Businesses working with data must manage the high volumes they collect if they want to get valuable insights from all the information. Unfortunately, even veteran data specialists can get quickly overwhelmed by the variety, velocity, and volume involved with big data. This is why having the right pipeline for your data is crucial to turning raw data into high-calibre information you can analyze and utilize.
5 Key Elements
An effective data pipeline has five crucial elements and knowing what they help you ensure yours is appropriately constructed.
- Storage: This is the foundation for everything else because you need a place to store all your big data until it is accessed for more detailed analysis and tasks.
- Preprocessing: This is when big data is prepared for eventual analysis with the primary goal of cleaning up data by removing dirty inputs and structuring the information properly.
- Analysis: Data starts yielding useful insights comparable to existing data during this stage. Business leaders can start seeing data relationships and patterns.
- Applications: Specialized tools have the functionality required to turn processed data into actual information. In many cases, business intelligence software can create applications based on this.
- Delivery: The end of the entire data process can take many different forms, including business intelligence solutions, software-as-a-service applications, or web-based reporting.
Could Outsourcing Work?
Creating and managing your data pipeline is crucial, but do you have the right people for it? This might be the kind of thing that’s best outsourced to experts. Unless your business is in the data management industry, you will unlikely have specialists already working for you.
Accessing a start-to-finish delivery platform lets you enjoy the advantages of procedures and processes that are entirely automated. You’ll still have total control over what you get, as you can tell your outsourced team what specific data you want to be extracted, the precise quality control monitors you want to be defined, and the exact output specifications you want to be delivered.
When you set your data up to be automated, you’ll free up resources for other revenue-building activities. Automated software reduces your labour costs, whether you are paying people internally or outsourcing the work. Once that pipeline is functional, hiring someone to create an automated system and pipeline for your data will have lower recurring costs.
5 Best Practices
Simplicity is the best overall practice that you should follow, but your pipeline should involve five other best practices, too:
- Maintenance: Your pipeline can’t involve excessive inline scripting, shell files, or big scripts. The impact on future users is likely to be burdensome and inherently negative. Keep accurate records, create repeatable processes, and ensure strict protocols for enduring pipeline maintenance.
- Monitoring: Data visibility helps ensure security and consistency. Proper monitoring verifies data currently in the pipeline and reduces the risk of vulnerabilities.
- Predictability: The path of data should be easy to follow, so that origins tracing is easy when problems or delays happen. Eliminate dependencies since they can result in domino effects of multiple errors.
- Scalability: Have some auto-scaling available. Otherwise, your pipeline might be unable to respond to data ingestion changes. Know what kind of fluctuations are common and have the resources in place to deal with expansions and contractions in the scope of the data flow.
- Testing: Traditional software testing is very different from testing a pipeline for data. The architecture requires many disparate processes, and the actual data quality must always be evaluated. You need seasoned experts who can ensure there aren’t vulnerabilities waiting to be exploited.
One final thought that you should consider is how Forbes points out the importance of mastering data observability. Someone in your organization needs to be the go-to person responsible for overseeing the growth of data volumes in the near future. Companies with higher data intelligence and maturity will enjoy more observability and quality in their data.
