Manage access in diverse big data and business intelligence setups involving multiple vendors promo 2024 complete oral homework + video mp4

In today's digital age, businesses are constantly generating vast amounts of data, from customer transactions to market insights. This data, commonly referred to as "big data," holds immense potential to drive innovation, optimize operations, and enhance decision-making processes. However, along with this potential comes the critical responsibility of safeguarding sensitive information and protecting business assets from various security threats.

Big data refers to extremely large and complex data sets that cannot be effectively processed using traditional data processing applications. These data sets typically contain massive volumes of both structured and unstructured data that are generated at high velocity and come from a variety of sources, including sensors, social media platforms, transactional systems, and more.

Business Intelligence (BI) refers to the process of collecting, analyzing, and presenting data to support decision-making within organizations. BI encompasses a range of tools, technologies, and methodologies aimed at transforming raw data into meaningful insights that can inform strategic, tactical, and operational decisions.

ig data presents both opportunities and challenges in terms of security. Here are some key considerations regarding big data and security:

Data Privacy: With the vast amounts of data collected and stored in big data systems, ensuring data privacy is paramount.
Data Governance: Establishing clear data governance policies and procedures helps ensure that data is accurate, consistent, and secure throughout its lifecycle.
Cybersecurity Threats: Big data systems are susceptible to cybersecurity threats such as data breaches, malware attacks, and insider threats.
Access Controls: Implementing robust access controls is essential to prevent unauthorized users from accessing sensitive data in big data environments
Data Loss Prevention (DLP): Big data systems should incorporate data loss prevention mechanisms to monitor, detect, and prevent unauthorized data exfiltration or leakage

Primary Drivers for Big Data & BI Security

Ensuring security in Big Data and Business Intelligence (BI) environments demands a comprehensive and holistic approach due to the complexity and sensitivity of the data involved. Here are some primary drivers for adopting such an approach:

Data Sensitivity: Big Data and BI systems often handle sensitive information, including personal identifiable information (PII), financial data, intellectual property, and proprietary business information. Protecting this data from unauthorized access, theft, or misuse is crucial to maintaining trust with customers, partners, and stakeholders.
Regulatory Compliance: Regulatory requirements such as GDPR, CCPA, HIPAA, and others mandate strict guidelines for the handling and protection of data. Failure to comply with these regulations can result in severe penalties, legal repercussions, and damage to reputation. A

Demand a ‘holistic’ security approach

In our research for Big Data Security, we focus on products that follow a holistic approach towards protecting Big Data and BI platforms instead of the disconnected point security solutions. Although many generic security tools like firewalls or anti- malware may play an important role in securing parts of Big Data frameworks, we do not cover them as part of Big Data security to avoid the confusion of functionally distinct security products that are generally covered in other KuppingerCole’s reports under separate market segments.

More precisely, we are not covering security solutions for protecting relational database management systems (RDBMS), since they are being reviewed in a separate Leadership Compass for Database Security.

To provide a more comprehensive overview, here are some additional aspects and links to security considerations for Big Data and BI environments:

Encryption: Implementing encryption techniques such as SSL/TLS for data in transit and encryption-at-rest mechanisms for data storage adds an extra layer of security to protect sensitive information from unauthorized access.