Big Data Challenges for Large Companies

Big Data helps companies improve their risk management models and create innovative business strategies. It supports companies to update their existing products and services while creating new ones.

A company can achieve a perfect product-market fit by collecting massive amounts of data and analyze it using analytics tools to generate useful information. However, research shows that over 85% of large companies use Big Data.

Among them, 37% of organizations achieve data-driven insights and valuable information. The rest face many challenges, which affect their business operations and lead to a lower return on investments (ROIs). In today’s article, we will talk about the Big Data challenges for large businesses. Read on!

Data Growth Issues

Companies collect hefty amounts of data every day, making it difficult for them to store it properly. It is challenging for businesses to manage the exponential growth of data. Most often, the data collected by a company comes in unstructured or raw form.

It usually comes from sources like documents, photos, text files, audios, and videos. They often find it challenging to store large datasets in databases due to a lack of skilled workers. For instance, your company receives thousands of Facebook comments every day about your products, services, or brand.

Although this data can provide insightful information, you may find it difficult to structure it. Many platforms offer software and tools to help with this challenge; it is still not easy to process raw data.

Ineffective Data Integration

Like data growth issues, data integration is also a big challenge for large companies. Large organizations use software and tools to collect data from various sources, such as website logs, call-centers, and social media sites. It is difficult for a company to integrate all this data because it comes in different formats.

Keep in mind that Big Data does not guarantee 100% accuracy and quality. That’s why it is essential to ensure you are collecting reliable data. Sometimes, a company collects duplicate or wrong data, which has nothing to do with its products and services.

Thus, this type of data does not provide useful insights to streamline business operations. Although you can enhance this by cleansing the raw data, it isn’t easy to create a proper Big Data model.

Expensive Data Management

Big Data management is very expensive for many organizations, especially if they are using on-premise solutions. Investing in new hardware and equipment is costly for most companies.

Likewise, data management requires a company to hire new software developers, administrators, and analysts. Although you can find open-source and free frameworks, you will pay for the setup, configuration, and maintenance of hardware.

Security Problems

Big data is often associated with security holes in the network. Most organizations do not pay attention to the security factor while launching their projects. Remember, this can lead to loss, theft, and inappropriate use of data.

It is essential to maintain your customers’ privacy. The lack of security features can cause loopholes in your business operations, leading to decreased customer trust and lowered ROIs.

Final Words

Mismanagement of Big Data can lead to severe consequences for large companies. If you face any of the above challenges, you need expert help to create new legal frameworks, transparency, and control over the data. Clovertex can help create and manage Big Data solutions for companies in various domains, including analytics, AI, MI, Data Lakes, etc. Contact us today!

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