Big Data is everywhere today. COmpanie sneed to find the right ways to relate with and handle Big Data if they really want to optimize their performances.
However, doing so will require you to understand certain considerations. below, we'll consider them in detail, so you know how best to go about Big Data management.
The Volume of Data Being Processed
Perhaps the most important consideration for you will be the volume of data you’re actually processing. Over the past few years, the sheer volume of data being processed has exploded. The number of data produced was expected to reach 35 zettabytes by 2020, and it will only continue to grow over time.
Twitter alone generates over 7 terabytes of data daily - that number is about 1o terabytes for Facebook. Some companies even generate terabytes of data every hour of every day. This is why they have clusters of data just stored by the terabyte somewhere.
Today, we store everything. We’re all drowning in data - financial data, environmental data, behavioral data, etc. As long as you can think about it, there’s probably some data that relates o it somewhere. Everything you do can be logged as an event, especially if that action is taken online. Every action generates data, and it can be stored.
Even when you consider your devices, you’d see that data explosion will continue. Years back, phones with 32 GB of storage were sufficient to handle processes for years without stress. Now, manufacturers have all but dumped the 32 GB standard and are now pushing for higher base storage capacities.
As implied by “Big Data,” companies are now facing massive data volumes. If your organization keeps generating data but can’t manage it effectively, you will eventually be overwhelmed. Of course, it is worth noting that the opportunity exists to analyze most of the data with the right technology - or, at the vest least, some of the data that is useful to you.
Thanks to technological advancements, it is now possible to use data to gain a better understandsing of your business, customers, and the market.
Another issue that is facing many companies with Big Data is the blind zone. Essentially, this is the conundrum where companies are dealing with large volumes of data - but a consistently reducing volume that can actually be processed and managed.
The Variety of Data
With the volume of Big Data comes another problem - even for the best of data management services. - the variety of data itself.
Today’s technological world is populated by smart devices and social collaboration. This means that processing enterprise data is even more complicated as it includes raw, semi-structured, and unstructured data. These are the ones gotten from web log files, web pages, emails, social media forums, and other online platforms.
A traditional data management system will most likely have a hard time performing the analytics needed to easily understand these data. This is because much of the data gotten isn’t compatible with traditional database technology. Even to date, most companies are just starting to understand the opportunities that abound with Big Data.
Essentially, variety deals with all types of data. There is a paradigm shift that is going on today, and it involves moving from traditional, structured data to raw, unstructured data as part of the decision-making process of companies.
A company’s success will rely on its ability to gain insights from the different types of data available to it. But, traditional data management sources won’t be able to handle raw and unstructured data.
Interestingly, a lot of data analysts still remain focused o just 20 percent of the data - the part that is relational and properly formatted, and which will be compatible with their schemas. But, the truth is that most of the world’s data is unstructured - or, at the very least, semi-structured.
It is worth noting that some data management services and systems are getting better at handling even unstructured data. They help companies to capitalize on Big Data opportunities, improving their ability to analyze all forms of data.
The Velocity of Data
In the same way that the volume and variety of Bid Data have changed, you also need to deal with the increasing velocity of data itself.
Essentially, velocity considers how quickly data is arriving and being stored - as well as the rate of retrieval for the data. Interestingly, the volume of data can sometimes be a consequence of the velocity at which it arrives.
To accommodate data velocity, you need to have a new way of thinking about the problem. Instead of condoning the idea of velocity to simply the growth of data repositories, you can think of it as data in motion - essentially, the speed of data mvoement.
So, we’ve established that enterprises today are dealing with and producing petabytes of data. With the increase in the number of information streams, we’ve now seen that there is a constant flow of data at levels that have made it impossible for traditional systems to keep up.
Interestingly, more of the data being produced is found to be temporary. With a short shelf life, organizations need to be able to analyze the data they have almost in real-time if they hope to get any insights from it.
With the traditional data processing systems, companies can simply run queries against specific static data to find what they need. With streams computing, you could even execute a process that’s akin to a continuous query.
When it comes to big data, you will need to perform analytics against the variety and volume of data even while it is still in motion. You need to be able to handle data while it changes or switches, so you can really harness its power.