With an end-to-end Big Data pipeline built on a data lake, organizations can rapidly sift through enormous amounts of information. This helps you find golden insights to create a competitive advantage. The following graphic describes the process of making a large mass of data usable.
The steps in the Big Data pipeline
Understanding the journey from raw data to refined insights will help you identify training needs and potential stumbling blocks:
Organizations typically automate aspects of the Big Data pipeline. However, there are certain spots where automation is unlikely to rival human creativity. For example, human domain experts play a vital role in labeling the data perfectly for Machine Learning. As well, data visualization requires human ingenuity to represent the data in meaningful ways to different audiences.
Additionally, data governance, security, monitoring and scheduling are key factors in achieving Big Data project success. Organizations must attend to all four of these areas to deliver successful, customer-focused, data-driven applications.
Where do organizations get tripped up?
Here are some spots where Big Data projects can falter:
- Failure to clean or correct “dirty” data can lead to ill-informed decision making. When compiling information from multiple outlets, organizations need to normalize the data before analysis.
- Choosing the wrong technologies for implementing use cases can hinder progress and even break an analysis. For example, some tools cannot handle non-functional requirements such as read/write throughput, latency, etc.
- Some organizations rely too heavily on technical people to retrieve, process and analyze data. This shows a lack of self-service analytics for Data Scientists and/or Business Users in the organization.
- At times, analysts will get so excited about their findings that they skip the visualization step. Without visualization, data insights can be difficult for audiences to understand.
A lack of skilled resources and integration challenges with traditional systems also can slow down Big Data initiatives.
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