Business leaders are growing weary of making further investments in business intelligence (BI) and big data analytics. Beyond the challenging technical components of data-driven projects, BI and analytics services have yet to live up to the hype.
Early adopters and proponents were quick to frame solutions as miraculous reservoirs of insight and functionality. However, big data has not met many C-level executives’ expectations. This disconnect has many executives delaying projects, filing end-to-end big data solutions under “perhaps, in the future.”
Increasing interest and investment in distributed computing, AI, machine learning and IoT are generating practical and user-friendly tools for ingesting, storing, processing, analyzing and visualizing data. Still, the necessary IT, data-science and development operations are time-consuming and often entail large resource displacements.
This is where data pipelines are uniquely fit to save the day. The data pipeline is an ideal mix of software technologies that automate the management, analysis and visualization of data from multiple sources, making it available for strategic use.
WHAT DOES A DATA PIPELINE DO?
The straightforward answer is “whatever you need it to do,” meaning that there are virtually endless and evolving ways of designing and using data pipelines. However, at a strategic business level, data pipelines have two fundamental use cases:
- Data-enabled functionality – From automated customer targeting and financial fraud detection to robotic process automation (RPA) and even real-time medical care, data pipelines are a viable solution to power product features regardless of industry. For example, adding data-enabled features to the shopping cart of an e-commerce platform has never been easier than with today’s streaming analytics technologies. The ability to easily create flexible, reliable and scalable data pipelines that integrate and leverage cutting-edge technologies pave the way for industry innovations and keep data-driven businesses ahead of the curve.
- BI and analytics – Data pipelines favor a modular approach to big data, allowing companies to bring their zest and know-how to the table. Data pipelines are designed with convenience in mind, tending to specific organizational needs. Stand-alone BI and analytics tools usually offer one-size-fits-all solutions that leave little room for personalization and optimization.
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