Big Data Integration and Challenges Involved

Big data integration can be complex since it involves data from varied sources.

In the current scenario, businesses are running on data and analytics. The pandemic-induced rapid digital transformation has highlighted the importance of big data for the growth of a business. Big data analytics is a core business solution today that can drive growth, agility, competitive edge, and provide a 360-degree view of the company. Companies leverage big data analytics to gain actionable insights and enhance customer experience.

What is Big Data Integration?

Big data integration, as the term suggests, is the process of extracting data from different sources, combining them, and processing to get valuable insights. This integration process is not simple as it sounds as it involves huge data sets that are structured, unstructured, and semi-structured. These data sets also need to be stored in data warehouses so that they can be retrieved later. The conventional data integration process relied on extracting, transforming, and loading the clean data into warehouses. However, this cannot be used in the case of big data as it originates from heterogeneous sources. The four important characteristics of big data are volume, velocity, variety, and veracity. These characters make it challenging to integrate big data into business processes.

What Are the Challenges Involved?

1. Variety of Data Formats and Sources

Since big data is gathered from different sources, it might exhibit heterogeneous formats and structures. Sorting them from this complex stage can be difficult. Data sets are extracted from different applications and platforms like marketing apps, CRM, customer service teams, and others.

2. Connecting Data Platforms and Increasing Accessibility

Business intelligence tools that are used to identify and collate data should be able to connect different big data platforms. The growing number of data consumers can become a challenge in big data integration. The company will need to respond to the growing demand and enable data accessibility in real-time to the users, which becomes difficult.

3. Speed of Data Processing

The current business scenario demands real-time data insights and it can pose a challenge to big data integration. Big data is extracted from heterogeneous platforms and thus needs time to process and get insights. Working with complex data structures makes it impossible to analyze them at the same time.

4. Picking the Right Data Management Framework

There are different data management frameworks that are commonly referred to as a category called NoSQL. Different NoSQL approaches employ different paradigms including the key-value store concept that enables them to connect with the entities in the data sets. There are various NoSQL approaches that are said to be evolving and carry scalability and performance. Thus, the existence of such a large variety of tools makes data management systems uncertain. Choosing from these data management landscapes according to the specific needs of the business can become a challenge.

5. Syncing Data Obtained From Different Sources

After extracting data from different sources, it needs to be synchronized with the originating system. Since they are from a wide range of sources, by the time a dataset gets integrated, another might become out of the syncing schedule and thus be termed as old. This will lead to variations in the commonality concepts like data definitions. Thus, while integrating big data, variations in data management, extraction, and transformation will create issues in syncing data sources.

6. Security Challenges

Since big data holds high significance to a company and its users, ensuring security in big data integration should not be ignored. Big data integration will have many security challenges as the data sources are not always well-known and can also create breaches. Integrating big data and storing them safely should be a priority.

7. The Demand for Skilled Analysts

With the accelerated adoption of big data and analytics, there has been a surging demand for talents in this field. The lack of analysts and data engineers can become a potential challenge to the process of big data integration.

Companies planning to deploy big data integration should be aware of these major challenges among others. With the right data cleansing and processing along with emerging innovations like data virtualization, big data integration can become less complex and more efficient.