What is it about big data that makes it so hard to understand?
Every day our data grows by more than 2.5 quintillion bytes (2,500,000,000,000,000,000) or just over two billion (2,328,306,436.5) gigabytes. so much that 90% of the data in the world today has been created in the last two years alone. Do you know how? There’s so much new data produced every day from social media sites, industrial sensors, satellites, cell phones, photographs, documents, and much more. There’s so much new data piling up that its storage, management, and analysis are overwhelming. This is why very few really understand big data.
Big data spans four dimensions: Volume, Velocity, Variety, and Veracity.
Volume: Enterprises are awash with ever-growing data of all types, easily amassing terabytes—even petabytes—of information.
- Turn 12 terabytes of Tweets created each day into improved product sentiment analysis
- Convert 350 billion annual meter readings to better predict power consumption
Velocity: Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value.
- Scrutinize 5 million trade events created each day to identify potential fraud
- Analyze 500 million daily call detail records in real-time to predict customer churn faster
Variety: Big data is any type of data – structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together.
- Monitor 100’s of live video feeds from surveillance cameras to target points of interest
- Exploit the 80% data growth in images, video and documents to improve customer satisfaction
Veracity: 1 in 3 business leaders don’t trust the information they use to make decisions. How can you act upon information if you don’t trust it? Establishing trust in big data presents a huge challenge as the variety and number of sources grows.
This huge amount of data is why you’re hearing so much about big data and why its understanding is difficult. As we all know, data has always been big relative to our capacity to store, retrieve, analyze, organize, archive, and purge but now the situation is almost out of our collective control. After all that data has to be stored somewhere—even temporarily—and sent through databases and applications for analysis.
We know how the data is generated. We know generally why we’re generating that data. We know what we’re supposed to do with that data but what we don’t know is how to handle that much data. In fact, we’re not even sure how to handle the metadata generated by big data.
Do you know what exactly is metadata? you might have heard a lot about metadata lately concerning the private information that the NSA has captured and analyzed. So what is so special about Metadata? In easy words, when you snap a digital picture, the metadata for that picture is the size, date, location, dimensions, pixels, and so on. All you have to do to check out metadata for a photo is to right click the photo file, select Properties, and then select the Details tab.
You can see that metadata also takes up space but is not the data itself. It is data about data. So we could discuss big metadata as well as big data. Now you probably have a better idea of why our data grows at such a high rate, when you understand that there’s more to data than just the data itself.
Big data is a lot of data. It’s more data than we’ve ever dealt with before and from more disparate sources. Plus the metadata. It’s a lot to think about. It’s a lot to store. It’s a lot to analyze. And those are the major issues of big data. You have to collect, store, analyze, organize, purge, and use the data. It’s that process from collection to use to purge that is the great unknown of big data. Big data is complex and difficult to manage.
The management part of big data is where the lack of understanding comes from. There are very few people who know how to manage that volume and complexity of data. Most companies have grown their own pieced together solutions. Each department usually tries to manage its own data in various forms. What happens is not only do these companies have huge amounts of disparate data, the data is stored in disparate locations, and in disparate data technologies. Big data. Big mess.
Now you should have a better understanding of what big data is, where it comes from, why it’s big, and what the problem is with big data. If you still don’t have a clue as to how to manage big data or what you’d do with it, join the club, you have a lot of company.
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