There are various Big Data Challenges. Big data projects have become a normal way of doing business; but, that doesn’t imply that big data is simple. Companies are coming across some significant troubles with regard to implementing their big data projects.
Before we dig into the big data challenges, we should initially characterize big data. There is no set number of gigabytes or terabytes or petabytes that differentiate big data from normal-sized data. Datastores are continually expanding; so what appears to be a great deal of data right now may appear to be normal data in a year or two. What’s more, every business is different, so the data that appears to be challenging for a little retail store may not be a great deal to a financial institution.
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Now, coming on to the most common challenges organizations face when dealing with Big Data. Some of those challenges are listed below:
1. Confusing Big Data Technologies
It is one of the most common Big Data challenges that companies face. It’s very easy to become mixed up in varieties of big data technologies now available. Do you need Spark or would the velocity of Hadoop MapReduce be sufficient? Finding answers can be difficult. Furthermore, it’s easy to make the wrong choice, in case you are exploring the expanse of technology without a clear view of what you need.
In the event that you are new to the universe of big data, professional assistance would be the right way to go. You could employ an expert or go to a merchant for big data counselling. In the two cases, with joint efforts, you’ll have the option to work out a system and, choose the right technology
2. Insufficient Understanding and Acknowledgment of Big Data
Often times, organizations neglect even the basics: what big data really is, what its advantages are, what framework is needed, and so forth. Without a clear understanding, a big data venture is destined to fail. Companies may burn through heaps of time and assets on things they don’t understand how to use.
Furthermore, if workers don’t see big data’s value or don’t prefer to change the current procedures for its adoption, they can oppose it and block the organization’s progress.
Big data, being a huge change for an organization, ought to be acknowledged by top management first. To overcome this data challenge and to guarantee big data understanding and acknowledgement at all levels, IT divisions need to sort out various training and workshops.
To see big data acknowledgement more, the execution and use of the new big data should be checked and controlled.
3. Paying a lot of Money
Big data projects involve heaps of costs. Many companies face this data challenge. If you pick an on-premises arrangement, you’ll have to bear the expenses of new equipment, fresh recruits (executives and engineers), power, etc. Also, despite the fact that the needed structures are open-source, you’ll need to pay for the setup, development, configuration, as well as upkeep of new software.
If you settle on a cloud-based big data arrangement, you’ll still need to employ staff and pay for cloud services, big data solution development, as well as the support of the required structure.
In addition, in both cases, you’ll have to take into account future expansions to avoid big data growth turning crazy and costing you a fortune.
The salvation of your company’s wallet will rely upon your technology needs and business objectives. For example, organizations that need adaptability profit by cloud; while organizations with incredibly high-security necessities go on-premises.
There are also hybrid arrangements available; when parts of data are put away and prepared in cloud and parts – on-premises, which can be financially effective.
All things considered, the best way to tackle this issue is to appropriately define your needs and choose an effective strategy.
4. Big Data Security Challenges
Frequently, big data projects put security off till later stages. Honestly, this isn’t a smart move. Big data technology continues to advance, but their security features are still being ignored; since it’s expected that security will be available on the application level. And, because of this multiple times, big data security just gets thrown away.
The safety measure against big data security challenges is putting security first. It is especially significant at the phase of planning your engineering.
5. Managing Data Quality
At some point, you’ll run into the issue of data integration, since the data you have to process originates from various sources in various formats. For example, E-Commerce business organizations need to examine data from site logs, call-centres, competitors’ site scans, and social media. Data formats will clearly differ, and coordinating them can be difficult.
Everyone is aware that big data isn’t 100% accurate. It doesn’t imply that you shouldn’t control how accurate your data is. It may contain wrong info, copy itself, or be inconsistent. The data of inferior quality cannot generate any useful insights or produce any quality opportunities for business
There are various techniques for cleansing data. First of all, your big data needs to have an appropriate model. After creating that, these other solutions can be applied:
1. Comparing data with a single purpose (for example, compare different addresses with their spellings in the postal service database).
2. Match records and merge them, in case they identify with the same entity.
In any case, keep in mind that big data is rarely 100% precise. You need to know it and manage it.
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