5 Big Data Myths
How much do you really know about Big Data? If you subscribe to the following Big Data myths, your understanding of may not be as complete as it could be.
Those Big Data myths include…
Myth 1: Big Data Needs to Be Petabyte-Big
The term Big Data implies that you need a lot of data – petabytes’ worth – in order for it to qualify as Big Data. Otherwise, it’s just plain old data – or so you might think.
The fact is, however, that the amount of data you collect and analyze does not need to be gigantic to apply Big Data analytics and data quality techniques to it.
Whether you are working with a few gigabytes’ worth of information or petabytes, you can derive value from it by developing a systemic data management, quality, and analytics strategy for it, then implementing it on an ongoing basis.
See also: Just How Big is Big Data, Anyway?
We’re busting Big Data myths! Fact: Your data set doesn’t need to be gigantic to be Big Data.
Myth 2: You Need a Ph.D. to Work with Big Data
Sure, you can get degrees in data science and Big Data. If you do, you’ll certainly qualify as a Big Data expert.
But in today’s world, almost everyone plays a role in helping to collect, store, manage or analyze data. We’re all citizen data scientists to one extent or another.
Thanks to modern data integration and analytics tools, you don’t need years of experience to work effectively with Big Data.
Myth 3: All Big Data is Quality Data
In the rush to start embracing Big Data analytics, it can be easy for an organization to forget that just because you have Big Data doesn’t necessarily mean you have quality data.
On the contrary, the Big Data you collect more than likely has data quality errors. Fortunately, those can – and absolutely should – be fixed if you want to leverage insight from your data.
This is why it is important to avoid the false assumption that collecting and analyzing Big Data is enough. Data quality is another essential part of the equation. (See also: How Much Is Big Data Worth? A Lot, When It’s Quality Data)
Myth 4: Big Data is (Always) Machine Data
Machine data — which means data automatically generated by servers, network switches, IoT sensors and other devices — is one important source of Big Data. Machine data can help you to understand your IT infrastructure, monitor for performance and security problems and so on.
However, machine data is only one type of Big Data. Any other kind of data that can be analyzed to provide insight qualifies as Big Data (and, as noted above, the data does not have to be massive in size to count as Big Data).
Other forms of Big Data might include information as simple as customer addresses or emails.
Myth 5: Big Data is Expensive
Given all the hype about Big Data, and the fact that the companies making the most noise about it tend to be tech giants like Google and Netflix, it can be easy to assume that Big Data analytics and quality solutions are only for large organizations that have a lot of cash to invest in Big Data strategies.
You don’t need to have a huge amount of cash or be a hot tech company to develop an effective Big Data strategy. Modern data analytics tools can be used in any organization, at any scale, without a huge investment of money.
Indeed, what you can’t afford to do is not invest in Big Data, because doing so would leave you behind the competition. And fortunately, you don’t need to have millions of dollars on hand to start leveraging Big Data analytics, data quality, and data integration tools.
Learn about the new rules for your data landscape, that transform the relationship between business and IT, so you unleash the power of your Big Data.