In the modern data management landscape, data activation is a critical component that dictates the strategic utilization of data to achieve actionable insights and improved business outcomes. With organizations recognizing the value of not just accumulating data but effectively utilizing it, there has been an increasing focus on the best practices for data activation.
In this article, we will discuss data activation and the best practices you can perform to harness the full potential of this process.
What is Data Activation?
Data Activation is the process of transforming raw data into actionable insights to make enhanced business decisions. It is a bridge that links the gap between raw data and meaningful outcomes by unlocking its value. While data activation is commonly confused with reverse ETL, they are two different concepts. Reverse ETL is a crucial part of data activation.
Data activation aims to enhance customer data usage within an organization and use it to improve customer experience and business operations. Although you can break down data activation into many phases, it commonly involves three stages: ingestion, data unlocking, and execution.
- Data Ingestion: This phase involves extracting data from multiple data sources and storing it in a centralized repository platform for performing analytics.
- Data Unlocking: After centralizing the data, you must unlock its value by performing analytics and machine learning tasks. This can be used later to extract insights to optimize business performance.
- Data Execution: The last phase involves deep integration with data sources and other systems to leverage the enhanced data. One example of this phase could be sharing the data segments developed in the above two phases with marketing partners for driving advertisements.
Best Practices of Data Activation
While there are several measures that you can utilize to perform data activation, here are some best practices for this process:
Establish Clear Objectives
Before starting data activation, you should have clearly defined objectives of the process. An objective can include customer engagement, increasing sales, enhancing your services, etc. To define objectives, you can consider conducting internal audits and engaging with different departments to understand business challenges and opportunities. Once you have a clear objective, you can define what type of data you want to collect, what tools to use, and what practices to carry out.
Utilize Data Strategically
Using data strategically to make data-driven business decisions is the foundation of data activation. Here are some of the practices you can use in the data activation process to utilize data efficiently:
- Centralize Data: In many cases, data is scattered across multiple storage systems, which causes data fragmentation and silos. To avoid this issue, you can practice centralizing data in one storage system, such as a data warehouse or analytical platform. For centralizing data, you can use data integration tools like Airbyte to migrate it from one system to another of your choice.
- Comprehensive Data Modeling: Data modeling is the process of taking raw data after it is loaded in a cloud data warehouse and transforming it into a format ready for analytics. You might not get the desired results if you send unstructured and uncleaned data into a data activation platform like customer data platforms (CDP) and data management platforms (DMP). Therefore, a proper data activation program requires a comprehensive data modeling strategy to complement it.
- Data Analytics And Insights: Leverage data analytics tools like SAS Analytics, Azure Synapse, and Tableau to gain actionable insights from data. You can use these tools to conduct segmentation, perform exploratory analysis, and use predictive modeling to identify trends. This will exponentially increase your data quality and optimize your business operations.
- Experimentation And Iteration: Continuously experiment, test, and iterate on data-driven strategies. Use techniques like multivariate testing and A/B testing to optimize business operations.
Integrate CDP and DMP
Just as ELT practices can include the use of tools like Airbyte to automate the process, data activation utilizes tools and technologies like CDP and DMP to streamline data activation tasks. Both types of platforms store and manage the data, generate analysis, and help create a single customer view. However, the difference is that DMP concentrates on third-party data from various sources, and CDP aims to centralize first-party data from disparate sources. Examples of CDP include Insider, Bloomreach, and Segment, while examples of DMP are Salesforce DMP, Cloudera, and Adobe Audience Manager.
Although organizations have traditionally used DMP to perform data activation, it has drawbacks, such as additional steps to ensure privacy regulations. This is why most modern organizations use CDP for their data activation needs. However, the drawback of CDP is it primarily focuses on first-party data.
An ideal approach is to use a combination of both tools to perform data activation tasks. This will provide you with detailed customer insights from existing data using CDP and targeting capabilities from third-party data with DMP.
Foster Collaboration
Data activation involves a lot of tasks, from data ingestion to actionable business insights. Therefore, it should not be the sole responsibility of data scientists or IT professionals to perform this task; instead, it should involve cross-functional teams. The collaboration of teams will allow a unified view of data, reducing data silos and bringing diverse perspectives from different areas of expertise.
To foster collaboration, you can establish cross-functional teams for specific data activation tasks, set up regular communication channels, and conduct training sessions to build a common understanding of data tools and practices. By following this best practice, you can ensure your data activation efforts are comprehensive, reliable, and deeply integrated into the base of its operations.
Implement Robust Security And Compliance
Implement robust security measures to protect your data from data breaches, cyber threats, and unauthorized access. This includes applying encryption, access control, security audits, and employee training on data security practices. Conduct processes like Data Protection Impact Assessments (DPIAs) for high-risk data processing activities to first identify and then mitigate privacy risks.
Along with the security measures, follow the rules and regulations of data protection laws and implement data activation to comply with these regulations. If your organization operates multi-nationally, regulations like GDPR in Europe, CCPA in California, and many other worldwide compliances become necessary.
Security and compliance measures not only protect against breaches and legal consequences but also build trust with customers and marketing partners.
Perform Ongoing Monitoring
Data activation is a continuous process that requires constant monitoring for better decision-making and business optimization. Below are some of the tasks to perform regularly:
- Quality Assessment: Consistently evaluate the quality of your data to ensure it meets standards of completeness and relevance.
- Auditing: Conduct periodic audits of data management systems to evaluate data activation practices, ensure compliance, and identify areas for improvement.
- Data Cleansing: Regularly monitor and clean your data to identify inconsistencies, incomplete records, or outdated data to ensure accuracy and reliability.
Quality assessment, auditing, and data cleansing are just three processes you can perform regularly to check your data activation framework. By implementing ongoing monitoring, you can ensure data activation aligns continuously with your organization's strategic objective.
Simplify Data Activation With Airbyte
Data ingestion is one of the key tasks of data activation; automating the process of data integration can be a best practice for this. Consider using SaaS tools like Airbyte to integrate data between a range of platforms. Airbyte is a leading data integration platform that streamlines the process of data migration from many data sources to the destination of your choice. With 350+ pre-built connectors, it provides many options to synchronize any marketing platform, database, or application of your choice to a data warehouse or analytical platform.
Key features of Airbyte:
Custom Connectors:
If you don't find the desired pre-built connector for data activation, you can build a custom one within minutes using its connector development kit.
Transformation:
Being an ELT tool, Airbyte allows you to transform data after loading it to the destination. Internally, the tool supports a specialized transformation service called dbt (Data Build Tool) to handle transformations. This feature is critical for data activation, as the process requires format conversion between different data sources.
Change Data Capture:
Airbyte provides a CDC feature for many data sources. This allows you to keep updated data records by efficiently capturing and synchronizing only the changes made to data from a source or destination.
Conclusion
Data activation is a critical practice to store and manage data efficiently for the optimization of your business insights and operations. There are a lot of considerations while performing the data activation process. We have mentioned six of the best practices or considerations in this article. These involve establishing clear objectives, utilizing data strategically, integrating CDP and DMP, fostering collaboration, implementing security and compliance, and performing ongoing monitoring. By following these, you can leverage the full potential of data activation to optimize your business operations.
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