Everybody is talking about big data these days. With its global market expected to reach an astounding $103 billion by 2027, it’s easy to see why: everyone wants a piece of the pie! Yet, as it happens with all emerging technologies, there’s a lot of information going around that seems contradictory or is just plain false.
Unfortunately, many business owners, so-called tech experts, and even Python developers are responsible for that misinformation. With such a noisy context, it’s only natural for people to get confused about what big data actually entails. That’s why is very important to clarify some things about big data and dispel some of the most common myths, like the 5 listed below.
1 – You need a lot of money for big data
This myth used to be true some years ago. Back then, Python development services developed systems that required a lot of human intervention. From collecting data and running the report to analyze it all in the end, the process was lengthy and expensive, so it was only affordable by big companies. That isn’t the case anymore.
Today, the possibility to leverage cloud-based systems available in the market makes it easy for companies of all sizes and budgets to get on the big data trend. It’s true that you have to make an initial investment to get things going, like setting up systems and training people to work with data. But after that early stage has passed, you’ll see that the investment is repaid in no time through the insights you can get out of the data.
There’s a small caveat to all this, though. You won’t need that much money to start running your big data systems as long as you take the time to foster a data-driven culture within your company. You can have the best software to work with big data, but if you don’t have the proper people to do so, all your efforts will be futile.
2 – You need to invest in big data right now
If you’ve read some articles about this topic, you’ve probably seen that a lot of authors, experts, businesses, and Python development teams saying that if you should definitely get on the trend now or risk your business future. However, it’s important for you to know that big data isn’t necessarily for everyone.
It’s true that data can level up almost any business in almost any industry, but that only happens as long as the company using big data is prepared to do so. The idea of adopting a data-driven model for your business has to come from an informed decision, not an impulse. Introducing big-data-based procedures into your workflow without knowing what you’re getting into could disrupt your business.
So, before jumping into the trend, you have to ask yourself why you are doing so. Do you know what are the benefits of big data? How are you going to leverage them in your specific case? Do you have a data culture in your company? Do you understand the potential impact on your workflow? Have you devised an implementation plan? These and many other questions need answers before you make a commitment to big data systems.
3 – The IT guys are the only ones that should deal with it
From predictive analytics to data insights, from system implementation to Python development outsourcing, big data can imply a lot of things that sound technical. That’s why you (and some of your employees) might feel tempted to think that, if you are to make the jump, big data will be something that IT will have to work on almost exclusively. You can’t begin to understand how wrong that is.
If you confine the data and all its processes to your IT team, you won’t see all the benefits it can bring to the table. Think about it. You can get your hands on a lot of different data today, given the numerous sources available: social media, website analytics, customer service, content marketing, sales reports, etc. Every bit of information coming from all of these channels can serve different purposes and different areas.
Naturally, the IT department will lead the implementation of the big data solutions—but the potential results should be accessible to everyone. That’s because this information can lead to everything from new sales pitches and different audience segmentation to an entirely different content strategy.
4 – You need the most data you can get to succeed
It’s right there in its name, isn’t it? “Big data” gives a lot of people the impression that they have to work with huge datasets to get results. And while the assumption that this technology will have you working with a lot of data is correct, that doesn’t mean that you need a colossal amount to get something of value out of it.
What we’re saying here is easy, to sum up—it’s better to have good data management with less data than being overwhelmed by a lot of it. It’s highly likely that, whenever you face your first data sets, you’ll want to use it all for your analysis. After all, having more details can lead to deeper insights, right? Well, not precisely. A lot of data can make it harder for you to identify the patterns and to sort out the things that are valuable from the ones that aren’t.
Rather than piling up more data in your systems is refining the inherent processes in those systems to get better analysis and results. That’s especially true if you’re constantly reviewing your methods and your data is ever-changing by nature. This doesn’t mean you can’t ever expand the data you’re looking into, though. Once you get the core processes defined and adjusted, you can add more data to improve your results.
5 – You can get certain predictions from the data
Finally, there’s this myth that has people believing that big data science can act as some sort of digital Nostradamus, anticipating the future and making precise predictions. That’s not true! Even with the most complex predictive analysis of some of the largest datasets available, the best you can hope for is for probable outcomes, not future certainties.
Big data science extrapolates trends and possibilities from the information it studies. In that way, big data solutions offer possible future scenarios based on what’s happened before (and what’s happening now, if you use real-time data). This means that even the most probable of the projections has an error margin because reality can add variables that the big data model might have not contemplated at all.
Is there any way to get more certainty? Of course. By adjusting the way you analyze data and by feeding your model with more relevant information, you can get more precise forecasts. However, don’t make the mistake of believing that your predictions will be 100% accurate because that could never happen in real-life scenarios.
Conclusions
Big data is one of the emerging technologies that are called to be major players in reshaping the way companies conduct their daily businesses. That’s because it holds a huge potential in its hands— namely, the ability to analyze a lot of information to provide deep insights that can lead to more sophisticated strategies and decisions.
That’s the main reason why they are so many people trying to capitalize on this trend. But that’s also the reason why there is so much misinformation doing the rounds. The final advice you should get is this: educate yourself about big data before making any decision. Your company’s future depends on it.
