Lot of content is thrown around Big Data, and how it could change the market landscape. It is running itâs hype cycle and many bloggers, experts, consultants, & professionals are lining themselves to align with big data. So, not everything that is known to this industry is accurate and some misconceptions that show up here and there have been discussed below.
- Big Data has the answer to everything: This has been floating as part of many coffee table conversations. Big data could help but itâs not a magic wand that will help you find answers to everything. Certainly Bigdata could potentially help you answer most cryptic questions but itâs not for everything. So, fine-tune the expectation of what to get out from BigData strategy.
- Data Scientist drives BigData: Almost every now and then, we stumble upon someone who claims to be a data scientist and boast about how they are driving BigData in their company. Surely, they all are doing important work of helping find insights from data but BigData is already happening whether Data Scientists drive it or not. BigData journey begins with capturing as much data as possible. Data scientists just help steer the insights from data. So, donât wait on data scientist before you start prepping for BigData.
- Big Data is complicated: With escalating pay cheque of Data Scientists, it is not difficult to understand that BigData is perceived as rocket science that only few could tame and understand. This is a pure evil that most of businesses has to deal with. BigData is just more volume, velocity and variety of contained data. It need not be complicated. In fact, a well-designed big-data system is often scalable, simple and fast. So, breath easy if you find your big data nicely laid out and easy to understand.
- The More Data the better: There is a debate on how much data is effective and whether more data is better. There are certainly two schools of thoughts. One suggesting that more data you have better you could learn from it. But I believe data and its effectiveness goes more around quality aspect of data and not always quantity. So, based on the circumstances, quality and in some case quantity could signify better impact from data.
- Big data is just hype: Surely, you must find yourself either for or against this statement, but that is because it is what it is. Bigdata is getting a lot of press hours and PR time. It is partly because there is hype, but partly because tools to deal with big data has unveiled the capability to address unmanageable blob of data and parse to insights using commodity hardware. So, hype is evident but there is whole capability shift that is fueling this hype like demand to handle more data and get to better insights within data. So, big data is not just hype but a real shift of capabilities on how businesses start to look at their data.
- Big data is unstructured: Yay, I am certain if you are into bigdata domain for more than 1day, you must have heard rants around bigdata being unstructured data. It is not true. As stated earlier, big data is just data that is beyond your expectations around 3 vectors: Volume, Velocity and Variety. So, data could be structured or unstructured, its 3 Vs that defines its BigData status and not the structure of data.
- Data eliminates uncertainty: Data surely helps convey more information around a particular use case but certainly it is not an indicator to predict certainty. Future data is as uncertain as the market condition. Uncertainty comes to business layers through various areas, competitive landscape, customer experience, market conditions, and other business dependent conditions. So, data is certainly not a good indicator for eliminating uncertainty.
- We must capture everything in order to analyze our Big Data: Sure, it sounds awesome to capture everything to learn everything, but itâs delusional. Everything is a very circumstantial thing. Business every time shifts its dependence from few sets of KPIs to others. So, there could never be an exhaustive list to capture. It will keep on changing with market. Another key area to understand is that few data sets have limited to no impact on business, so data should be picked according to itâs impact on the business. And these KPIs must be evaluated every now and then to measure changing market shift.
- Big Data systems are expensive to implement and maintain: Yes, this still exists as a misconception with many businesses. But businesses should try to understand that the very fact Bigdata is sitting in hot seat is because commodity hardware could now be used to tackle bigdata. So, bigdata systems are not expansive anymore. They have been low and getting lower on their cost. So, cost should never be a deterrent for indulging in bigdata project.
- Big Data is for Big Companies Only: Like the point previously quoted, big data tools are cheap and they are run on cheap commodity hardware. So, they are accessible and no more the dream/passion of big corporation only. Small/Mid size companies have almost similar leverage when it comes to thinking like big corporations. So, bigdata capabilities are for the strong hearts and not the rich pockets.
So, BigData landscape is filled with Truth and Myth, so make sure to check which side your hurdle lies before calling in quits and throwing in the towel.