Blockchain Data Analytics For Dummies. Michael G. Solomon

      1 The importer requests the product from the exporter.

      2 The importer applies for a letter of credit from a local bank on behalf of the exporter.

      3 The importer’s bank issues a letter of credit and sends it to the exporter’s bank.

      4 The exporter ships the product.

      5 Based on the terms of the letter of credit, the exporter may receive partial payment while the product is in transit. If partial payment is executed, the exporter’s bank claims the payment due and the importer’s bank transfers the specified payment.

      6 When the importer receives the product, the importer notifies the bank and the importer’s bank transfers the remaining funds to the exporter’s bank.

      Believe it or not, this process is simplified! I didn’t even touch on export licenses or bills of lading. Even with this simple scenario, you can see that the process has many steps. In a real application, some of these steps might occur at different times and some might occur at the same time (such as in Step 5). It would be helpful if the blockchain stored status information about how the transaction was carried out, as opposed to storing just the amount transferred from one account to another.

      Event logs provide that functionality. No events occur by default; smart contract programmers must request each one. Most smart contract code includes at least minimal event invocations. A best practice when developing a smart contract is to invoke an event any time a package of work of interest to the application’s user is completed. That description of when to use events is a loose one and open to interpretation.

      Knowing how the smart contract code that will supply data for your analytics projects works is important. In Chapters 5 and 6, you learn how to get smart contract source code and how to use it to build your data acquisition plan. But until then, remember that analyzing data in a blockchain environment requires familiarity with far more than just blockchain data.

Aligning Blockchain Data with Real-World Processes

      Although understanding the data available through transactions, events, and contract state is important, you must understand what that data represents before you can make much sense out of it. An important part of any data analytics project (blockchain or traditional data) is to align data with the real world. In a blockchain environment, that understanding starts with smart contracts.

      Understanding smart contract functions

      You can think of smart contracts as programs that contain data and the functions to manipulate that data. One way to help understand smart contracts is to think of state data as nouns and functions as verbs. Associating smart contract elements with parts of speech helps to understand each element’s purpose. You store data that represents something in the real world, such as an order, a product, or a letter of credit.

      Functions provide the actions that applications take on data, such as creating an order, createOrder(), shipping a product, shipProduct(), or requesting a letter of credit, requestLoC(). Data analytics is focused on extracting meaningful and actionable information from data. It is important to understand the data available to you, along with how that data was created and what real-world things and processes it represents. Smart contract functions provide the roadmap to how data gets added to the blockchain and what that data means.

      Assessing smart contract event logs

      One process early in any data analytics project is assessing your available data. In a blockchain environment, that step should include assessing any events related to the smarts contracts you’ll examine. One way to view events is as documentation of internal operations. These microtransaction artifacts often provide a level of granular data that you can’t get anywhere else. Don’t ignore the event logs — they may provide your best description of blockchain data and what it really represents.

      Ranking transaction and event data by its effect

      After you have a catalog of the data available to you, rank each data item’s importance by its effect. A data item has greater effect when it corresponds to some entity attribute or action in the real world. Data that represents a letter of credit’s approval status change is likely more important than the field that records the page count of the letter of credit document. All data is not equal. It is always up to you, the analyst, to focus on the important data and not spend too much time on data with little value. Properly ranking data value by its effect is a learned skill, and one that takes practice.