Adopt and Adapt Adobe Experience Platform to deal with Large Datasets
Data multiples itself and grows at a rapid pace for large enterprises. Imagine Enterprise is dealing with a customer database of over 1 billion customers, a massive amount of data logs. This will not stop; this will keep on growing and the problem will multiply in no time. The problem will become even worse with complex datasets.
· Can we live with the problem of a large dataset?
· Can we sideline the problem of the large dataset?
· Can we get business results with the problem of the large dataset?
The answer is No, we can’t ignore it, nor we can bypass this problem. This needs to be resolved and addressed.
Onboarding of different Customer Data Platforms or likes of Adobe Experience Platform, will not solve a problem. The task must be taken in hand and methodology must be devised to ensure Adobe Experience Platform or any other CDP provides optimal results.
· Without an incremental increase in cost
· Without putting too much pressure on resources
· Without impacting the performance of the system
In real-world scenarios, the enterprise must deal with operational challenges irrespective of any Customer Data Platform as its just one of the components of the whole MarTech ecosystem. Data needs to be synchronized across the MarTech platform to deliver real-time experience, imagine the challenge when enterprises are dealing with large datasets.
Adapt Adobe Experience Platform to deal with large datasets.
Handling large datasets in Adobe Experience Platform (AEP) to optimize cost and performance involves careful planning, efficient data management, and leveraging scalable resources. Here are some strategies to handle large datasets effectively in AEP:
· Data Sampling and Subset Selection: Enterprises must focus on dealing with relevant and contextual data, thus Instead of processing the entire dataset for every task, consider using data sampling techniques or selecting relevant subsets that represent the overall superset of data. This approach can reduce processing time and resource requirements while still providing meaningful insights.
Processing of large datasets must be avoided to optimize the cost and resources.
· Data Partitioning: Partition the large dataset into smaller, manageable subsets. This partitioning can be based on time intervals, customer segments, or any other relevant criteria. By processing data in smaller chunks, Enterprises can improve performance and resource utilization.
· Incremental Data Processing: Data always keep growing and multiples in scale, If your dataset is continuously growing at a massive rate, consider using incremental data processing. Process new data as it arrives rather than reprocessing the entire dataset from scratch. This approach helps optimize processing time and resource usage.
· Data Compression and Storage Optimization: Use data compression techniques to reduce the storage requirements of Enterprise large datasets. Compressed data takes up less disk space, leading to cost savings on storage resources.
· Cloud-Based Solutions: Utilize cloud-based data storage and computing services. Cloud platforms offer scalable resources, allowing Enterprises to adjust processing power and storage based on their current needs. This flexibility helps optimize costs as Enterprises only pay for the resources they use.
· Distributed Computing: Leverage distributed computing frameworks, such as Apache Hadoop or Apache Spark, to parallelize data processing tasks. These frameworks can distribute the workload across multiple nodes, significantly improving processing speed for large datasets.
· Data Indexing: Implement efficient data indexing techniques to speed up data retrieval and analysis. Proper indexing helps in quick data access and reduces the time required for querying large datasets.
· Data Preprocessing: As mentioned in a previous response, perform data preprocessing to clean and reduce the size of the dataset. Removing irrelevant data and handling missing values can optimize data storage and processing costs.
· Query Optimization: Optimize the queries used for data retrieval and analysis. Ensure that Enterprises are using the appropriate data structures and algorithms for efficient data processing.
· Monitoring and Resource Allocation: Regularly monitor the performance of data processing tasks and resource usage. Use this information to fine-tune resource allocation and optimize cost and performance based on actual usage patterns.
By adopting these strategies, Enterprises can effectively manage large datasets in the Adobe Experience Platform, optimize costs, and improve overall performance.
Please note that the specific approach may vary based on the characteristics of your dataset, the tasks Enterprises are performing, and the resources available to you in the platform.
Build Adaptive Data Organization
Customer Data Platforms can only see success if adaptive data organization is built within an Enterprise. Why this is important as Enterprise will continuously face challenges pose by Data in the category of
· Volume
· Variety
· Velocity
· Veracity
· Value
Putting in place a regular data evaluation and refinement process will help Enterprises in handling data and will ensure ongoing cost optimization and optimal performance. As I stated earlier, deploy a blend of data techniques and processes in a continuous manner to ensure make best use of Adobe Experience Platform aka Customer Data Platform.
