How to prepare your data for Web3

In our last blog post, we explored the concept of Web3 and how it’s important for publishers to consider how they might use it in the future.

But before they get to that stage, there’s one fundamental point to remember: while blockchain technology can help with data privacy and direct communication with advertisers, the raw data still needs to become foundational data before it’s ready for any Web3 application or business intelligence.

The difference between raw and foundational data

It can be easy to make the mistake of thinking all you need to do is to feed your raw data – the data that comes direct from each data source across an organization, which could be tens, or it could be hundreds. Crucially each set of raw data is likely to be in a different format.

Meanwhile, foundational data is the result of the raw data being transformed into more understandable insights through a series of processes including data cleansing, verification, formatting and sorting for intended use, labeling, and governance.

Why foundational data is important for Web3

When you use raw data (for anything, not just Web3 applications), you run the risk of:

• Incomplete data. When data isn’t delivered as expected, it can create skewed results, and this can happen from errors in vendor systems or network downtime – these generate gaps resulting in incomplete data.
• Data corruption. When data is delivered in incorrect formats or there’s an error during transmission, it can become incorrect or unreadable. Data corruption leads to damaging existing datasets and interrupts workflows.
• Duplicate data. Even if the data you get is correct, you can still end up with duplicate entries – which if left alone can be hard to detect and remove.
• Unmatchable data. When one source or vendor formats the same type of data differently, it creates mismatched datasets.

READ MORE: How to deploy DataOps: Step 2 – Create foundational data

With the issues that come with raw data, using transformed, foundational data becomes essential for making informed business decisions. Understanding basic metrics such as Revenue per User and Impressions Delivered is dependent on reliable foundational data.

Needless to say, using foundational data offers several benefits that counter the issues with using raw data. Those include improved data quality, better scalability, lower cost, better use of talent (if the data pipeline is automated – taking care of repetitive tasks), and saving time compared to managing raw data.

To take your raw data and turn it into foundational data, it’s best to use an ETL (Extract, Transform, Load) tool or platform that automates the data pipeline for your business.

Preparing your data for Web3

For lots of businesses, raw data comes from a wide range of disparate sources and it can be difficult to manage, organize, and use in a meaningful way. As mentioned above, one solution to this issue is using an ETL process, which involves the following steps:

• Extract. Using a tool or platform (such as Switchboard, or a custom-built solution) you can extract the raw data from all your sources.
• Clean. At this step, the collected raw data is verified to be of good quality before being transformed. This usually involves a process of standardization and normalization to remove any invalid or incomplete raw data.
• Transform. This step takes the clean data and applies processing rules to convert it into an appropriate format ready for the desired target destination.
• Load. Once the data has been transformed, you can move it to your target destination such as a data warehouse (stored either locally or on the cloud).
• Analyze. To check the data pipeline is running properly, i.e. no bottlenecks or errors, you’ll need to analyze it and evaluate the output.

The most important application of this ETL process is in business intelligence, where businesses can use the loaded data to develop strategies for analysis and information management.