaluate your data collection strategy
Your business needs are constantly changing. To keep up with them, it is important to keep a close eye on your data collection strategy and ensure it remains fit for purpose. In many companies, high volumes of data flows into the data warehouse or data lake from internal and external sources. To make sure the data being collected is fresh and relevant to your business, your organization should invest in data observability, which gives your company full transparency and control over the health of your data pipeline and allows you to quickly troubleshoot and resolve problems, minimizing data downtime.
3. Control how data is collected
At Snowplow, we firmly believe that you should have total control and ownership of your data infrastructure—including your data collection process. By regaining control of how data is collected, your business will have full visibility into how the data is being collected and processed. When this happens, your business can make adjustments for data quality upstream, ensuring that data is complete, accurate, and in the expected format early on the in data lifecycle.
Owning your data collection process builds assurance in the data and allows stakeholders to trust the data they are working with. Trust in the data is paramount within in the organization. Once lost, it’s extremely difficult to regain.
4. Schematize your data
Schemas allow you to define the structure of the data that your business collects. Schematizing data enables you to enforce data structures and capture data in a consistent format. it also allows you to evolve your data collection as your business needs change.
We recommend using a tool like Snowplow that makes it possible for users to define their own events and entities and update them over time. With Snowplow, you can ensure that your data provides a clear and easy-to-understand record of what actually happened.
5. Collect data with privacy in mind
Users are (rightly) becoming aware of their privacy online and are cautious of who has access to their data. On top of the ever-changing privacy regulations that favor users, it is becoming increasingly complex to manage these changes while still collecting high-quality data.
When it comes to data collection, you should strive to be as transparent as possible about what information you collect from a user and how you are processing that information within your organization to benefit your customer. Data should be used to enhance the end-user experience, whether through more relevant product recommendations or through streamlining the customer journey.
How can you collect data with privacy in mind?
With privacy being top-of-mind for consumers today and GDPR fines reaching as high as $56.6 million for companies, many businesses are faced with the challenge of collecting data with a user’s privacy in mind.
While some businesses still rely on packaged analytics solutions or third-party tools, lack of ownership of the data makes it impossible to verify the data is being handled in an ethical way. Safari, Google Chrome and other browsers are cracking down on third-party tools, and in some cases, are working on eliminating support for them altogether. Because of this trend, there are fewer data points your business can collect to identify users and their interests and then combine them together for a complete picture of your user’s behavior.
Organizations are also investing in server-side tracking to further develop a complete picture of a user’s behavior. Since every interaction a user completes does not take place in the browser, server-side event capture tracking will identify what is missing and lead to new opportunities to refine, expand, and take action on your analytics.
Finally, it’s now possible with Snowplow to capture data anonymously.
How Snowplow makes it easy to collect high-quality behavioral data
Here at Snowplow, we enable data-informed organizations to build a robust data collection strategy as they evolve their business. With Snowplow, data quality is a key focus, assuring that data being collected is accurate, complete, and valuable to your organization. Snowplow is a first-party behavioral data platform that gives you control and flexibility over what events you want to track, so you can collect what matters to your business. Snowplow gives users the ability to version their schemas, making it easier for users to reflect their changes over time.