Three Big Data Terms No One’s Told You How to Use
Big data terms are entering the mainstream. It’s not unlikely to hear marketing teams bandy around terms like predictive analytics, real-world data sets, amplification, growth and so on. To keep your head from spinning and your feet grounded, read on. Here are the key concepts and terms that you need to know.
This is simple enough. You are probably already using the term and have a fair understanding of what it means. It means data and a lot of it. What you might not appreciate is Big Data’s magnitude. IBM has estimated that 2.5 quintillion bytes are generated each day. No entity is currently capable of using all of that information.
Sure, vast amounts of data are collected and stored in the cloud. And that massive amount of data is plugged into algorithms, processed and analyzed. The information garnered from analytics drives business decisions. And while this has been going on for the better half of a decade, Big Data itself has emerged in its own right as a computer/behavioral/social science. Data scientists analyze climate data, health data, meteorological data and local real estate sales data.
When using the term “Big Data,” be aware of it’s looming potential and its multitude of uses.
As mentioned earlier, Big Data has been evolving in the mainstream consciousness for the better half of a decade. The result is a lack of uniform data entry practices, programs or even digital records. Some industries are still in the painstaking stages of migrating legacy data into digital systems. Other industries are at the forefront of digital transformation. The new technologies and tools these organizations leverage may not have the expected longevity hoped for by their founders.
A proliferation of data streams have emerged as a result of rapid growth and multiple data uses. While this might seem like a problem, a multitude of data streams offers the opportunity to map out what is known and isolate the unknown. This leads to more robust data collection.
Prolific data streams are an obstacle when data scientists are incapable of transforming unstructured data, like emails or legacy data programs, into useful, structured formats. Extract, transform and load pipelines solve these problems. An ETL pipeline can transform unstructured data from multiple streams into structured data that capable of offering up helpful solutions to important problems. The pipeline effectively delivers data from one holding area to another.
When using the term “data streams” be aware that not all data streams are usable. If you are tasked with managing your organization’s data, then map out all data streams and assess their viability and usefulness.
Clean data sets can be used to predict certain behaviors and anticipate certain outcomes. When using predictive analytics be certain that you ask questions with an open mind. For instance, in a retail environment you might expect to see a greater amount of missing inventory during the holiday season than at other times during the year. If you believe theft is the reason behind missing inventory, then you might not notice other patterns.
Missing inventory can be caused by theft, missed shipments or a hectic pace that lends itself to employee error. Data scientists have to determine what variables to monitor in order to make correct predictions. You might find, for instance, that missing inventory spikes in the summer and winter, but for different reasons. As a result you can hone in on the causes and seek remediation in a more cost-effective manner. Rather than spending time and money on intense employee screenings, you might find that improving workflow and incorporating inventory protocols might be a better way to resolve the issue.
When using terms like Big Data, data streams and predictive analytics make certain that all stakeholders are on the same page. Baseline knowledge of Big Data terms goes a long way when implementing data-driven decisions.