Before the development of Big Data technologies, the data was managed by conventional programming languages and simple structured query languages. However, these languages were unable to handle data due to the constant expansion of organization’s information and data. That is why it has become crucial to manage such huge data and implement a reliable technology that meets all the requirements and needs of clients and large businesses and is responsible for data production and control. Big Data technologies have become a buzzword for these requirements in recent years.
Businesses would benefit from the increase in data but we must be cautious and aware of the issues associated with Big Data storage. Thankfully, there are workable solutions that businesses can put into place to solve their data issues and thrive in the data-driven economy.
Before examining the most common Big Data problems, let’s first define the term “Big Data”.
Table of Contents
Table of Contents
What Is Big Data?
Big Data is the term used to define king-sized information that may be processed to divulge trends, patterns, and associations mainly related to human behavior and interactions. It describes data sets that are too big or complicated to be handled by conventional data-processing application software. Big Data is classified into three types: Structured. Unstructured and Semi-based.
Characteristics of Big Data
To categorize any data as Big Data we use these 5 V’s:
Big Data Software
Some of the Big Data Software are mentioned below.
What are the Big Data Challenges?
Here are some Big Data challenges that the company must be prepared for:
Insufficient understanding and acceptance of Big Data
Companies frequently lack even the most fundamental knowledge, such as what Big Data is and how it can be used, as well as its advantages and the necessary infrastructure. Without a clear understanding, a Big Data project runs the risk of failing. Businesses could waste a significant amount of time and money on products they don’t even understand how to use. And, If employees don’t understand Big Data’s value they might delay the company’s success.
Since Big Data represents a significant shift for a business, top management should be the first to accept it. To ensure Big Data awareness and acceptability at all levels IT departments must plan multiple pieces of training and workshops.
To increase Big Data acceptability the use of the new Big Data solution must be monitored and supervised. Top management should exercise some control, but not too much, as this could have unfavorable consequences.
Confusing a variety of Big Data technologies
It can be easy to get confused with so many different Big Data technologies now in the market. Do you require Spark, or will Hadoop MapReduce’s speeds suffice? Which database is preferable, Cassandra or HBase? Finding the solutions can be challenging. And it’s more simple to make a bad decision if you’re sifting through the ocean of technical possibilities without a clear understanding of what you need.
If you are new to the Big Data industry the best course of action would be to try to find professional assistance. For Big Data consulting, PeoplActive can help you. Together, you can come up with a plan for each situation, from which you can select the appropriate technological stack.
Paying loads of Money
Big Data projects are very expensive. If you choose an on-premise solution, you must consider the price of new hardware, new staff (administrators and developers), electricity, etc. Additionally, even though the required frameworks are open-source, the creation, installation, configuration, and upkeep of new software still require payment.
If you choose a cloud-based Big Data solution, you’ll still need to engage staff (as mentioned above), pay for cloud services, develop the Big Data solution, and set up and maintain the necessary frameworks.
Moreover, in both cases, you’ll need to make room for future expansions to prevent massive data growth from spiraling out of control and costing you a fortune.
The preservation of your business’s money will depend on its particular technology requirements, operational strategy, and corporate objectives. For instance, some businesses employ the cloud for its flexibility advantages. Other businesses might choose to be on-premise due to their highly stringent security requirements.
Additionally, hybrid solutions are available in which some data is processed and kept on-premises and on the cloud. And in particular, this tactic may be highly economical. Additionally, employing data lakes or algorithm optimizations (but only if done correctly) can help reduce costs:
Data Lakes can help you save money by storing data that is not currently required for analysis.
Optimized algorithms can reduce computing power consumption by 5 to 100 times.
Google Cloud vs Microsoft Cloud
The complexity of managing data quality
Data from different sources
Data integration is a significant issue that businesses must deal with eventually. This is mostly because the data utilized for analysis comes from multiple sources. And there are various formats in which this data might be presented.
Like any other technology, even Big Data is not always accurate.
There are many methods available on the market that are used only for data cleansing. But first, you need to have a good model and the right approach for your large data. Only after that can you move on to further tasks, such as
- Connect data to a single source of truth.
- Simply match and include data that relates to a specific entity.
Dangerous Big Data security holes
The most naive move that Big Data adoption projects make is delaying security till later stages. Big Data security is frequently neglected. Although technology advances, security is not a factor that is taken into account until the application level.
As they say, prevention is better than cure. Prioritizing security is crucial. And this is a precaution against any potential problems with large data security. It is especially crucial while designing your solution’s architecture.
The tricky process of converting Big Data into valuable insights
Here is an illustration: Your super-cool Big Data analytics examines what product pairs individuals buy using only historical data about customer behavior. Meanwhile, a soccer player is also seen on Instagram sporting his most recent outfit, which includes the beige cap and white Nike sneakers that are unique to him. He looks terrific in them, and the individuals who witness him frequently dress similarly. You then visit the store, but nothing is there. Now that you’ve been let down, you decide you’ll never again purchase anything from this store. And as a result of your dissatisfaction, the business lost money and a loyal customer.
You might be asking at this point where the issue is. The research carried out by a company’s Big Data tool does not take into account data from social media platforms or the websites of competitors.
To address this issue, the company requires an ideal solution, one that, upon analysis, yields helpful insights and ensures that no important information or data is lost.
Trouble of Upscaling
One of the most challenging aspects of Big Data is its immense potential to grow.
The major issue with upscaling is not the process. Even though your design might be modified in a way that doesn’t need any additional work, it won’t necessarily result in the same or better performance.
The most crucial consideration you should make while creating your Big Data algorithms is Future Upscaling. But aside from that, it is imperative to plan for system maintenance and support so that any changes may be handled promptly. Additionally, conducting regular performance audits can assist you in locating weak points and promptly addressing them.
Is very obvious that most of the described difficulties may be anticipated and handled if your Big Data solution has a competent, well-organized, and carefully thought-out architecture. And this requires companies to start approaching it methodically.
Additionally, companies ought to:
- Hold workshops for staff to ensure the use of Big Data.
- Carefully choose your technological stack.
- Keep costs in mind and prepare for future upscaling.
- Keep in mind that data isn’t entirely correct but manages its quality.
- Dig deep to find useful insights.
- Never ignore the security of Big Data.
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