Demystified: 5 Myths of Big Data
- AI Big Data Demystified ML Myths News
As any new technology, Big Data gains a lot of attention and gathers around itself quite a ton of myths. We will look through 5 most popular myths of Big Data today — and demystify them!
When something new appears and the ways of its application are not clear enough yet, there always is a mix of hype, exaggerations and wild expectations not backed up by solid proofs. For the matter of Big Data this includes the statements like:
- Everyone works and succeeds with Big Data nowadays;
- Big Data should be really big;
- Big Data is a crystal globe of a soothsayer;
- Big Data costs a fortune;
- Big Data should be of concern of IT-related businesses only.
Below we will explain what each of these myths is based upon, what are the main reasons for such misbeliefs and what should be actually done to make Big Data work for your business.
Everyone works and succeeds with Big Data nowadays
While this definitely should be done this way in a perfect world of ponies and rainbows, in the real world the situation is quite different. On one hand, multiple surveys and researches from trustworthy sources like New Vantage Partners depict up to 75% of the respondents already having some kind of Big Data and/or Machine Learning (ML) solution implemented to a certain degree and receiving tangible results of the project.
Another research from Narrative Science shows 62% of all enterprises plan to have some Big Data solution in place before the end of 2018. On the other hand, there are multiple challenges in Big Data adoption, barring many companies from deploying their Big Data initiatives efficiently.
However, the companies that DO deploy their Big Data initiatives according to a thought-out long-term strategy get tangible successes as a result. We have recently published a 2-part article on such Big Data-based business success stories, feel free to check part 1 here and part 2 here.
Big Data should be really big
Big Data is defined by three V’s: volume, variety and velocity. Having your Big Data solution simply hoard a large volume of documents is clearly not enough. The system should be able to both processes various types of incoming data and do it quickly enough, so the Big Data stores begin delivering Big Answers for your business.
In order to do this, applying various Machine Learning algorithms is needed (this is especially true as around 75% of the respondents to the aforementioned New Vantage Partners survey are C-suite executives from the financial industry). To accomplish the task of processing the Big Data of various types from disparate sources and doing it quickly enough, the businesses should opt for the right set of Big Data tools and delegate the task to a skilled in-house team or contractor, of course.
Big Data is a crystal globe of a soothsayer
While predictive and prescriptive Big Data analytics are definitely amongst the 10 hot trends of Big Data analytics nowadays, they are not a panacea of sorts. This is simply a technology using the data stores at hand to try and discover patterns in the ongoing events. This helps build somewhat trustworthy models of the future turns of events.
However, as with any modeling, there is some room for error. With Big Data though, the percentage of correct guesses will be high and will grow over time, as the ML algorithms deployed will learn and improve. Thus said, sometimes the events take quite an unexpected turn, like the results of the Brexit vote or the latest presidential elections in the US, so no ML model based on any Big Data store will be able to predict such an outcome.
Big Data costs a fortune
This misbelief is largely fueled by the misconception the business needs to build a data center to deploy the Big Data infrastructure and hire a ton of staff to maintain it. Add a team of expensive Big Data specialists to actually work with the data itself — and you will see why exorbitant expenses are expected while implementing Big Data projects.
This cannot be farther from the truth, actually. As AI literally becomes the synonym for cloud computing and the multi-cloud strategy allows optimizing the allocation of resources to build scalable, manageable and affordable cloud infrastructures, deploying the Big Data solution is really the next logical step for any forward-thinking business. Adopting the DevOps culture can be a huge boost for this, as it helps your company become more versatile and competitive.
This obviously does not mean you are already lagging behind if such a project is not yet underway in your company or organization. However, engaging in such an endeavor should be an essential part of your short-to-mid term strategy. Keep in mind your competitors already are or soon will be using their Big Data to the max — and so should you.
Big Data should be of concern of IT-related businesses only
Computers are really ubiquitous nowadays and are a major part of any business’s operations, from HR and finance department to customer support service, logistics or even Industry 4.0 automated factories. As we told in one of our recent articles, even the businesses that are quite far from the IT (like propulsion systems manufacturers, cruising operators and restaurant chains) are successfully implementing Big Data solutions into their operations.
All in all, Big Data is not only about storing vast volumes of various information. It is about making the right decisions based on this information. And making the right decisions is hugely beneficial for any kind of business, don’t you think?
We hope you liked the article and you now have these 5 myths of Big Data demystified. Please share and subscribe to receive the latest hints, insights and updates on the IT industry!
Feel free to browse through the latest insights and hints on the DevOps, Big Data, Machine Learning and Blockchain from IT Svit!