Little wonder so many conspiracy theorists are having a field day. They now teach their disturbing versions to the curious public. First off, there is no link between this concept and world domination. You can rest easy now. So what does big data mean? It means a massive volume of data. But it doesn’t stop there. It also encompasses studying this enormous amount of data with the goal of discovering a pattern in it. It is a cost-effective and complicated way of processing information to find useful insights.
How Much Data Is Stored Today?
Today the estimated volume of data online is about 2.7 zettabytes. To put things in perspective, one Zettabyte is equal to one billion terabytes! The trend is not slowing down. Studies show that Facebook servers receive 500 terabytes daily. Also, we send about 290 billion emails every day. We expect that by 2020 we will produce 44 times more data than we did in 2009! The above stats are intriguing. The amount of data we produce in two days is equal to the amount we generated from the dawn of time until 2003. The volume of data we have today is a direct result of the invention of the computer and Internet. The information uploaded to social media platforms, forums, businesses, etc. are all part of this concept.
Characteristics of Big Data
Big data has five characteristics that describe its use and philosophy:
Studying Big Data
Analysing such large volume of data is very complicated. Every day, programmers write newer algorithms to process massive data sets. This level of complexity also means that a lot of complicated hardware has to take part in the process. But for simplicity sake, here’s a high-level rundown of the processes involved.
1. Capturing the Data
The first step is to capture the data. You can only grow your data library if you have a means to obtain data. Use a sophisticated algorithm to find the data needed to populate your data library.
2. Curation
The system curates the captured data and sorts them into smaller units. An algorithm is also responsible for this process. The reason for this sorting is to allow for simplification in the later stage of the process.
3. Indexing the Data – Making the Data Searchable
Due to the velocity of data flow, data scientists organize data sets into a searchable library. The system organizes and indexes everything. That way anyone can look through it and pull up information – in real time.
4. Storage
While all the above processes are going on, the system is simultaneously storing data. But because it is still raw and untouched, data is only temporarily stored. Indexing and storage happen concurrently. So at any moment, the algorithm in control knows where to find a data set.
5. Analysis of the Data
In this stage a lot of things are going on under the hood of the infrastructure. Plenty of algorithms are running, and computer processors are heating up. The system examines the stored data sets and analyzes the patterns.
6. Sharing and Transfer
Here, the system makes the analyzed dataset shareable and transferable. This new data generated is also still prepared to go through the entire process again.
7. Visualization
The patterns discovered in the analysis of the data create visual descriptions using an algorithm. These illustrations show the relationships between various data sets and data types. It also provides patterns and inferences.
8. Information Privacy
All the processes above are expensive. They are also confidential and should not leak out of the concerned company. Information privacy is the final process in this concept. Realize that while the system serializes the entire process, it all happens concurrently in real life. A lot of processors may be handling one set of operations while others cater to other sets.
Benefits of Big Data
A lot of corporations are investing big in this technology. For a good reason, too. The benefits of implementing this concept in business strategy justify the investment.
Common Pitfalls You Should Know
Yes, Big Data can help in making your work a breeze, more enjoyable, and profitable. But it’s not all roses without thorns. Users have encountered some of the pitfalls listed below:
This concept doesn’t lend itself to bespoke query solutions.Turning your collected data into useful insights can be onerous and complex.Data analysis can mislead you.Big data demands speed of data delivery to keep up with accurate updates. If your rate of real-time data delivery isn’t fast enough, your analysis will be false or inferior in quality. And sometimes, data isn’t available at all.High overhead expenses.
Wrapping Up
Big Data is a complex subject and will need intensive research and maybe some real-life practice to fully understand it. But with this article, you’re on the right path. The benefits are far-reaching, and the advancement is not slowing down soon. If you’re a business seeking innovative solutions, you’ll want to hop on this bandwagon NOW!
