How To Build A Data Pipeline
Big Data is math, not magic. It is science, not sorcery. And, because Big Data is about quantifying, you need to have a protocol in place: ways to measure your results, to catalogue and store your data, and ways to move that data from raw information to actionable intel. We all know what a sales pipeline looks like, but what should your Big Data pipeline look like, and how should it operate?
Four Steps Courtesy of David Steinberg of Zeta Global:
1 – Eliminate some steps: Right out of the gate you need to understand that Big Data is not about giving you or your tech team more work to do. It’s about results with greater efficiency. So, your data management architecture must lend itself to collaboration, and here’s the big one, eliminate any unnecessary manual steps in the process. Your protocol needs to include only what can help you gather, refine, explore, and distribute data. As bare bones and efficient as possible.
2 – Acquisition and storage: Do not filter your data first. That’s one of the big mistakes of Big Data rookies. Trying to determine which data will help you, that is. One of the secrets to the success of Big Data is realizing you don’t know what you don’t know. So, you need to get all types of data from any possible platform, whether that be legacy systems, social media, machine data, or any potential source. And, you want that data as current and “real time” as possible.
3 – Refining your collection: You will need your marketing and tech teams on the same page for this one. Before you try to analyze your data, it needs to be cleansed and prepared. Data should be catalogued and grouped for both immediate and potential later use.
4 – Pattern recognition: The real “magic” of Big Data is in discovering things you did not know, and finding answers to questions you did not know to ask. This happens in the pattern recognition step. Look for trends, insights, and applications, but be open. Remember, this is about learning what you don’t know you don’t know.
Once you have all your data collected, reviewed, and catalogued, you are ready to put it to work. Following these four protocols will get you closer to moving your Big Data plans from concept to reality.