Top 5 Challenges in Big Data & Analytics

Challenges in Big Data & Analytics

As large volumes of raw and complex data, Big data enables programmers to take better decisions and optimize business processes by understanding customer behavior, latest trends, and changing patterns. An enterprise can collect, store, and analyze these large datasets in a number of ways. An enterprise can even use robust big data tools to store, access, and manage the structured and unstructured data collected from various sources in a faster and more efficient way. But no business can utilize and leverage big data optimally without addressing a number of challenges.

Understanding 5 Major Challenges in Big Data Analytics and Integration

    1) Picking the Right NoSQL Tools

The enterprises cannot manage large volumes of structured and unstructured data efficiently using conventional relational database management systems (RDBMS). They have to switch from relational databases to NoSQL or non-relational databases to store, access, and process large datasets rapidly and efficiently. The enterprises have the option to choose from a wide range of NoSQL databases. Likewise, they can also choose from several NoSQL tools. The NoSQL databases and tools differ from each other in several aspects. Also, each non-relational database or NoSQL tool has its own shortcomings. Hence, businesses find it challenging to pick the right non-relational database and invest in the best data management tool.

    2) Scaling up and down Big Data according to Current Demand

Unlike conventional software development projects, big data projects grow and evolve consistently. But enterprises often ignore the fact that the big data volume and workload grows rapidly. The enterprises must create an infrastructure that facilitates addition and processing on fresh datasets regularly. They also need to ensure that adequate resource is available to meet the constant increase is big data volume and workload. Many enterprises opt for cloud platforms to store and manage big data efficiently by availing computing resources on demand. But some of the big data solutions will not perform optimally in the cloud. The enterprises must address this challenge to scale up and down the big data according to current demand.

    3) Overcoming Big Data Talent and Resource Constraints

Each enterprise needs skilled professionals to manage and analyze huge volumes of real-time data being collected from various sources and in multiple formats. The large enterprises deploy skilled data scientists and big data analysts to utilize and leverage big data optimally. But data scientist and big data analysts are currently in short supply but high demand. Often small enterprises and start-ups underutilize big data due to lack of resources and skills. Also, large enterprises find it difficult to manage large datasets efficiently by deploying additional data scientists. It will take some time for enterprises to address the major hurdle in big data analysis and management.

    4) Collecting and Integrating Massive and Diverse Datasets

The big data tools enable businesses to collect real-time data from both external and internal sources. They can further collect large volumes of structured and unstructured data from each source. The data collected from various sources will differ in formats and quantity. The businesses have to set up scalable data warehouses to store the incoming data in a reliable and secure way. At the same time, they need to invest in robust big data solutions to integrate the structured and unstructured data by eliminating disparities and inconsistencies. But the businesses have to resolve new errors, eliminate data inconsistencies, and prevent data loss consistently to utilize big data properly.

    5) Maintaining Data Integrity, Security, and Privacy

An enterprise can use big data tools to collect structured and unstructured data from varied sources. The tools even enable businesses to store, process and analyze efficiently. But the Facebook-Cambridge Analytica data scandal makes it essential for businesses to focus on keeping the entire big data lifecycle secured to retain customers and avoid legal hassle. The businesses must implement a robust security strategy to collect, store, analyze, manage, and utilize large volumes of real-time data by eliminating risks of data breaches and exposure. They also need to keep the big data security strategy flexible and dynamic enough to address the new security and privacy issues being generated due to the constant increase in data volume.

A number of studies suggest that many enterprises underutilize big data not addressing these challenges early and effectively. It is always important for the businesses to implement a comprehensive strategy to manage and leverage big data by addressing these common big data challenges. However, big data management is a continuous process. The enterprises have to address new challenges and issues proactively to leverage big data fully.