- Growth of data
One of the biggest challenges for any big data initiative is the storage of data. This has been made worse by the exponential growth of data with time. With this, enterprises are now struggling to find ways of storing data that come from diverse sources and in different formats. The challenge is accommodating either structured or unstructured data in formats such as audio, video, or text. To make it worse, such formats, mainly unstructured, are hard to extract and analyze. These are the issues that impact the choice of infrastructure. Solving the challenge of data growth demands facilitation through software-defined storage, data compression, tiering, and duplication to reduce space consumption and minimize costs. This can be achieved through tools such as Big Data Analytics software, NoSQL databases, Spark, and Hadoop.
- Unavailability of data
One reason why big data analytics and big data projects fail is because of a lack of data. This can be caused by failure to integrate data or poor organization. New data sources must be integrated with the existing ones to ensure enough data from diverse sources is useful in analytics and decision-making.
- Data validation
As highlighted before, the increasing number of devices means more data from diverse sources. This makes it difficult for organizations to validate the source or data. Also, matching data from these sources and separating the accurate, usable, and secure data (data government) is a challenge that will linger for some time. It will require not only the hardware and software solutions but also teams and policies that will ensure this is achieved. Further, data management and governance solutions that will ensure accuracy will be needed, therefore increasing the cost of operations.
- Data security
Security continues to be one of the biggest challenges in big data initiatives, especially for organizations that store or process sensitive data. Such data is a target for hackers who want to access sensitive information and use it for malicious purposes. As big data initiatives increase, the number of hacking cases is expected to rise. The cases of theft of information are expected to rise. The loss of information can cost billions of dollars for a company due to lawsuits and compensation to the affected parties. The data security challenge will increase operational cost since cybersecurity professionals, real-time monitoring, and data security tools will be required to secure data and information systems.
Datasets are a great source of insights. However, they are of little or no value at all if they are not insightful in real-time. Big data should generate fast and actionable data that brings about efficiency in result-oriented tasks such as new product or service launch. It must offer information that will help create new avenues for innovation, speeding up service delivery, and reducing costs by eradicating service and operational bottlenecks. The biggest challenge going forward is generating timely reports and insights that will help satisfy customers who are becoming so demanding. This requires organizations to invest in more sophisticated analytics tools that will enable them to compete in the market.