Big data, a buzz-phrase that has been around for quite some time now, is well understood by a select few. Here, we bust some common myths linked to this particular model of analyzing and collecting data.
With fast data and big data analytics
dominating the scene like never before, it is time to take a closer look at what they are all about and understand the realities that guide their processes.
Myth #! Big Data is all about Storage of Terabytes or Petabytes of Data
Contrary to what most people think, big data is not only about storing large volumes of data; rather, it is skewed towards gaining useful information from the same. For instance, a national address database, which will obviously be huge in size, is worthless unless used for reaching out to enlisted people.
In this case, big data behaves in another way. The tools used by big data
understand the requirements and buying patterns of people residing at the addresses in the said database and then use the same data for creating profitable and more robust systems. To cite an example, the ordering patterns of those enlisted in the national database can come in handy for optimizing the ways in which a company would ship its packages, predict trends
, or develop further.
Myth# 2 It’s Impossible to Deal with Big Data
Well, the big data required by you may not be "bigger" than Facebook. This social media giant handles over 1.32bn users, out which 829m log in each day and generate over 4.5bn likes on a daily basis (also consider checking out this perfect parcel of information for a data science degree
). Though Facebook spends a lot of money on upgrading and maintaining the system, it also earns large profits via the information processed about users.
Just like Facebook, you can get reliable access to tools and methods that are specifically designed for big data. While some of them would be appropriate for the type of data you intend working with, other will not be applicable to your organization. Here it deserves mention that while the tools existing in your data environment may be capable of storing normal information, they may not be adequate for your big data needs. There are plenty of other specialized tools like NoSQL, NewSQL, RDBMS, HDFS, or even flat files that will help your cause at this stage.
Myth#3 Big Data is just not Worth the Hassle
As per the results of a study by the Gartner Group in 2012, companies handling big data
and making good use of the same are bound to outperform others by over 20 per cent, in the next two to five years. Of course, in accordance to your initial setup costs, the system may not prove to be worthwhile in the first few years, but with the passage of time ( and increasing operations), the head start attained by you will help in paying off your investments.
For instance, Facebook may be spending $1 (63p) annually on a single user for storing and processing their data, but then, each member generates over $4 (£2.50) of yearly annual revenue for the company (also consider checking out this career guide for data science jobs
). So, even if you are finding big data difficult to handle and not using it for your present operations, a good strategy related to saving the same for future will surely pay rich dividends, especially when you are in a position to make further investments.
Myth # 4 Our Company is not Equipped to Catch up with Big Data
Apparently not so; even though big data has been a huge talking point, many companies refrain from adopting the methodologies/ tools
that make it very distinct from other normal data management
. If the same is true for your company, then do understand that you are not lingering behind with regard to this technology change; you just have to start collecting and using relevant data to give yourself the much-needed competitive advantage
Myth # 5 Big Data is Always Better Data
Is big data better?
It all depends on how you process the data and use it to generate better profitability figures. So, if you are trying to generate more useful information to make your services/sales turnover figures get an edge over rival companies, or bring about improvements in your business with available data, then you are obviously on the right track. Having a sensible and well-defined data usage strategy helps in getting a lot more out of big data, even if it is developed with an extended timeline in view.