The data preparation process is critical, due to the importance of maintaining clean, high-quality data for operational and analytical workloads. Here are 7 essential data preparation steps, and another big move to consider.
Table of Contents
Step 1: Collection
Step 2: Discovery and Classification
Step 3: Cleansing
Step 4: Structuring
Step 5: Transformation, Enrichment, and Anonymization
Step 6: Validation
Step 7: Delivery
The Next Step: Productizing Data Preparation Flows
The 7 Data Preparation Steps
Step 1: Collection
We begin the process by mapping and collecting data from relevant data sources. Data collection is an ongoing process that should be conducted periodically (in some cases, continually, in real time), and your organization should implement a dedicated data extraction mechanism to perform it. The more fragmented and unstructured your data is to begin with, the harder the data preparation process that follows.
Step 2: Discovery and Classification
Before data is ingested into a data pipeline, you need to gather it from across your organization, and then sift through it to pinpoint the relevant data for the specific workload. This part of the process requires an in-depth understanding of the data sources and systems, the context of the data, and more. It often requires tools that can automatically discover and classify data.
Cleaner data leads to optimized business practices, productivity, sales cycles, and decision making.
Step 3: Cleansing
Faulty (“dirty”) data is a major problem that will affect the downstream insights you reach. Surveyed organizations believe that about a third of their data is inaccurate, which goes to show how widespread the issue is. This data preparation step involves the detection of corrupt, irrelevant, or missing data – and the correction process during which it is completed, replaced, and/or modified.
Step 4: Structuring
We’ve discussed the importance of data structuring earlier on during the collection step, and here it is again. Companies must decide on a specific data structure that the preparation process should map into.
Structuring the data based on business entities (such as customers, devices, etc.) can be highly effective in making the data suitable for both analytical and operational workloads. Structure the collected data using common business terminology and a schema that matches the business objects at the core of your workloads. Take this opportunity to create a data layer that your data consumers will understand – whether technical or business.
Data must be anonymized to protect sensitive information and comply with privacy laws.
Step 5: Transformation, Enrichment, and Anonymization
This is one step that involves three critical actions. The data you’ve gathered must be modified to fit the chosen structure from the previous step. Along the way, it may need to be transformed to a different data format, (e.g., another date or address format), or enriched with additional information to add context, to make it more valuable.
Finally, to protect sensitive information, perform data anonymization to ensure that sensitive personal information cannot be exposed. Data security and compliance with privacy regulations are top priorities. According to Forbes, 2020 broke every known record in terms of data lost due to such breaches, so don’t let it happen to you in 2021.
Step 6: Validation
This step checks the data and validates that it meets your data quality standards. It will ensure that the transformed data is not only accurate and ready for your data structure, but also fits the purpose it is meant to serve – and is suitable for your targeted business applications and consumers.
Step 7: Delivery
Data delivery assures that the prepared data is securely pipelined to the relevant target applications, data lakes, and data warehouses. Support of various delivery methods is required in order to keep the data fresh and to minimize the lode on both source and target systems. Some of the common delivery methods include batch (ETL), streaming, messaging, change data capture (CDC), ODATA, and JDBC
The Next Step: Productizing Data Preparation Flows
There’s a lot to consider when choosing your data pipeline technology. First, select a single, scalable solution that can support all the above steps – and be able to deep-dive into each one of them, while providing an overview of the entire process.
Second, make sure that all data preparation steps are integrated, to make the process as smooth, simple, error-free, ongoing, and updated, as possible.
Finally, a potential Step 8: Consider moving from self-service to automation by productizing data preparation flows, enabling their reuse across different analytical and operational workloads, and then measuring their usage, as well as the value they bring to the business.
