The integration and analysis of large data volumes can provide enterprises with invaluable insights, but the complexity of big data analytics poses significant challenges. Here are seven key obstacles that business enterprises frequently encounter:

1. Data Quality and Cleansing: Ensuring the accuracy and completeness of data is paramount for any big data initiative. Poor data quality can lead to erroneous analytics, making it a challenge to cleanse, standardize, and validate the information before analysis.

2. Data Integration: Companies often have data spread across various sources and systems, which makes integrating it into a central analytics platform a complex task. The difficulty lies in harmonizing different formats, structures, and semantics without losing critical details.

3. Processing Large Volumes of Data: As the amount of data continues to grow exponentially, businesses struggle to process and analyze these vast datasets in a timely manner. The challenge is further exacerbated when dealing with streaming or real-time data that require immediate processing.

4. Lack of Skilled Personnel: Big data analytics requires a specific set of skills that are currently rare in the job market. Finding personnel who are proficient in data science, statistical analysis, and machine learning can be both difficult and expensive.

5. Security and Privacy Concerns: With big data comes great responsibility to protect sensitive information. Companies face challenges in establishing stringent security measures to prevent breaches while also navigating complex privacy regulations and ensuring compliance.

6. Costly Infrastructure: The infrastructure needed for storing and analyzing big data can be prohibitively expensive. This includes not only physical hardware but also software tools and platforms designed for big data processing.

7. Understanding Big Data Analytics Tools: A wide array of tools is available for big data analytics, each with its strengths and use cases. Businesses often struggle to understand which tools fit their specific needs best and how to integrate them effectively within their existing technology stack.

To overcome these challenges, enterprises must adopt strategic approaches that involve investing in quality data governance practices, continuous staff training, prioritizing security measures, scaling resources efficiently, and staying agile to adapt to new technologies and methodologies as they emerge in the big data space.