Big Data analytics is a business heavy-hitter that helps organizations run targeted campaigns to acquire more customers and retain them, create smart strategies for potential risk identification, and find more opportunities for product innovations. But to reap all those benefits, companies should be capable of dealing with concerns around big data first. Below, we have gathered the five main challenges of using big data for your business.
1. Big Data Privacy and Security Challenges
It is one of the most important issues of using Big Data, due to its legal significance and ethical concerns. Confidential information about their customers is collected and used to add value to the organization’s business. Personal information of an individual, when combined with large external data sets, leads to new facts about this person. And every company does not know if an individual wants to keep and edit his/her data secret or store them for marketing purposes. Moreover, a possible business information leakage is one of the large-scale issues of data security that affects not only the organization itself but unsuspected individuals too. Big data privacy used by law enforcement will increase the chances of people suffering from adverse effects without the ability to resist or even be aware of discrimination.
2. Data Access and Sharing Information
If the data is used to make accurate decisions in time, it becomes necessary to have the ones that are available in accurate, complete, and timely ways. This makes the information management and governance process a bit complex, adding the need to make it open and available to government agencies. This standardized manner requires APIs, metadata, and formats, leading to sharing information to improve decision-making, business intelligence, and production processes. This exchange of data between companies is inevitable due to the constant technological development which is increasing competition. Data sharing for customers and their activities threaten the culture of secrecy and competitiveness. And the question is what companies are going to do with their customer’s data when customers have expressed their abhorrence of storing problems. Are they going to keep their promise of staying GDPR compliant with their clients?
3. Storage and Processing Big Data Challenges
The available storage space is not enough to store the large volume of data generated by almost everything: Social media sites are the same large factor along with sensor devices. Due to the strict requirements of storing Big Data in storage media networks and servers, outsourcing to the cloud may seem a good selection. However, uploading this large amount of information to the cloud does not solve the problem. This is because Big Data information requires collecting and linking it to some medium to extract important information.
Terabytes of datasets require a large time to upload to the cloud. In addition, datasets change so fast. Uploading them in real-time can have many different hurdles. The transfer of it from the storage point to the processing big data point can be avoided in two ways. One is space-only processing storage in which the results can be transferred. The other is to transfer only the data that is important. But both of these methods require the preservation of the integrity and origin of the information.
Processing such a large volume of figures also takes a long time. To find suitable details the whole dataset must be scanned, something that is somewhat impossible. Thus, indexing is just the beginning while collecting and storing the datasets. It is a good practice that can significantly reduce processing big data times.
4. Challenges of analysis
Big Data brings with it some huge analytical challenges. The analysis that needs to be done on this huge amount of information is unstructured, semi-structured, or structured, requiring a large number of skills in advance. To a large extent, the type of analysis depends on the results to be obtained, i.e. the decision-making. This can be done either by incorporating huge volumes of data into the analysis or determining in advance what specifications are relevant.
5. Skills Challenges
Since Big Data is an emerging technology that is in its infancy phase, it should attract organizations and young people with different new skill sets. These skills should not be limited only to techniques, but also extended to research, analytical, interpretive, and creative contributions. These skills need to be developed individually and therefore require training programs by organizations. Universities must introduce a curriculum for Big Data to produce specialized employees in this technology.
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