In the digital world; produces huge amounts of data, through each search being on Google Drive for each minute users spend on social media platforms, and other digital activities of the other, and now with the proliferation of smart watches, and wearable devices, will become the world mechanism exists to collect the data.
The term big data to massive amounts of different information, which are difficult to collect and evaluate through conventional techniques, in addition, they are characterized by the need for fast processing, so that it can be presented and the common points, attitudes, and patterns in the behavior of the target group.
Monitoring data is a difficult process because it includes several important elements, starting from the security, privacy, and even meet the standards of compliance and ethical use of data, and when it comes with big data problems and challenges are increasing in size; because the data is unstructured, and unpredictable, as indicate the statistics indicate that by 2020 there will be around 44 trillion GB of data.
Struggling companies large business to discover ways to store data and management, and use, and analysis; to make the most of them, he has explained the opinion poll carried out by the foundation NewVantage Partners that about 37.1% of the companies think they are successful in trying to use big data, while 71.7% of the companies that they have not yet formulated a data-driven culture, and 53.1% of the companies stated that it did not deal yet with data as an asset business.
So from now onwards; companies must understand the challenges of dealing with big data and exploitation solutions that must be adopted to overcome these challenges.
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The following are the top 5 challenges companies face when using big data, and ways to solve it:
1 – lack of understanding of the importance of big data:
Frequently; fail to know the basics about: what is big data, its benefits, and infrastructure necessary for their adoption, etc. Without a clear understanding of each of these basics will draft adoption of big data, the companies add a lot of time, and resources on things you don’t know how to use it.
As to the bigger challenge is the staff, train them, if employees don’t realize the benefits of big data, or do not want to change methodology to the current processes for adoption, then they will resist them, and then to impede the progress of the company.
Solution: ColeN big data represents a big change for the company, you must accept it, and by department first, and then explain their relevance to the employees each according to the position of his job, to ensure understanding of the importance of it and acceptance from all levels, so you need departments to organize several training courses, workshops, etc.
To learn more about the acceptance of new strategies for the data, you must control the way the application of staff no, but we must not Exchange Management in the car, because that may have a negative effect.
2 – data quality:
Companies face the problem of data integration, since data that need to be analyzed come from a variety of sources, in a variety of different formats, for example: you need e-commerce companies to analyze data from logs, web sites, call centers, and the locations of competitors, it is clear that the data formats will be different, and match them.
There is also a bigger challenge is the data not reliable, as the huge data is not 100% accurate, not only because it can contain false information, but because they can be frequent, as well as may contain contradictions. It is not likely to provide quality data flowing any useful information, or important opportunities, but may lead to inaccurate information to increase the risk of making business decisions is wrong to harm the company.
Solution: there is a whole range of techniques devoted to examine and prepare the data, take necessary actions to ensure maintaining the quality of data on the long-term.
Businesses can also search for automation tools that can perform the functions of preparing the data, and can determine the data that you don’t need at all, through the creation of automated processes for checking data harmful at the beginning of the operations, all; to verify this data before they reach the network.
3 – spending a lot of money:
Projects the adoption of big data used a lot of expenses, so you must consider the costs of new devices, and personnel such as: managers of systems, developers, etc. Although the regulations for the open source, but you still need to pay the costs of developing new software, preparation and maintenance.
And if you decide to rely on one of the Big Data Solutions based on cloud services, you will need to recruit staff, pay the costs of cloud services, the development of Big Data Solutions, in addition to the preparation and maintenance of frameworks for action.
Solution: will determine the optimal solution to the technology needs of the specific goals of your company, for example: companies want flexibility can benefit from cloud solutions, while companies with stringent security requirements can rely on the software solutions Internal On-premises software.
There are also mixed solutions, so that the stored parts of the data, and addresses depending on the cloud solutions, as well as depends on the software procedure, which can be also cost-effective. Or you can resort to strategies of data lakes Data lakes, or the algorithms improved, and if it is done correctly can save also a lot of money.
- Can provide Lake data the chances of cheap storage of data, which do not need to analysis at the current time.
- The algorithm was improved and can reduce the power consumption of computer by 5 to 100 times, or more.
4 – problems of translation, and integration:
The most typical feature of big data is its large capacity for growth, and the need to integrate data from sections of different work, so you may prefer to design special Solutions, adjusted to, without additional efforts. But the real problem is not the introduction of processing capabilities, storage of new, but the complexity lies in the ability to expand, while maintaining the level of performance of your system, and keep it within budget.
Solution: needs the first and most important challenges such as these: is the structure decent to solve big data, and something else very important is the design of algorithms to massive data of your own, taking into account the raised level in the future.
You will also need to plan for the maintenance and support of the system, so that they can follow any changes related to the growth of data correctly, moreover, the audits regular performance can help identify weaknesses, and address them in a timely manner.
5 – security problems:
A lot of times; and defer projects that embrace the big data the topic of cyber security into the later stages, there is no doubt that this is a big risk, where technologies require big data, but its own safety feature is still neglected.
Solution: you must give the necessary measures to address the challenges of data security a high priority, where the data security is very important especially in the design stage of the structure solution your company, in order to avoid the occurrence of anything unexpected that may lead to failure of the entire project, and threaten the security of the company itself.