Big Data Analytics involves the thorough examination of large volumes of intricate data sets, also known as Big Data. This data comes with the complexities of extreme volume, variety, and velocity. Data is considered 'big' when it becomes too large and complex for conventional data-processing methods to manage. Big Data Analytics simplifies this data and converts it into knowledge and insight to improve decision making and prediction accuracy.

• Mainstream Availability: Unlike custom-built systems developed solely for a particular business, Big Data Analytics solutions are widespread and readily accessible to a broad customer base.
• Sophisticated Features: Big Data Analytics comes with a wide array of functionalities and techniques such as text mining, machine learning, predictive analytics, data mining, artificial intelligence, and deep learning.
• Limited Customization: While some level of customization is possible, extensive technological proficiency is often needed for large-scale alterations in Big Data Analytics systems.
• Vendor Support: Vendors typically take responsibility for providing scheduled updates, maintaining the software's efficiency and offering customer support.
• Cost-Effectiveness: Big Data Analytics are often more affordable than developing a custom data solution from scratch, especially for large conglomerates that produce massive amounts of data daily.

Implementing Big Data Analytics demands a well-planned strategy. An organization needs to start with a comprehensive analysis of its needs and thorough research of the most suitable analytics solution. Subsequent cost-benefit evaluation, along with an assessment of vendors to ensure alignment between product features, support services, and company requirements, plays a crucial role in successfully operationalizing Big Data Analytics. Deployment should be monitored closely, and necessary adjustments should be made to achieve the desired outcomes.

• Cost-Effective: Big Data Analytics allows businesses to create cost efficiencies by cutting down on wastage and identifying the most efficient ways of doing business.
• Reduced Time: Big Data Analytics methods can run processes parallel on servers, reducing the time taken to extract business intelligence.
• Product Development: Big Data Analytics help develop new products by determining what customers want based on analysis of customer feedback across multiple communication channels.
• Customer Experience: Predictive analytics and machine learning algorithms help improve the shopping experience, customer satisfaction and create brand loyalty.
• Risk Analysis: Organizations can manage and mitigate risks better with data analytics techniques.

• Limited Customization: Big Data Analytics software is designed with a broad market in mind, and there may be limited customization to meet business-specific needs.
• Vendor Dependence: Relying solely on the vendor for updates and support can create vendor lockdown situations.
• Complex Integration: The complexity of integrating these systems with existing software can present significant challenges.
• Hidden Costs: Licensing, customization, integration, along with staff training and maintenance costs could potentially surpass the initial logic of achieving cost efficiency.
• Security Threats: Big data storage may expose businesses to cyber threats and data breaches, which would require taking substantial security measures.
• Risk of Obsolescence: Big Data Analytics software, just like any other technology, faces the risk of becoming obsolete due to rapid technological advancement.

In conclusion, while Big Data Analytics has its challenges, there's no denying the immense benefits it offers, including improved customer experience, increased operational efficiency, and a sharper competitive edge in the market. With comprehensive exploration, proper planning, and strategic implementation, businesses can unleash the potential of Big Data Analytics to spur growth and innovation.