Article

Edward Huskin
Edward Huskin 23 December 2019

5 Challenges of Big Data Integration

Before undertaking a big data integration project, certain challenges need to be taken into consideration and this article takes a look at some of them and how to deal with them.

Big data integration refers to large, complex datasets where traditional applications for data processes are not sufficient. The integration of big data sets is quite complex and requires a set of expertise.

The specialised management of data is quite necessary for big data integration and ensure good decision making and the ability to encounter various challenges. Before undertaking an integration project of this nature, certain challenges need to be taken into consideration and this article takes a look at some of them and how to deal with them.

Several core challenges affect big data integration and these can include data analysis, curation, capturing and sharing. Despite these challenges, an informed decision allows for a smoother transition and/or integration.

1. Lack of comprehension

Big data integration requires a team of experts behind it or consultation with an expert in the field. At times, companies overlook the magnitude of what they are doing and waste valuable resources.

Without understanding this process clearly, it is more likely that failure will occur. With experts at the helm of integration, it becomes easier to create a failproof strategy to implement big data integration. Naturally, this is a drastic transition, so the employees should be properly inducted and made to understand new processes associated with the integration. In particular, the IT department of the company should organise workshops and training for the rest of the employees to understand and accept the process.

2. Lack of certainty

There is a wide range of tools to manage big data integration, and this adds to the fact that there is no set model for data integration. Each data management system has its own way of working which might not a good fit for an enterprise.

In other words, big data integration is a risk that entails choosing between platforms such as JSON, XML, and BSON. The market offers varied ways of streamlining the process and innovation and disruption in the industry create a highly competitive industry with various options to choose from. The wide range of SQL developers and tools for in-memory compute and other tasks along with the unpredictable market, have created uncertainty in terms of data management.

3. Difficulties with scalability

The increase of storage capacity and the need thereof largely depend on future projects and possibilities. This is hard to measure realistically and might result in under or over calculating for a need. Big data integration projects expand quickly because of large data from different sources into a single platform or system.

When this occurs, the demand for additional processing power and storage capacity in the organisation will significantly increase too. An organisation should consider taking a "piecemeal approach" wherein they examine the data points individually then evaluate their values within the big data integration strategy.

This allows the organisation to scale the process gradually. This, in turn, may increase its success and achieve accurate calculations for needs. Delivering data is a complex process but it can be streamlined to ensure that it is available on a single platform.

Transformation and extraction are made possible through processing data sets and this ensures access to data. Access to information is becoming easier for the end-user but some processes are complex for developers, and this is why structuring and packaging information is a challenge.

4. Syncing and extraction of data

After importing data from different sources to a single platform, the next challenge would be to synchronise this data within the originating system. During the process, data originating from one source might go out of date by the time the next data comes in.

This also means that there might be variations in the commonality of concepts, metadata, data definitions, and so on. One of the most practical uses of big data integration involves the availability of data, the augmentation of existing data warehouse, and permitting access for others to discover/extract data.

The company needs to connect all of the big data integration platforms to ensure data transparency to consumers, thus limiting custom-coding requirements. With an increase in clients, there is a need to create simultaneous user accesses, and this might change in demand may change depending on the organization's process cycles.

Another challenge in terms of extraction in big data integration is ensuring that the data consumers have access to the most updated or recent data available.

5. Issues with security

Big data integration comes with a lot of security challenges, especially if the company doesn't understand big data integration completely. Security needs to be into consideration from the beginning to the end of the process.

Overlooking security can result in serious damage and comprise of data because big data technologies are constantly evolving. However, companies tend to ignore security features hoping that this aspect will get granted when they reach the application level.

When it comes to data, security is essential to ensure that information is safely stored and never compromised. Security is a priority in ensuring that big data integration is successful and yields positive results for an organization.

Easing the process of data integration

Being able to preempt associated challenges will ensure that you are well-equipped to deal with them. Organizations should invest in ensuring that employees understand their role in big data integration. Big data integration is also a costly exercise and should be treated as an investment and well-researched software should be used.

Conclusion

For companies planning to implement big data integration, they should consider all of these challenges and overcome these challenges. Big data integration is becoming necessary for most companies, and the earlier a company, the more successful they are likely to become in the future.

With comprehensive planning, knowledge, and expertise - a company can execute this process effectively. Challenges in big data integration have a host of solutions, and this is why an organization should anticipate them and mitigate the risks that are resolved. Big data integration allows organizations to compete at an advantage among businesses that are increasingly becoming driven by data.

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